# i-Dent: A virtual assistant to diagnose rare genetic dental diseases

Hocine Kadi, Marzena Kawczynski, Sara Bendjama, Jesus Zegarra Flores, Audrey Leong-Hoi, Hugues de Lastic, Julien Balbierer, Claire Mabileau, Jean Pierre Radoux, Bruno Grollemund, et al.

<figure><img src="image_1.png" /></figure>

## To cite this version:

Hocine Kadi, Marzena Kawczynski, Sara Bendjama, Jesus Zegarra Flores, Audrey Leong-Hoi, et al. i-Dent: A virtual assistant to diagnose rare genetic dental diseases. Computers in Biology and Medicine, 2024, 180, pp.108927. 10.1016/j.compbiomed.2024.108927. hal-04943682

HAL Id: hal-04943682

https://hal.science/hal-04943682v1

Submitted on 9 Apr 2025

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

<figure><img src="image_2.png" /></figure>
<figure><img src="image_3.png" /></figure>

# i-Dent: A virtual assistant to diagnose rare genetic dental diseases

Hocine Kadi<sup>a,1</sup>, Marzena Kawczynski<sup>b,1</sup>, Sara Bendjama<sup>b</sup>, Jesus Zegarra Flores<sup>a</sup>,
Audrey Leong-Hoi<sup>a</sup>, Hugues de Lastic<sup>a</sup>, Julien Balbierer<sup>a</sup>, Claire Mabileau<sup>a</sup>,
Jean Pierre Radoux<sup>a</sup>, Bruno Grollemund<sup>b</sup>, Jean Jaegle<sup>c</sup>, Christophe Guebert<sup>c</sup>, Bertrand Bisch<sup>c</sup>,
Agnès Bloch-Zupan<sup>b,d,e,f,*</sup>

## ARTICLE INFO

### Keywords:

- Deep learning
- Dental age assessment
- Mask R-CNN
- Medical diagnosis
- Panoramic radiograph processing
- Rare genetic diseases
- Teeth segmentation
- U-Net

## ABSTRACT

Rare genetic diseases are difficult to diagnose and this translates in patient's diagnostic odyssey! This is particularly true for more than 900 rare diseases including orodental developmental anomalies such as missing teeth. However, if left untreated, their symptoms can become significant and disabling for the patient. Early detection and rapid management are therefore essential in this context. The i-Dent project aims to supply a pre-diagnostic tool to detect rare diseases with tooth agenesis of varying severity and pattern. To identify missing teeth, image segmentation models (Mask R-CNN, U-Net) have been trained for the automatic detection of teeth on patients' panoramic dental X-rays. Teeth segmentation enables the identification of teeth which are present or missing within the mouth. Furthermore, a dental age assessment is conducted to verify whether the absence of teeth is an anomaly or a characteristic of the patient's age. Due to the small size of our dataset, we developed a new dental age assessment technique based on the tooth eruption rate. Information about missing teeth is then used by a final algorithm based on the agenesis probabilities to propose a pre-diagnosis of a rare disease. The results obtained in detecting three types of genes (PAX9, WNT10A and EDA) by our system are very promising, providing a pre-diagnosis with an average accuracy of 72 %.

## 1. Introduction

DENTAL agenesis is a dental developmental anomaly characterized by a failure of teeth development and subsequently missing teeth. Agenesis occurs mainly in the permanent dentition but can be present in

the primary dentition. Hypodontia refers to the absence of less than 6 teeth. Oligodontia refers to the absence of 6 or more teeth and is a rare disease (ORPHA:99798). Finally, anodontia describes the absence of all teeth [1]. The prevalence of congenitally missing teeth in the primary dentition is very low 0.3 %-0.9 % in the European population. The

<footnote><sup>a</sup> Capgemini Engineering, Medica Division, 950 Bd Sébastien Brant, 67400, Illkirch-Graffenstaden, France</footnote>

<footnote><sup>b</sup> Reference Center for Rare Oral and Dental Diseases, Hôpitaux Universitaires de Strasbourg (HUS), Pôle de Médecine et Chirurgie Bucco-dentaires, Centre de Référence des Maladies Rares Orales et Dentaires, CRMR O-Rares, Filière Santé Maladies Rares TETE COU, European Reference Network ERN CRANIO, 1 Place de l'Hôpital, 67000, Strasbourg, France</footnote>

<footnote><sup>c</sup> e-media, Bâtiment Gauss - Parc d'innovation, 950 Boulevard Sébastien Brant, 67400, Illkirch-Graffenstaden, France</footnote>

<footnote><sup>d</sup> Université de Strasbourg, Faculté de Chirurgie Dentaire, 8 Rue St Elisabeth, 67000, Strasbourg, France</footnote>

<footnote><sup>e</sup> Université de Strasbourg, Institut d'études Avancées (USIAS), Strasbourg, France</footnote>

<footnote><sup>f</sup> Université de Strasbourg, Institut de Génétique et de Biologie Moléculaire et Cellulaire (IGBMC), INSERM, U1258, CNRS - UMR7104, BP 10142, 1 Rue Laurent Fries, 67404, Illkirch-Graffenstaden, France</footnote>

<footnote>* Corresponding author. Reference Center for Rare Oral and Dental Diseases, Hôpitaux Universitaires de Strasbourg (HUS), Pôle de Médecine et Chirurgie Bucco-dentaires, Centre de Référence des Maladies Rares Orales et Dentaires, CRMR O-Rares, Filière Santé Maladies Rares TETE COU, European Reference Network ERN CRANIO, 1 Place de l'Hôpital, 67000, Strasbourg, France.</footnote>

<footnote>E-mail addresses: hocine.a.kadi@capgemini.com (H. Kadi), marzena.kawczynski@chru-strasbourg.fr (M. Kawczynski), sara.bendjama@gmail.com (S. Bendjama), jesus.zegarraflores@capgemini.com (J.Z. Flores), audrey.leong-hoi@capgemini.com (A. Leong-Hoi), hugues.delasticsaintjal@capgemini.com (H. de Lastic), julien.balbieber@capgemini.com (J. Balbierer), claire.mabileau@capgemini.com (C. Mabileau), jeanpierre.radoux@capgemini.com (J.P. Radoux), bruno.grollemund@gmail.com (B. Grollemund), jean@e-media.fr (J. Jaegle), c.guebert@e-media.fr (C. Guebert), b.bisch@e-media.fr, b.bisch@jlb-groupe.com (B. Bisch), agnes.bloch-zupan@unistra.fr (A. Bloch-Zupan).</footnote>

<footnote n="1">¹ The first and second author made an equal contribution for this paper.</footnote>
prevalence in the permanent dentition for hypodontia ranges from 3.4 to 8 % excluding third molars. The reported frequency of agenesis of the third molar(s) is higher, varying from 9 % to more than 30 %. Oligodontia has a prevalence of 0.08 %-0.16 % [2,3]. Dental agenesis may exist in isolation or associated to other symptoms in syndromes or rare diseases [4]. The diagnosis is based on routine clinical and radiographic examinations.

In fact, at the age of three, all primary teeth are usually present, so their absence, which is relatively rare, is usually noticed and leads to a dentist consultation. Similarly, all permanent teeth are usually present by the age of 12–14 years (except for third molars). When teeth have not appeared, at the appropriate age, in the oral cavity, panoramic dental X-rays are usually performed. When making the radiographic diagnosis, it is important to consider not only the number of missing teeth, but also their type. The most common permanent missing teeth are premolars (30–47 %), maxillary lateral incisors (17,5–27 %) and third molars [2, 3]. Some permanent teeth are rarely absent: first molars, canines, central upper incisors. If they are discovered as missing, this should alert as a possible sign leading to the diagnosis of a rare disease [2,3].

Missing teeth very often follow a pattern corresponding to different genes expression within the jaws [5,6]. Moreover, in case of agenesis, abnormalities of hair, nails and sweat glands among others may also be present and thus constitute an additional indicator [7,8]

The clinical diagnosis of oligodontia or anodontia can be ascertained through a molecular confirmation via a genetic analysis. More than 50 genes are known to be responsible for tooth agenesis. Among the most recurrent are: PAX9, MSX1, WNT10A, AXIN2, LTBP3, PITX2 and EDA. WNT10A is known to be the most frequently involved gene [4,9].

This genetic analysis is fundamental because it drives the diagnosis towards rare diseases with a wider implication in terms of diagnosis and overall healthcare management.

Patients suffering from significant agenesis are confronted with problems of mastication, phonetics and aesthetics which can induce psychological problems such as low self-esteem [10]. Early diagnosis and management are essential to mitigate these problems and reduce the psychosocial impact of the condition.

Therapeutic management involves a multidisciplinary team including the pediatric dentist, orthodontist, prosthodontist, oral surgeon, implant specialist, oral biologist, and maxillofacial surgeon [11]. The treatment of hypo/oligodontia or anodontia is a multistep long-life process from childhood to adulthood and may vary between removable dentures to complete full fixed mouth prosthetic rehabilitation built on implants. Treatment aims at promoting growth and mastication as well as providing a normalized smile. In cases of oligodontia or depending on the patient's needs, treatment can be started early, and dentures can be offered as early as 3–4 years of age. These removable dentures can be renewed as the child grows. Early diagnosis is therefore essential for the well-being of the patient. Our objective through this study is to provide an automatic pre-diagnosis tool allowing a quick and early detection of these dental disorders.

The rise of artificial intelligence has allowed great progress in the field of medicine, especially in diagnosis. The incredible performance of these algorithms in processing large amounts of data such as medical images has allowed the development of new diagnostic methods. Segmentation and classification algorithms are increasingly used to detect diseases, tumors, and other conditions [12–14]. Their abilities surpass that of humans in terms of time, accuracy, and reproducibility. However, verification by an expert remains essential, these new tools are not intended to replace doctors but to help them and lead their diagnosis towards areas that they would not have been able to detect with their own eyes. The i-Dent project is part of this vision. It offers a virtual assistant to help diagnose rare oral diseases. The project aims to conduct medical pre-diagnosis of children with rare diseases encompassing in their clinical synopsis missing teeth. The objective is to detect which teeth are missing and which are present on dental panoramic radiographs, and then to compare this with theoretical dental models of the

disease. Incorporating dental age findings strengthens the recognition of age-related dental formula (present teeth) and contributes to the detection of missing teeth.

The i-Dent project is structured as a pipeline that processes panoramic dental images to generate a preliminary diagnosis of rare oral-dental diseases associated with three specific genes (PAX9, WNT10A, and EDA). This automated preliminary diagnosis leverages data concerning absent teeth and estimates of dental age. We developed an algorithm that uses the probabilities of dental agenesis to suggest a potential rare disease diagnosis based on information about missing teeth. Missing teeth are identified by performing teeth segmentation in the panoramic dental images using a deep learning model, which also evaluates the eruption of teeth to estimate dental age. Several research questions have been formulated to carry out this work.

**RQ1:** To what extent can deep learning-based tooth segmentation and numbering algorithms be effective in panoramic dental images of children with dental agenesis?

Many previous works have focused on teeth segmentation and numbering, but few have addressed the segmentation of children's teeth, where there are 52 different classes of teeth instead of 32 in the case of adult dentition. To our knowledge, no previous work has been developed on dental panoramic images in the context of dental rare diseases, which are affected by dental agenesis.

**RQ2:** Can we build an efficient deep learning-based algorithm to assess dental age using dental eruption? The use of tooth eruption to estimate dental age was dictated by the small size of the dataset, which did not allow us to use classification or regression techniques generally used in the literature.

**RQ3:** Is it possible to automatically diagnose several dental rare diseases using only missing teeth?

We employed several classification machine learning algorithms and developed our own algorithm that compares patterns of several diseases based on agenesis probabilities. The latter was chosen for implementation in the pipeline because it offers detailed explanations for diagnosis.

The structure of the article is organized into six main sections: "Pipeline," "Dataset," "Methods," "Results," "Discussion," and "Conclusion." The "Pipeline" section outlines the general methodology adopted, describing the various stages of data processing and modeling. The "Dataset" section explains the dataset used, highlighting its key characteristics as well as precautions taken for its acquisition and preparation. The "Methods" section provides a comprehensive overview of the algorithmic approaches employed at different stages of the pipeline. The "Results" and "Discussion" sections offer an analysis of the outcomes derived from our methodology, exploring their implications and situating them within the broader research context. Finally, the "Conclusion" summarizes the key findings of our study and underscores its contributions to the current understanding.

## 2. Pipeline

Fig. 1 shows the general pipeline of our system. First, the images are pre-processed; they are cropped to the edges of the oral cavity and resized (A). This step helps the automatic detection and numbering of teeth performed by an instance segmentation deep learning model (B). Once the present teeth are detected, the patient's age is assessed via the dental age estimation algorithm (C). Indeed, for the objective of this study, it is important to know if the absence of some teeth is due to the young dental age of the patient or to a genetic condition. Finally, using agenesis probabilities, the combination of missing teeth is associated to most probable rare disease (D) for pre-diagnosis.
<figure><img src="image_4.png" /><figcaption>Fig. 1. General pipeline of our virtual assistant.</figcaption></figure>

## 3. Dataset

The data used in our study were provided by the Reference Center for Rare Oral and Dental Diseases, CRMR O-Rares, Hôpitaux Universitaires de Strasbourg (HUS), France and performed in collaboration with Université de Strasbourg and the Institute of Genetics and Molecular and Cellular Biology (IGBMC/CERBM). We gathered 272 dental panoramic X-rays of individuals aged 3–64 years, including 142 radiographs for children under 17 years. Dental panoramic X-rays from a cohort of 123 control patients were also collected. Patients and legal representatives consented to the use of their anonymized clinical data in research (NCT02397824). Each radiograph was annotated and numbered by experts to accurately segment and classify each apparent tooth. For hypo/oligodontia individuals, we were able to identify different genetic diseases thanks to the Next Generation Sequencing GenoDENT diagnostic panel [4,16], and we identified several pathogenic variants in the following genes: *EDA*, *EDA1*, *EDAR*, *EVC2*, *IKBKG*, *LRP6*, *MSX1*, *NEC-TIN4*, *PAX3*, *PAX9*, *PITX2*, *SMOC2*, *TSPEAR*, *CTNNB1*, *WNT10A* and *WNT10B*. Numerous genes are indeed involved in hypodontia/oligodontia [4,9]. Variants in these genes are discovered after genetic testing using the GenoDENT NGS panel [15,16]. We however selected in our cohort the genes that were most frequently involved by decreasing order (*WNT10A*, *EDA*, *PAX9*) to have a sufficient number of cases to develop a discriminating algorithm. *WNT10A* and *EDA* are the most common genes associated with tooth agenesis [17–19]. The missing tooth pattern associated with *PAX9* mutation is also unique and easily recognizable with missing molars [20]. The distribution in the dataset is imbalanced: there are 64 X-rays of individuals with *WNT10A*, 16 with *EDA* and 13 with *PAX9* mutations, we have less than 5 radiographs for each other genes. Because the dataset available for hypo/-oligodontia related to other genes was smaller we focused our study on these three genes related diseases, as shown in Table I.

In order to identify each rare genetic disease, the CRMR O-Rares created a file presenting the gene and the different associated symptoms, and including a dentition model in a spreadsheet form that associates a probability of agenesis to each permanent tooth. The record form corresponds to the information collected within the Diagnosing Dental Defect Database D4/phenodent [21]. These agenesis probabilities were calculated using the literature and our own cohort data. They

<table><caption>**Table 1**
Distribution of our image dataset between control subjects and Dental agenesis subjects.</caption><thead><tr><th colspan="3">Numbers of X-rays</th></tr></thead><tbody><tr><td>Control subjects</td><td></td><td>123</td></tr><tr><td rowspan="3">Dental agenesis subjects</td><td><i>PAX9</i></td><td>13</td><td>93</td></tr><tr><td><i>EDA</i></td><td>16</td><td></td></tr><tr><td><i>WNT10A</i></td><td>64</td><td></td></tr><tr><td><strong>Total</strong></td><td></td><td><strong>216</strong></td></tr></tbody></table>

correspond to the probability that a patient has agenesis of a given tooth in the context of the genetic defect [22].

## 4. Methods

The pipeline comprises several key components: a pre-processing step for dental panoramic images, a teeth segmentation model, a dental age estimation algorithm, and a final component for making a pre-diagnosis based on missing teeth. In this section, we will elaborate on the selection of each algorithm and describe the experiments conducted at every stage of the pipeline.

### 4.1. Preprocessing

To minimize detection errors, we initially preprocessed the images. In dental panoramic images, we met challenges involving the spine and nasal slits, which share similar shapes and intensities with teeth, potentially leading to false detections. As demonstrated by Ref. [23], using a segmentation algorithm to specifically detect the oral cavity helps reduce segmentation errors by isolating the area of interest to just the teeth. The authors used U-Net [24] to detect the tooth region and then Faster R-CNN to precisely detect the teeth, where the tooth detection module exhibited a recall of 0.992 and a precision of 0.994. Conversely, when they used Faster R-CNN without U-Net-based cropping, the tooth detection method yielded lower performance, achieving a recall of 0.9566 and a precision of 0.9418.

In this work, we opted for a similar approach, where we detect the oral cavity and subsequently crop the image by centering on the detected region. We used U-Net<fnref n="2" /> for oral cavity detection due to its demonstrated effectiveness in identifying the region of interest, as illustrated in Ref. [23]. Moreover, numerous studies have highlighted the efficacy of U-Net and its variations in accurately performing teeth binary segmentation [25,26].

For practical implementation, we randomly selected 116 dental panoramic images from the control cohort and annotated the oral cavity, as shown in Fig. 2. Unlike the approach described in Ref. [23], where U-Net was applied to labels generated programmatically as binary mask images from teeth bounding boxes, we opted for additional labeling to detect the entire oral cavity rather than just the teeth. This allowed us to create a single and more homogeneous region of interest. We split the selected dataset into 80 images for training, 20 for validation, and 16 for testing. U-Net was trained with the following parameters: binary cross-entropy loss function, learning rate of $1e^{-4}$, and 20 epochs.

We also evaluated different cropping methods. We first used an exclusively lateral cropping. We then cropped the upper part by 33 % with respect to the segmented oral cavity (crop 33 %). Finally, we evaluated by removing only half of the area outside the oral cavity detected with U-Net (crop 50 %).

<figure><img src="image_5.png" /><figcaption>**Fig. 2.** Example of a panoramic dental image on the left of the figure and corresponding labeling of the oral cavity on the right.</figcaption></figure>

<footnote n="2">² We used this implementation: https://github.com/zhixuhao/unet.</footnote>
## 4.2. Teeth detection and numbering with mask R-CNN

Teeth detection and segmentation in dental panoramic images have achieved significant advancements through the integration of deep learning techniques.

Jader et al. [27] employed Mask-R-CNN for teeth detection. Zhang et al. [28] merged Faster R-CNN and R-FCN for tooth detection and classification. Silva et al. [29] conducted a comparative study between Mask R-CNN and PANet for teeth segmentation and numbering using the UFBA-UESC Dental Images dataset. Koch et al. [25] explored U-Net and FCN for dental panoramic radiograph segmentation and used techniques such as network ensembling and data symmetry exploitation to improve performance. Chandrashekar et al. combined Mask R-CNN and Faster R-CNN for tooth numbering. Lee et al. [30] utilized data augmentation and Mask R-CNN for automated tooth segmentation using individual annotation of panoramic radiographs. Zhu et al. [31] combined R-CNN, SSD, and heuristics for tooth numbering. Zhao et al. [32] introduced TSAS-Net for addressing fuzzy boundary challenges. Almalki et al. [33] employed self-supervised learning for teeth detection and restoration. Hou et al. [34] proposed Teeth U-Net with advanced attention blocks. Kanwal et al. [35] introduced a panoramic-segmentation based strategy that integrates instant segmentation with background semantic segmentation, employing a dual path transformer-based network combined with a panoptic quality (PQ) loss function. Swin-Unet, a U-shaped transformer-based architecture, was used in Ref. [26] to segment panoramic radiographs.

While a variety of techniques have been utilized in the cited studies, they predominantly focus on permanent teeth. Conversely, there is limited scientific literature addressing deciduous or mixed dentition with only a few articles tackling this subject, all employing CNN-based models. Kilic et al. [36] employed Faster R-CNN Inception v2 models to automate the detection and numbering of deciduous teeth on pediatric panoramic radiographs, achieving high sensitivity and precision rates. Similarly, Pinheiro et al. [37] utilized Mask R-CNN to detect and number both permanent and deciduous teeth, albeit with challenges in mislabeling instances. Rubiu et al. [38] reported suboptimal segmentation performance, particularly for certain deciduous teeth, due to the absence of some classes in the test set. Additionally, Kaya et al. [39] employed YOLO V4, a CNN-based object detection model, for automated tooth detection and numbering in mixed dentition, achieving successful results. Burman et al. [40] introduced a collaborative learning model leveraging Mask R-CNN to identify primary and permanent teeth and detect fillings on panoramic radiographs. Their classifiers achieved high mean average precision (mAP) for tooth segmentation and filling detection, thereby enhancing radiograph interpretation accuracy.

To the best of our knowledge, no study has specifically addressed tooth segmentation in cases of dental agenesis in mixed dentition. Given the promising results of Mask R-CNN in handling mixed dentitions, we opted to employ this model for automatic detection and recognition of teeth in panoramic radiographs within our pipeline.

In our research, we aimed to number teeth according to ISO 3950:2016 Dentistry — Designation system for teeth and areas of the oral cavity, as defined by the World Dental Federation (FDI). To understand the impact of dental agenesis on tooth segmentation and numbering, we conducted three distinct training sessions, as detailed in Table II: first using images of control subjects annotated by our experts, and then with a dataset including both control and affected subjects. The initial training aimed to evaluate the model performance on images of

<table><caption>**Table 2**
Training data of the different Mask RCNN training performed.</caption><thead><tr><th>Training</th><th>N°1</th><th>N°2</th><th>N°3</th></tr></thead><tbody><tr><td>Control subjects</td><td>65</td><td>65</td><td>65</td></tr><tr><td>Dental agenesis subjects</td><td>0</td><td>40</td><td>74</td></tr><tr><td>Total</td><td>65</td><td>74</td><td>139</td></tr></tbody></table>

control subjects unaffected by dental agenesis. Training sessions 2 and 3 were performed using a mixed dataset comprising control images and images of people with agenesis. Training 2 comprised 65 control images and 40 images of individuals with agenesis, while Training 3 included the same control images and 75 images with agenesis.

We evaluated the best models on several test datasets composed of 30 dental radiographs of control subjects, and 31 radiographs of dental agenesis subjects.

We used the Matterport Mask RCNN<fnref n="3" /> implementation which is coded in Python 3, Tensorflow 1.8, and Keras. The training method consists of three phases. Initially, for the first 10 epochs, only the head of the network is trained. Subsequently, for 40 epochs, the first 4 layers of the network are trained. Finally, for 60 epochs, the entire network is trained. Transfer learning is employed with pretraining on the COCO dataset. Throughout each step, the learning rate is set to 1e-3, and the batch size is 2. We used a DGX server to train the models. This server consists of 40 CPUs, 252 GB of RAM, and four NVIDIA Tesla V100 DGXs 32 GB graphics cards, each with 32 GB of VRAM. The server runs on a Linux version 4.15.0-50-generic. Only one of the four GPUs is used during training.

To evaluate the results obtained from the U-Net and Mask R-CNN models, we employed the following metrics along with their definitions:

Mean Average Precision (mAP) evaluates the accuracy of object detection and instance segmentation models. It considers both precision and recall across all classes and computes the average precision for each class. mAP is calculated by interpolating the precision-recall curve at various points and then taking the mean of the average precision across all classes.

Precision measures the ratio of true positive predictions (correctly identified instances of the positive class) to all instances predicted as positive. It focuses on the accuracy of positive predictions.

$$Precision = \frac{True Positives}{True Positives + False Positives}$$

Recall measures the ratio of true positive predictions to all actual instances of the positive class. It assesses the model's ability to capture all positive instances.

$$Recall = \frac{True Positives}{True Positives + False Negatives}$$

In these calculations:

True Positives (TP) are instances correctly classified as positive.

False Positives (FP) are instances incorrectly classified as positive (but are actually negative).

False Negatives (FN) are instances incorrectly classified as negative (but are actually positive).

## 4.3. Dental age estimation

Once the existing teeth are detected, the combination of missing teeth is used to pre-diagnose the responsible gene. However, for the objective of this study, it was necessary to consider the dental age of the patient, to confirm that the absence of certain teeth was the result of a developmental anomaly and not the young dental age of the patient (for example, germs of premolars only appear on X-rays around 4 years of age; of third permanent molars around 8 years of age). Dental age estimations can be related to a classification or to regression tasks, which is why many papers have used machine learning and deep learning algorithms to classify dental panoramic radiographs based on dental age [41–46]. However, in our case, we did not have enough data from dental panoramic radiographs to use a neural network classifier. Therefore, we developed an algorithm to assess dental age. It draws on the way dentists determine patients dental age: observation of tooth eruption. Tooth eruption is characterized by the emergence of the tooth from the

<footnote n="3">³ https://github.com/matterport/Mask_RCNN.</footnote>
maxillary or mandibular bone support within the oral cavity. The algorithm is based on the eruption of teeth as a function of age. This output is measured by calculating the intersection between the surface of each tooth crown and the surface between the two alveolar bone supports, represented by the green lines in Fig. 3.

A new training of the Mask R-CNN model was therefore necessary to integrate the detection of the maxillary or mandibular bone support, and then to estimate the eruption stage of the detected teeth. We annotated the oral cavity in each image and then added this new class to the first dataset annotations. We used the 123 panoramic of control patients, we split these radiographs on train/test with 37 left for testing. We used data augmentation techniques including zooming, cropping, intensity variation, and blur effects. These techniques do not alter the relative position of teeth in the images.

Our medical experts estimated the eruption age of each of the primary and permanent teeth based on the reference atlas [47,48]. We obtained 13 age classes, from 6 months to 18 years. A zero-surface intersection corresponds to a tooth that has not erupted, while a non-zero intersection represents the complete or partial eruption. The summary Table III below allows us to summarize the eruption ages selected for each tooth.

The diagram below, Fig. 4, describes the processing performed after detection.

Using the eruption rate can help refine dental age estimation. This eruption rate is related to the patient dental development [23]. It is defined as follows:

$$Rate of eruption = \frac{A_{intersection}}{A_{tooth}}$$

Where $A_{tooth}$ is the total surface area of the tooth and $A_{intersection}$ is the surface area of the emerging part of the tooth from the bone support. Thus, a tooth with less than 10 % of its total surface area emerging indicates an age close to that of eruption, while a tooth with about 50 % of its surface area outside the bone support is a tooth that has finished growing, having emerged for a longer time.

Two verification mechanisms were also introduced to make dental age estimation more robust.

* The observation score: quantification of the number of teeth detected for the detected age.

$$S_{obs} = \frac{\text{nb of teeth out at the assessed age}}{\text{nb of teeth out at the theoretical age}}$$

* Verification score: census of detected teeth for all ages below the detected dental age. This is done sequentially by separating permanent teeth (6 years and older and primary teeth 2.5 years and younger)

<figure><img src="image_6.png" /><figcaption>Fig. 3. Example of tooth eruption detection by surface intersection measurement, where the green lines represent the alveolar bone supporting tissue, the tooth in red, and the erupted tooth surface in yellow. In the right image, we illustrate the situation where the tooth has not yet erupted, and the intersection between the contour of the tooth in blue and the two alveolar support lines in green is zero.</figcaption></figure>

<table><caption>**Table 3**
Presentation of the age groups and their representative teeth.</caption><thead><tr><td>Dental age (years)</td><td>Identification of the last erupted teeth</td></tr></thead><tbody><tr><td>0.5</td><td>51, 61, 71, 81</td></tr><tr><td>1</td><td>52, 62, 72, 82</td></tr><tr><td>1.5</td><td>54, 64, 74, 84</td></tr><tr><td>2</td><td>53, 63, 73, 83</td></tr><tr><td>2.5</td><td>55, 65, 75, 85</td></tr><tr><td>6</td><td>16, 26, 36, 46</td></tr><tr><td>7</td><td>11, 21, 31, 41</td></tr><tr><td>8</td><td>12, 22, 32, 42</td></tr><tr><td>9</td><td>33, 43</td></tr><tr><td>10</td><td>14, 24, 34, 44</td></tr><tr><td>11</td><td>13, 23, 15, 25, 35, 45</td></tr><tr><td>12</td><td>17, 27, 37, 47</td></tr><tr><td>18+</td><td>18, 28, 38, 48</td></tr></tbody></table>

$$S_{verif} = \frac{\text{nb of teeth out}}{\text{nb of teeth out at the theoretical age} - n}$$

The observation score indicates whether the assessed age has been reached. If only some of these teeth are out, then the actual dental age is in the first part of the age range. On the contrary, if it is high (close to 100 %), then the dental age is in the upper part of the range of the determined class.

The verification score allows to detect inconsistencies, either in the detection or in the dentition: this score highlights the fact that teeth have not been detected when they should already be out at the observed age. It can therefore highlight a problem in dental development, but also allows correction of the initially determined dental age. Here is how this correction algorithm works.

1. Dental age is first determined based on the detected erupted tooth corresponding to the highest age. This is noted as $A_{init}$.

2. The erupted teeth corresponding to the age $A_{init} - 1$ are checked.

1. If at least one of them is present, the dental age is correct. $A_{corr} = A_{init}$

2. If not, the step is repeated with the teeth of $A_{init} - 2$

3. If $S_{verif}$ of A-N = 0 %, the verification is repeated at the lower age.

4. When A-N > 0 %. The correction stops. The final age kept is the first one with a non-zero verification score.

$$A_{corr} = A_{init} - N$$

## 4.4. Missing teeth pattern detection

After detecting and identifying the present and missing teeth and estimating the dental age, we set up a method for identifying the pattern of missing teeth for each individual and comparing it to recognized patterns defined by gene dysregulation. This problem comes down to a classification problem.

Because of the small amount of data we have, we first developed a classification method capable of comparing different rare diseases to determine the possible genotype of each patient based on their missing teeth. Then, we compared the results of our method to classification machine learning algorithms trained on our dataset.

### 4.4.1. Comparative method

We developed a method to compare dental models of rare disease with the aim of matching the model that shows the highest degree of concordance with a patient's combination of missing teeth. This comparison was carried out on data collected by our experts, and which contains the probability of agenesis for each tooth and for each disease [9,22]. To compare two diseases, we calculated for each tooth the difference between the agenesis probabilities associated to these two
<figure><img src="image_7.png" /><figcaption>Fig. 4. How our dental age assessment algorithm works.</figcaption></figure>

diseases. The greater this difference, the more indicative the presence or absence of this tooth becomes in distinguishing one disease from the other. Defining the significance threshold is essential. Setting a high threshold would keep only teeth that strongly distinguish one disease from another. However, it is crucial not to make it excessively high, as Mask RCNN may wrongly classify some teeth as absent when they are actually present. Additionally, we need to avoid being overly restrictive because it could impact our subsequent multi-class comparisons as well. Therefore, a balanced threshold of 20 has been established empirically. The absence of a tooth was deemed characteristic when the agenesis probability difference exceeded 20, and its presence was considered characteristic when the difference was less than -20.

In Fig. 5, the teeth framed in green are the teeth with a difference in agenesis probability greater than 20. Their absence is strongly characteristic of PAX9 associated molar agenesis compared to WNT10A mutation related missing teeth. Conversely, the teeth framed in red have a difference in probability of agenesis lower than -20. Their presence is therefore strongly characteristic of PAX9 disease compared to WNT10A. We obtained the following Table IV:

To compare two diseases with remarkably similar scores, we devised a multi-level classification system where the coefficients are proportional to the difference in agenesis probabilities. For example, when comparing PAX9 and WNT10A in this order, we noticed that the difference in agenesis probability of tooth #16 is 51.37 %, therefore the absence of tooth #16 is characteristic of PAX9 disease, and we associate it with a coefficient of 5. On the other hand, tooth #35 has a difference in agenesis probability of -30.9 %. Thus, it is the presence of the tooth #35 which characterizes disease PAX9 compared to disease WNT10A, and this with a coefficient of 3. By applying this rule and only keeping the probability differences greater than 20 %, we constructed the multi-level comparison of PAX9/WNT10A, Table V.

Using Table V, we can calculate a score for each of two diseases. Let's take the example of a patient with PAX9 related hypo/oligodontia and the following missing teeth: 15, 25, 35, 45, 55, 16, 26, 17, 27, 37, 47, 18, 28, 38 and 48.

$$ score[PAX9] = \frac{\left[ \frac{2}{2} * 5 + \frac{4}{4} * 3 + \frac{0}{2} * 2 + \frac{4}{6} * 3 + \frac{6}{6} * 2 \right]}{15} = 0.8 $$
<figure><img src="image_8.png" /><figcaption>Fig. 5. Dentition model comparison of PAX9 and WNT10A using the D4/phenodent template.</figcaption></figure>

<table><caption>**Table 4**

Comparative table of PAX9 and WNT10A missing teeth pattern with their coefficients in one level comparison.</caption><thead><tr><th>Category</th><th>Teeth</th><th>Coefficient</th></tr></thead><tbody><tr><td>Missing</td><td>16, 26, 36, 46, 17, 27, 37, 47</td><td>1</td></tr><tr><td>Present</td><td>12, 22, 32, 42, 13, 23, 14, 24, 34, 44, 35,45</td><td>1</td></tr></tbody></table>

<table><caption>**Table 5**

Comparative table of PAX9 and WNT10A missing teeth pattern with multi-level coefficients.</caption><thead><tr><th>Category</th><th>Teeth</th><th>Coefficient</th></tr></thead><tbody><tr><td rowspan="3">Missing</td><td>16, 26</td><td>5</td></tr><tr><td>17, 27, 37, 47</td><td>3</td></tr><tr><td>36, 46</td><td>2</td></tr><tr><td rowspan="2">Present</td><td>12, 22, 34, 44, 35,45</td><td>3</td></tr><tr><td>13, 23, 14, 24, 32, 42</td><td>2</td></tr></tbody></table>

$$ score[WNT10A] = 1 - 0.8 = 0.2 $$

The probability associated with PAX9 disease is much higher than that of WNT10A, so the diagnosis leans towards PAX9. This one-level binary classification can be used for multi-class classification. Returning to the example of the comparison of PAX9 and WNT10A, we wanted to add a third disease by also comparing PAX9 to EDA. First, a single coefficient binary comparison of PAX9 to EDA was performed (Table VI).

<table><caption>**Table 6**

Comparative table of PAX9 and EDA in one-level binary classification.</caption><thead><tr><th>Category</th><th>Teeth</th><th>Coefficient</th></tr></thead><tbody><tr><td>Missing</td><td>16, 26, 36, 46, 17, 27, 37, 47, 18, 28, 38, 48, 15, 25</td><td>1</td></tr><tr><td>Present</td><td>12, 22, 32, 42, 13, 23, 34, 44, 11, 21, 31, 41, 33, 43</td><td>1</td></tr></tbody></table>

<table><caption>**Table 7**

Comparative table of PAX9 vs EDA and WNT10A.</caption><thead><tr><th>Category</th><th>Teeth</th><th>Coefficient</th></tr></thead><tbody><tr><td rowspan="2">Missing</td><td>16, 26, 36, 46, 17, 27, 37, 47</td><td>2</td></tr><tr><td>18, 28, 38, 48, 15, 25</td><td>1</td></tr><tr><td rowspan="3">Present</td><td>12, 22, 32, 42, 13, 23, 34, 44</td><td>2</td></tr><tr><td>11, 21, 31, 41, 33, 43, 14, 24, 35, 45</td><td>1</td></tr></tbody></table>

By comparing Table IV and V and Table VI, several similarities could be seen. The absence of permanent first and second molars 16, 26, 36, 46, 17, 27, 37 and 47 is characteristic of PAX9 disease compared to EDA but also compared to WNT10A, as well as the presence of teeth 12, 22, 32, 42, 13, 23, 34 and 44. It is important that the absence or presence of teeth characteristic of PAX9 disease compared to EDA and WNT10A is highlighted in the calculation of the score. Therefore, a second coefficient was added. Teeth which are present in both binary comparisons will therefore have a coefficient of 2 (Table VII).

We then calculated the PAX9 score for our example patient (whose missing teeth were: 15, 25, 35, 45, 55, 16, 26, 17, 27, 37, 47, 18, 28, 38 and 48):

$$ score[PAX9] = \frac{\left[ \frac{5}{8} * 2 + \frac{6}{6} * 1 + \frac{8}{8} * 2 + \frac{8}{10} * 1 \right]}{6} = 0.84 $$

The determination of the coefficients is done automatically. It is therefore possible to add any disease for which the dentition model is known, see Fig. 6. The number of coefficients is proportional to the number of diseases compared, so if we compare *n* diseases, the coefficients will be between 1 and *n-1* for the two categories "Missing" and "Present".
<figure><img src="image_9.png" /><figcaption>Fig. 6. General pipeline of pattern diseases comparison method.</figcaption></figure>

### 4.4.2. Machine learning methods

We compared our method for multi-class classification with several different supervised machine learning classification algorithms: decision tree (DT), random forest (RF), support vector machine (SVM), logical regression (LR).<fnref n="4" />

We randomly split the dental agenesis subjects' rows of dataset of Table 1 into training and validation with a ratio of 70/30, and then we trained the models using grid search with the cross-validation strategy splitting on 5 folds to find the best parameters of each model.

### 4.5. Hardware

The purpose of the tooth detection and identification tool is to be integrated into existing equipment (the anonymization box from e-media [49]), which has its own environment. The main challenge here is therefore to include all the necessary environment for carrying out the detection, from setting up AI to image processing requirements, without modifying the current operation and environment of the existing equipment. To process the panoramic radiographs, we used two deep neural networks: U-Net for pre-processing and Mask R-CNN for tooth detection and identification. Using two different neural networks, each requiring a different environment, was a challenge. We decided to use a Docker container to encapsulate this part of the program which allowed us to integrate independent environments, without affecting the main system environment. To limit the resources and python libraries kept in memory, and thus avoid redundancy of information at the application level, we chose to use a single Docker container. The Mask R-CNN code was migrated to Tensorflow version (TF 2.3.1), to ensure compatibility between the necessary environments and thus limit redundancies in the creation of the docker. The diagram below (see Fig. 7) illustrates the architecture achieved using a single Docker for the entire processing.

## 5. Results

### 5.1. Preprocessing

Following the training of the U-Net model to detect the oral cavity, we analyzed the results obtained. In Fig. 8, we present an example of a result from the U-Net model detecting the oral cavity in a dental panoramic radiograph. We observe that the oral cavity is well segmented, although there are some artifacts present. Through postprocessing

<figure><img src="image_10.png" /><figcaption>Fig. 7. Integration of our pipeline in e-media Infrastructure.</figcaption></figure>

<figure><img src="image_11.png" /><figcaption>Fig. 8. Result of oral cavity detection on the panoramic radiograph by the U-Net model.</figcaption></figure>

techniques, such as filtering, we can eliminate these small segmentation errors to achieve optimal detection.

The pre-processing performed by U-Net following by cropping allowed us to reduce the teeth detection area centered on the oral cavity, as shown in Fig. 9.

Table VIII shows the results obtained with the different pre-processing methods. These results were calculated based on the images of control subjects and those with rare oral diseases with missing teeth. Only the overall summary table is presented, as the results are roughly equivalent in the two populations. The table shows the number of images where at least one false positive was found as well as the type of false detections. We noted that the lateral cropping of the image by U-Net allowed to drastically reduce the number of false positives (–72% of false detections and –66% of concerned images) related to bones or

<footnote n="4">⁴ We used Scikit Learn in all our experimentations https://scikit-learn.org/stable/supervised-learning.html#supervised-learning.</footnote>
<figure><img src="image_12.png" /><figcaption>**Fig. 9.** Original image and teeth detection with cropping. The detected teeth are represented by different colors, where teeth of the same class are depicted in the same color.</figcaption></figure>

elements outside the oral cavity. However, this lateral cropping causes an increase in false detections of the nasal walls or cavities (+61 % of false detections). The two crop methods, crop 33 % and crop 50 %, gave equivalent results and made it possible to considerably reduce the different types of false detections.

## 5.2. Teeth detection and numbering with mask R-CNN

After conducting the three trainings mentioned in Table II using Mask RCNN, we evaluated the best models on multiple test datasets consisting of 30 dental radiographs of control subjects and 31 radiographs of subjects with dental agenesis. We systematically obtained a mAP exceeding 79 % which shows a good teeth segmentation. The results of the tooth numbering are shown in Table IX where we calculated the precision and recall on the test data by including a post-processing layer which consisted only in removing the duplicate predictions. We note that on the three trainings, the results obtained on the test dataset of control subjects are systematically better than those obtained from dental agenesis subjects. Also, we note the improvement of the classification results on the test datasets of dental agenesis subjects by adding radiographs in training, see Table IX.

As mentioned in the methods section, there is a limited number of methods and studies that have tackled the challenge of mixed dentition, encompassing both permanent and deciduous teeth.

<table><caption>**Table 8**

Results obtained with the different cropping methods as function of false detection type.</caption><thead><tr><th>Type of false detection</th><th>Application</th><th>Mask R-CNN only</th><th>Lateral U-Net + Mask R-CNN</th><th>U-Net (crop 33 %) + Mask R-CNN</th><th>U-Net (Crop 50 %) + Mask R-CNN</th></tr></thead><tbody><tr><td rowspan="2">Lateral, outside the oral cavity (spine)</td><td>Number of images</td><td>99</td><td>33</td><td>34</td><td>32</td></tr><tr><td>Number of false positives</td><td>127</td><td>36</td><td>36</td><td>33</td></tr><tr><td rowspan="2">Slits or nasal walls</td><td>Number of images</td><td>22</td><td>35</td><td>4</td><td>6</td></tr><tr><td>Number of false positives</td><td>28</td><td>45</td><td>6</td><td>7</td></tr><tr><td rowspan="2">Others outside the dental area (jaw)</td><td>Number of images</td><td>12</td><td>8</td><td>2</td><td>5</td></tr><tr><td>Number of false positives</td><td>14</td><td>9</td><td>2</td><td>5</td></tr></tbody></table>

<table><caption>**Table 9**

Comparison of the obtained results by the best mask r-cnn models of the three training.</caption><thead><tr><th>Training</th><th>Test data (radiographs)</th><th>Precision (%)</th><th>Recall (%)</th></tr></thead><tbody><tr><td rowspan="2">N° 1</td><td>Control subjects</td><td>91.01</td><td>89.70</td></tr><tr><td>Dental agenesis subjects</td><td>58.73</td><td>48.92</td></tr><tr><td rowspan="2">N° 2</td><td>Control subjects</td><td>92.55</td><td>92.17</td></tr><tr><td>Dental agenesis subjects</td><td>66.11</td><td>57.40</td></tr><tr><td rowspan="2">N° 3</td><td>Control subjects</td><td>94.52</td><td>90.52</td></tr><tr><td>Dental agenesis subjects</td><td>71.87</td><td>67.02</td></tr></tbody></table>

Kılıç et al. [36], utilized a Faster R-CNN model in combination with Google Net Inception v 2 architecture to detect and number the deciduous teeth of children. Their model, trained and tested on 421 panoramic images, demonstrated high sensitivity and precision rates, achieving an estimated sensitivity, precision, and F1 score of 0.9804, 0.9571, and 0.9686, respectively. Pinheiro et al. [37]), trained and tested Mask R-CNN with PointRend module on 450 dental panoramic X-rays to detect and number both permanent and deciduous teeth, obtaining a numbering mAP of 75.3 %. While their results showed accurate detection and classification of most teeth, challenges remained, particularly in mislabeling instances involving molars and deciduous teeth. In another study, Rubiu et al. [38]), applied Mask R-CNN to the Tufts Dental Database, comprising 1000 panoramic dental radiographs featuring both deciduous and permanent teeth. They reported a classification accuracy of 98.4 % and a dice score of 87 %, albeit noting suboptimal segmentation performance, particularly for the right mandibular third molar. Notably, a limitation arises from the absence of some classes of deciduous teeth in the test set, for which no performance information is available. Furthermore, Kaya et al. [39], employed YOLO V4 for automated tooth detection and numbering on 4545 pediatric panoramic X-ray images of patients aged 5–13. Their model achieved successful detection and numbering of mixed dentition with a mean average precision (mAP) value of 92.22 %. Bumann et al. [40], introduced a collaborative learning model to identify primary and permanent teeth and detect fillings on panoramic radiographs, aiming to improve radiograph interpretation accuracy. Leveraging Mask R-CNN, they developed high-performance classifiers achieving mean average precision (mAP) of 95.32 % for tooth segmentation and 91.53 % for filling detection. Their novel collaborative learning method, integrating tooth segmentation and filling detection, enhanced recognition performance with a mAP of 94.09 %.

Comparing our results with those obtained in the previously analyzed studies, we observe similar outcomes in terms of the F1 score achieved on the control dataset. Specifically, while our study achieved an F1-score of 92.47 % on the control images, similar F1 scores were reported by Kılıç et al. [36] (96.86 %) and Bumann et al. [40] (99.30 %). This suggests that our model performance on the control dataset is comparable to the state-of-the-art methods reported in the literature. However, when considering images with dental agenesis, our model performance seems to decrease, with an F1-score of 69.41 %. Comparing the dataset sizes used in our study with those of previous works, we
<figure><img src="image_13.png" /><figcaption>**Fig. 10.** Confusion matrix obtained by model 3 on dental agenesis testing dataset. The cells on the diagonal (green) represent correct predictions, in red are misclassifications of neighbor teeth, in blue are errors between permanent and deciduous teeth, and in orange are displayed bilateral symmetries errors.</figcaption></figure>

employed a relatively smaller dataset, consisting of 216 images for both the control and agenesis datasets. In contrast, some earlier studies utilized larger datasets, such as Kaya et al. [39] with 4545 images and Rubiu et al. [38] with 1000 images. Despite the smaller dataset size, our model achieved competitive performance, particularly on the control dataset. However, the decrease in performance on the agenesis dataset suggests the need for further investigation and possibly a larger dataset to improve the model robustness in handling cases of missing teeth.

Finally, out of the dental agenesis test dataset, 387 of 557 teeth (69,48 %) were correctly classified with the third model, see the confusion matrix for details in Fig. 10.

Analyzing the metrics for each class, we notice good detection and classification of teeth #11, 21, 32, 33, 43, 73, 34, 44, 74, 84, and 85. On the contrary, we observe very poor detection of tooth #17, with 10 non-detections and 2 false detections. The performance for teeth #18, 28, 38, and 48 cannot be evaluated as there are few examples. Indeed, most people with rare diseases do not develop a third molar.

The most frequent error (53 %) was confusion with a neighbor tooth. The algorithm also had difficulty differentiating a permanent tooth from its primary counterpart (39 %) and finally bilateral symmetries were sometimes confused (8 %).

### (1) Conflicts between neighbor teeth

Upon manual analysis of several examples, it is observed that this type of conflict is generally the result of either non-detection or excessive detection. If AI model misses a tooth, the entire classification will be shifted. Thus, avoiding these conflicts involves improving detection.

### (2) Conflicts between permanent and temporary teeth

In the training data, 60 % of subjects are over 12 years old. Our initial hypothesis was that the model is more accustomed to recognizing permanent teeth than temporary ones and therefore more often confuses a primary tooth with a permanent tooth. However, detailed analysis showed that 67 % of conflicts between permanent and temporary teeth

involve permanent teeth being mistaken for primary teeth. A detailed study was therefore conducted on several examples, revealing that.

- Individuals with rare diseases generally have few permanent teeth lodged inside the gums. Distinguishing between permanent and temporary teeth is therefore more complex. In healthy individuals, a primary tooth is identifiable because a permanent tooth is located beneath it. However, individuals with rare diseases have the peculiarity of not developing all the permanent teeth typically found in adults.

- The size of the tooth is a parameter considered by artificial intelligence, as permanent teeth mistaken for primary teeth are smaller than "typical" permanent teeth.

### 5.3. Dental age estimation algorithm

Our dental age estimation algorithm underwent evaluation using data from control patients. Table X provides a summary of the results obtained from analyzing 37 test images. The algorithm was applied both with and without correction, and the estimated age was then compared to the chronological age. It is important to note that the chronological age should not be considered as an absolute reference due to variations in development among individuals, but it serves as an indicative measure within the context of healthy individuals. Ruiz-Mealin et al. [50] showed a statistically significant association between the severity of dental agenesis and the delay in dental age when measured with dental age assessment methods.

<table><caption>**Table 10**

Obtained results of dental age assessment method with and without correction.</caption><thead><tr><td>Method</td><td>Average difference with real age using Mask R-CNN</td><td>Average difference with the real age using Ground Truth</td></tr></thead><tbody><tr><td>Without correction</td><td>2.47 years</td><td>1.22 years</td></tr><tr><td>With correction</td><td>1.90 years</td><td>1 year</td></tr></tbody></table>
The estimated age, calculated by assessing the intersection between tooth contour and bone support, was calculated using ground truth annotations and Mask R-CNN predictions from training n°3. When we applied the algorithm with correction based on ground truth annotation, it yielded an average difference of 1 year compared to the chronological age. However, when we relied on Mask R-CNN to estimate tooth eruption, the results were less reliable, with an average difference of 2.47 years without correction and 1.9 years with correction. Nevertheless, our algorithm based on tooth eruption has a notable limitation when estimating age discrepancies with chronological age because the highest age class considered is 18 years old. Additionally, our analysis of tooth eruption does not provide precise age assessments for individuals between the ages of 2.5–6 years old and 12–18 years old.

## 5.4. Pattern comparison

We analyzed the results of our approach to binary classification according to the level used, namely one level and multi-levels. The results are reported in Table XI. We have also reported the results of the pipeline in the same table: teeth detection and numbering by Mask R-CNN plus pattern determination using our novel approach (one and multi-levels). We also compared the outcomes to the ground truth, i.e., teeth detection and numbering by the experts to test the entire pipeline.

The average accuracy is the mean of the accuracies of the three classifications: PAX9/EDA, PAX9/WNT10A and EDA/WNT10A. We also reported in the table (in the last column) the number of cases in which the probabilities for two diseases are equal at 50 %, which implies that distinguishing between the two diseases is unfeasible. We noticed the use of expert annotations to determine missing teeth performed better than the use of Mask R-CNN. This difference in performance is related to the prediction errors of the neural network and not to the classification method itself. The multi-level comparison achieved the best average accuracy at 88 % when the missing teeth were determined automatically. When expert annotations were used, the average accuracy decreased to 84 % compared to 89 % for the single-level comparison method. However, the number of indistinguishable elements decreased from 13 to 6, which was a good improvement.

We then compared the results of the multi-level method applied to the three diseases WNT10A, PAX9 and EDA, related dental agenesis and compared the results obtained with those of the machine learning models evaluated on the test dataset as detailed in the previous section D-2. The results are reported in Table XII.

The multi-level approach applied to the classification of the three diseases yields an average accuracy of 78 %, a score much lower than the scores of the machine learning models which give scores of 86 % for the decision tree and 90 % for RF, SVC and LR. However, these results must be put in perspective: our method does not require automatic learning, and we applied it to the entire dataset. The machine learning algorithms were trained on 70 % of the dataset and then evaluated on the remaining 30 %, which is an important limitation of the comparison. On the other hand, our approach is completely explainable because missing and

<table><caption>**Table 11**
Performances of the binary classification method using experts' annotations (ground truth) and mask r-cnn teeth segmentation predictions.</caption><thead><tr><th>Principle</th><th>Missing Tooth</th><th>Average Accuracy</th><th>Indistinguishable elements</th></tr></thead><tbody><tr><td rowspan="3">One level of comparison</td><td>Ground-Truth</td><td>89 %</td><td>13</td></tr><tr><td>Mask R-CNN</td><td>78 %</td><td>9</td></tr><tr><td>Ground-Truth</td><td>88 %</td><td>6</td></tr><tr><td rowspan="2">Multilevel of comparison</td><td>Mask R-CNN</td><td>84 %</td><td>8</td></tr></tbody></table>

<table><caption>**Table 12**
Machine Learning model results compared to our multi-level classification method.</caption><thead><tr><th>Model</th><th>Average Accuracy</th><th>Indistinguishable elements</th></tr></thead><tbody><tr><td>Decision Tree</td><td>86 %</td><td>–</td></tr><tr><td>Random Forest</td><td>90 %</td><td>–</td></tr><tr><td>SVC</td><td>90 %</td><td>–</td></tr><tr><td>Logistic regression</td><td>90 %</td><td>–</td></tr><tr><td>Multi-level classification</td><td>78 %</td><td>2</td></tr></tbody></table>

<table><caption>**Table 13**
Performances of the multi-class classification method using experts' annotations (ground truth) and mask r-cnn teeth segmentation predictions.</caption><thead><tr><th>Multi-class classification</th><th>Missing teeth</th><th>Average Accuracy</th><th>Indistinguishable Elements</th></tr></thead><tbody><tr><td rowspan="3">PAX9, EDA and WNT10A</td><td>Ground Truth</td><td>78 %</td><td>2</td></tr><tr><td>Mask R-CNN</td><td>72 %</td><td>2</td></tr><tr><td>Ground Truth</td><td>85 %</td><td>2</td></tr><tr><td rowspan="2">PAX9, EDA, WNT10A and MSX1</td><td>Mask R-CNN</td><td>72 %</td><td>2</td></tr></tbody></table>

present teeth used for classification and the associated coefficients were determined directly from the dental patterns.

We then analyzed the results of the multi-class classification when using Mask R-CNN to detect and number the teeth, applied to the three main diseases and including the MSX1 related disease for which 5 annotated radiographs are available. The results are reported in Table XIII.

We note that for multi-class classification, the use of Mask R-CNN reduces the accuracy. We also note that the addition of the MSX1 disease improves the accuracy score on the 4 diseases classification, as we obtain an accuracy of 85 %.

## 5.5. Hardware

We implemented the tool in the information system of the University Hospital of Strasbourg. The joint encapsulation of the two deep learning models (U-Net and Mask R-CNN) allowed a reduction of 15 % of the disk space usage. We present below (Fig. 11) the interface of the demonstrator, displaying the original image after processing, with the segmentation of each tooth detected. Below the image, the dental age assessment and the responsible genes are displayed with the associated symptoms.

# 6. Discussion

In this study, we proposed a pipeline aimed at pre-diagnosing specific rare oral diseases associated with dental agenesis. Our approach involved using a U-Net model to detect the oral cavity, followed by applying the Mask R-CNN model to detect, segment, and number the teeth present in panoramic radiographs, whether deciduous or permanent. Additionally, we integrated a dental age estimation model that uses tooth and eruption detection. These processing steps enabled us to identify both the teeth present and those that are missing, which is crucial for pre-diagnosing rare diseases with hypo/oligodontia.

Our research demonstrated that incorporating preprocessing, along with a U-Net model to aid in segmenting the oral cavity, significantly enhances the effective detection of teeth and reduces false positives. Further preprocessing techniques, such as histogram equalization, may be required to refine detection.

As indicated in the literature, Mask R-CNN is a fairly effective model for segmenting and numbering teeth in dental panoramic images, especially when individuals have nearly all their teeth. However, in
<figure><img src="image_14.png" /><figcaption>Fig. 11. Interface of the demonstrator.</figcaption></figure>

cases of severe tooth agenesis, Mask R-CNN encounters difficulties in performing this segmentation due to gaps and missing teeth. The integration of pre-processing and post-processing steps has contributed to reducing the likelihood of false detections, although some numbering errors persist.

This work highlights the challenges associated with tooth segmentation methods, particularly in cases of mixed dentition. On panoramic radiographs, where several teeth may be missing, the absence of consistent reference teeth compromises the model ability to identify teeth accurately. Additionally, overlapping structures, such as adjacent teeth, pose significant obstacles to accurate segmentation. Furthermore, the limited representation of certain classes of deciduous teeth in the datasets hinders evaluation of model performance for these specific teeth, highlighting a constraint in assessing segmentation accuracy. Exploring more modern deep learning architectures, particularly those based on Transformers and attention mechanisms, could improve detection and segmentation by adopting more comprehensive approaches. Additionally, post-processing techniques can improve numbering by using patterns based on tooth shape or position.

Improving the detection of teeth and bone support would have a direct impact on dental age estimation (to reduce the lack of precision between 2.5 and 6 years old and between 12 and 18 years old) and therefore an impact on the diagnosis. Indeed, obtaining more data would give better results in terms of dental age estimation.

Despite exhibiting lower performance compared to machine learning models in rare disease classification, our approach still yields promising results. Further improvements could be achieved by refining multi-level coefficient determination. A significant advantage of our approach is its complete explainability, which may facilitate its use and acceptance, especially in the medical device context. We are currently working on

replacing this multi-level technique with only machine learning models incorporating an explainability layer.

The i-Dent project is quite unique in that it focuses on the diagnosis of rare genetic oral and dental diseases through machine learning and deep learning, using only dental panoramic images. Despite increasing attention to deep learning for diagnosing anomalies and diseases related to oral and dental health [51], particularly those associated with missing teeth [52,53], the topic of diagnosing rare diseases through deep learning remains largely unexplored in the literature. Also, as mentioned in Ref. [51], it is essential to include explainable AI methods in studies for reliability, which is why we are currently using a template-based pattern matching method as a pre-diagnostic approach while awaiting the development of satisfactory explainable AI.

The i-Dent project is targeting not only dentists but also other health professionals contributing to rare diseases diagnosis.

If the dentist is indeed able to recognize and name teeth and identify missing teeth. He may be less aware of the association of missing teeth with rare genetic diseases [54] and the possibility of pattern recognition of tooth agenesis leading to the suspicion of the involvement of a possible gene. The current knowledge of dentist on rare diseases was reported inadequate in suitable screening and therapy [54]. The aim of the i-Dent tool is to facilitate rare disease recognition (on the basis of oral and tooth developmental anomalies here tooth agenesis) and the management of the patient including a precision diagnosis in collaboration with expert rare diseases centers. Missing teeth could indeed be a trigger sign leading to the diagnosis of a broader rare disease affecting teeth but also other organs and systems (like in the extended ectodermal dysplasia disease family [55] or pointing towards an association with cancer susceptibility for example [56]). This clinical diagnosis will be supported by genetic testing in personalized oral medicine.
# 7. Conclusion

Our study provides encouraging results for the automatic pre-diagnosis of rare genetic oral diseases with teeth agenesis. To the best of our knowledge, this is the first study using Artificial Intelligence for the detection of rare genetic oral disease. Despite the difficulties related to the small amount of data and the age of the subjects (confusion of deciduous teeth with permanent teeth), the improvement of the model by the methods described in this paper allowed us to obtain sufficiently reliable results for the detection of 3 distinct patterns of missing teeth associated with pathogenic variants in *PAX9*, *WNT10A* and *EDA* genes. The detection algorithm is reliable enough to be used for dental age detection and pre-diagnosis. However, this project is still a proof of concept. As it is now implemented in the HUS system, we will be able to retrieve new data as we use it and thus make it even more efficient.

Therefore an automated segmentation algorithm proved to be useful to overcome issues related to.

- **The complexity of segmentation**, tooth segmentation can be particularly complex, even for dentists, due to the effects of rare diseases on the shape and position of the teeth, especially in cases of severe dental agenesis. An automated algorithm can help overcome these challenges by providing consistent and accurate analysis.

- **Accuracy and efficiency**, offering superior accuracy and efficiency compared to manual methods. This reduces the risk of human error and enables faster diagnosis.

- **Real-time data**, by using such algorithm, dentists/users can obtain real-time results, which can be crucial for making quick and informed decisions in a clinical setting.

- **Reduction of workload**, automating the segmentation of missing teeth can significantly reduce the workload of dentists, allowing them to focus on more complex tasks and direct patient care

- **Tool for other health professionals or the general public**, the automated segmentation tool can also be designed to guide other health professionals (geneticist, pediatricians ...) less familiar with the oral cavity. In this context, an automatic tool is essential to provide precise and reliable information to non-specialized users, thus enabling better early detection and guidance toward appropriate care.

# CRediT authorship contribution statement

**Hocine Kadi:** Writing – review & editing, Writing – original draft, Validation, Supervision, Software, Resources, Project administration, Methodology, Investigation, Formal analysis, Conceptualization. **Marzena Kawczynski:** Supervision, Resources, Project administration, Methodology, Formal analysis, Data curation. **Sara Bendjama:** Validation, Resources, Formal analysis, Data curation. **Jesus Zegarra Flores:** Visualization, Validation, Supervision, Software, Methodology, Investigation, Conceptualization. **Audrey Leong-Hoi:** Resources, Project administration. **Hugues de Lastic:** Visualization, Software, Methodology, Investigation. **Julien Balbierer:** Visualization, Software, Methodology, Investigation. **Claire Mabileau:** Visualization, Software, Methodology, Investigation. **Jean Pierre Radoux:** Validation, Supervision, Software, Methodology, Investigation. **Bruno Grollemund:** Resources, Methodology, Investigation. **Jean Jaegle:** Supervision, Software, Methodology, Conceptualization. **Christophe Guebert:** Supervision, Software, Methodology, Conceptualization. **Bertrand Bisch:** Supervision, Software, Project administration, Methodology, Conceptualization. **Agnès Bloch-Zupan:** Writing – review & editing, Writing – original draft, Validation, Supervision, Project administration, Methodology, Investigation, Funding acquisition, Formal analysis, Data curation, Conceptualization.

# Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

# Acknowledgment

We thank patients and families for their trust, participation and continuous support in our projects. We also thank G. Icre, C. Englender, N. Kadiri, C. Lecomte, the CRIH team (HUS), Biovalley France, SATT Conectus (M. David), for their help throughout this project.

This work was supported by the following grants: No. 1.7 "RARENET: a trinational network for education, research and management of complex and rare disorders in the Upper Rhine" (2016–2019) co-financed by the European Regional Development Fund (ERDF) of the European Union in the frame-work of the INTERREG V and previously INTERREG IV Upper Rhine program; the project e-GenoDENT financed by the Fonds d'Intervention Régionale (FIR) of the Agence Régionale de Santé Grand Est (2019–2022); the French Fondation Maladies Rares Atelier de co-design en e-santé (2019) and the AMI Economie numérique Grand-Est i-Dent (2020–2021).

This work contributed to S. Bendjama's DDS thesis and received the following awards in 2022: Roger Rialland award, ANCD, Scientific and clinical price ADF/Dentsply Sirona.

# References

[1] Haute Autorité de Santé, Agénésies dentaires multiples : Oligodontie et anodontie, Protocole National De Diagnostic Et De Soins (PNDS), 2021. https://www.has-sante.fr/upload/docs/application/pdf/2021-11/synthese_mg_agenesies_dentaires_multiples_oligodontie_et_anodontie.pdf.

[2] S. Arte, Phenotypic and Genotypic Features of Familial Hypodontia, University of Helsinki, 2001, p. 100p.

[3] R.J. Gorlin, M.M. Cohen, R.C.M. Hennekam, Syndromes of the Head and Neck, fourth ed., Oxford university press, 2001 1344p.

[4] M. de La Dure-Molla, B.P. Fournier, M.C. Manzanares, A.C. Acevedo, R. C. Hennekam, L. Friedlander, M.L. Boy-Lefèvre, S. Kerner, S. Toupenay, P. Garrec, B. Vi-Fane, R. Felizardo, M.V. Berteretche, L. Jordan, F. Ferré, F. Clauss, S. Jung, M. de Chalendar, S. Troester, M. Kawczynski, J. Chaloyard, International Group of Dental Nomenclature, M.C. Manière, A. Berdal, A. Bloch-Zupan, Elements of morphology: standard terminology for the teeth and classifying genetic dental disorders, Am. J. Med. Genet. 179 (10) (2019) 1917-1981.

[5] P. Nieminen, Genetic basis of tooth agenesis, J. Exp. Zool. B Mol. Dev. Evol. 312B (4) (2009) 320-342, 15.

[6] A. Tucker, P. Sharpe, The cutting-edge of mammalian development; how the embryo makes teeth, Nat. Rev. Genet. 5 (7) (2004) 499-508.

[7] A. Bloch-Zupan, H. Sedano, C. Scully, Dento/oro/craniofacial Anomalies and Genetics, first ed., Elsevier, Boston, MA, 2012, p. 264p.

[8] M. de La Dure-Molla, C. Gaucher, N. Dupré, A. Bloch Zupan, A. Berdal, C. Chaussain, La dent : un marqueur d'anomalies génétiques du développement [The tooth: A marker of developmental abnormalities], Med. Sci. 40 (1) (2024) 16-23.

[9] B.P. Fournier, M.H. Bruneau, S. Toupenay, S. Kerner, A. Berdal, V. Cormier-Daire, S. Hadj-Rabia, A.E. Coudert, M. de La Dure-Molla, Patterns of dental agenesis highlight the nature of the causative mutated genes, J. Dent. Res. 97 (12) (2018) 1306-1316.

[10] M. Aarts, S. Mettenberger, E.M. Bronkhorst, E.M. Ongkosuwito, Oral health-related quality of life in patients with oligodontia: a FACE-Q assessment, J. Dent. 135 (2023) 104544.

[11] HAS, PNDS Agénésies dentaires multiples, oligodontie et anodontie, https://www.has-sante.fr/jcms/p_3301900/fr/agenesies-dentaires-multiples-oligodontie-et-anodontie-pnds, 2021.

[12] S.T. Hsieh, Y.A. Cheng, Multimodal feature fusion in deep learning for comprehensive dental condition classification, J. X Ray Sci. Technol. 32 (2) (2024) 303-321.

[13] A. Fatima, I. Shafi, H. Afzal, K. Mahmood, I.T. Díez, V. Lipari, J.B. Ballester, I. Ashraf, Deep learning-based multiclass instance segmentation for dental lesion detection, In Healthcare (Basel) 11 (3) (2023) 347.

[14] B. Abhisheka, S.K. Biswas, B. Purkayastha, A comprehensive review on breast cancer detection, classification and segmentation using deep learning, Arch. Comput. Methods Eng. 30 (8) (2023) 5023-5052.

[15] M.K. Prasad, V. Geoffroy, S. Vicaire, B. Jost, M. Dumas, S. Le Gras, M. Switala, B. Gasse, V. Laugel-Haushalter, M. Paschaki, B. Leheup, D. Droz, A. Dalstein, A. Loing, B. Grollemund, M. Muller-Bolla, S. Lopez-Cazaux, M. Minoux, S. Jung, F. Obry, V. Vogt, J.L. Davideau, T. Davit-Beal, A.S. Kaiser, U. Moog, B. Richard, J. J. Morrier, J.P. Duprez, S. Odent, I. Bailleul-Forestier, M.M. Rousset,
L. Merametdijan, A. Toutain, C. Joseph, F. Giuliano, J.C. Dahlet, A. Courval, M. El Alloussi, S. Laouina, S. Soskin, N. Guffon, A. Dieux, B. Doray, S. Feierabend, E. Ginglinger, B. Fournier, M. de la Dure Molla, Y. Alembik, C. Tardieu, F. Claus, A. Berdal, C. Stoetzel, M.C. Manière, H. Dollfus, A. Bloch-Zupan, A targeted next-generation sequencing assay for the molecular diagnosis of genetic disorders with orodental involvement, J. Med. Genet. 53 (2) (2016) 98-110.

[16] T. Rey, J. Tarabeux, B. Gerard, M. Delbarre, A. Le Béché, C. Stoetzel, M. Prasad, V. Laugel-Haushalter, Kawczynski, J. Muller, J. Chelly, H. Dollfus, M.C. Manière, A. Bloch-Zupan, Protocol GenoDENT: implementation of a new NGS panel for molecular diagnosis of genetic disorders with orodental involvement, Methods Mol. Biol. 1922 (2019) 407-452.

[17] C. Tardieu, S. Jung, K. Niederreither, M. Prasad, S. Hadj-Rabia, N. Philip, A. Mallet, E. Consolino, E. Sfeir, B. Noueiri, N. Chassaing, H. Dollfus, M.C. Manière, A. Bloch-Zupan, F. Claus, Dental and extra-oral clinical features in 41 patients with WNT10A gene mutations: a multicentric genotype-phenotype study, Clin. Genet. 92 (5) (2017) 477-486.

[18] K. Yu, J. Dou, W. Huang, F. Wang, Y. Wu, Expanding the genetic spectrum of tooth agenesis using whole-exome sequencing, Clin. Genet. 102 (6) (2022) 503-516.

[19] M.J. van den Boogaard, M. Créton, Y. Bronkhorst, A. van der Hout, E. Hennekam, D. Lindhout, M. Cune, H.K. Ploos van Amstel, Mutations in WNT10A are present in more than half of isolated hypodontia cases, J. Med. Genet. 49 (5) (2012) 327-331.

[20] D.W. Stockton, P. Das, M. Goldenberg, R.N. D'Souza, P.I. Patel, Mutation of PAX9 is associated with oligodontia, Nat. Genet. 24 (1) (2000) 18-19.

[21] Orphanet, D4/phenodent : Registre français de patients atteints des maladies rares odontologiques (2024).

[22] S. Bendjama, Identification des signatures diagnostiques dans les maladies rares avec hypo/oligodontie - Thèse pour le diplôme d'état de docteur en chirurgie dentaire N° 43.22.22.03, Faculté de Chirurgie Dentaire, Université de Strasbourg, 2002.

[23] M. Estai, M. Tennant, D. Gebauer, A. Brostek, J. Vignarajan, M. Mehdizadeh, S. Saha, Deep learning for automated detection and numbering of permanent teeth on panoramic images, Dentomaxillofacial Radiol. 51 (2) (2022) 20210296.

[24] O. Ronneberger, P. Fischer, T. Brox, U-net: convolutional networks for biomedical image segmentation, in: Medical Image Computing and Computer-Assisted Intervention-MICCAI (2015) 18th International Conference, Munich, Germany, October 5-9, 2015 vol. 18, Springer International Publishing, Proceedings, Part III, 2015, pp. 234-241.

[25] T.L. Koch, M. Perslev, C. Igel, S.S. Brandt, Accurate segmentation of dental panoramic radiographs with U-Nets, in: 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019), 2019, pp. 15-19.

[26] C. Sheng, L. Wang, Z. Huang, T. Wang, Y. Guo, W. Hou, L. Xu, J. Wang, X. Yan, Transformer-based deep learning network for tooth segmentation on panoramic radiographs, J. Syst. Sci. Complex. 36 (1) (2023) 257-272.

[27] G. Jader, J. Fontineli, M. Ruiz, K. Abdalla, M. Pithon, L. Oliveira, Deep instance segmentation of teeth in panoramic X-ray images, in: Proceedings of the 2018 31st SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI), Parana, Brazil, 29 October-1 November 2018, IEEE, Piscataway, NJ, USA, 2018, pp. 400-407.

[28] K. Zhang, J. Wu, H. Chen, P. Lyu, An effective teeth recognition method using label tree with cascade network structure, Comput. Med. Imag. Graph. 68 (2018) 61-70.

[29] B. Silva, L. Pinheiro, L. Oliveira, M. Pithon, A study on tooth segmentation and numbering using end-to-end deep neural networks, in: 2020 33rd SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI) IEEE, 2020, pp. 164-171.

[30] J.H. Lee, S.S. Han, Y.H. Kim, C. Lee, I. Kim, Application of a fully deep convolutional neural network to the automation of tooth segmentation on panoramic radiographs, Oral Surg Oral Med Oral Pathol Oral Radiol 129 (6) (2020) 635-642.

[31] G. Zhu, Z. Piao, S.C. Kim, Tooth detection and segmentation with mask R-CNN, in: 2020 International Conference on Artificial Intelligence in Information and Communication (ICAIIC) IEEE, 2020, pp. 70-72.

[32] Y. Zhao, P. Li, C. Gao, Y. Liu, Q. Chen, F. Yang, D. Meng, TSASNet: tooth segmentation on dental panoramic X-ray images by two-stage attention segmentation network, Knowl. Base Syst. 206 (2020) 106338.

[33] A. Almalki, L.J. Latecki, Self-supervised learning with masked image modeling for teeth numbering, detection of dental restorations, and instance segmentation in dental panoramic radiographs, in: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, 2023, pp. 5594-5603.

[34] S. Hou, T. Zhou, Y. Liu, P. Dang, H. Lu, H. Shi, Teeth U-Net: a segmentation model of dental panoramic X-ray images for context semantics and contrast enhancement, Comput. Biol. Med. 152 (2023) 106296.

[35] M. Kanwal, M.M. Ur Rehman, M.U. Farooq, D.K. Chae, Mask-transformer-based networks for teeth segmentation in panoramic radiographs, Bioengineering (Basel) 10 (7) (2023) 843.

[36] M.C. Kılıç, I.S. Bayrakdar, Ö. Çelik, E. Bilgir, K. Orhan, O.B. Aydın, F.A. Kaplan, H. Sağlam, A. Odabaş, A.F. Aslan, A.B. Yılmaz, Artificial intelligence system for automatic deciduous tooth detection and numbering in panoramic radiographs, Dentomaxillofacial Radiol. 50 (6) (2021) 20200172.

[37] L. Pinheiro, B. Silva, B. Sobrinho, et al., Numbering permanent and deciduous teeth via deep instance segmentation in panoramic X-rays, in: 17th International Symposium on Medical Information Processing and Analysis, SPIE, 2021, pp. 95-104.

[38] G. Rubiu, M. Bologna, M. Cellina, M. Cè, D. Sala, R. Pagani, E. Mattavelli, D. Fazzini, S. Ibba, S. Papa, M. Ali, Teeth segmentation in panoramic dental X-ray using mask regional convolutional neural network, Appl. Sci. 13 (13) (2023) 7947.

[39] E. Kaya, H.G. Gunec, S.S. Gokyay, S. Kutal, S. Gulum, H.F. Ates, Proposing a CNN method for primary and permanent tooth detection and enumeration on pediatric dental radiographs, J. Clin. Pediatr. Dent 46 (4) (2022) 293-298.

[40] E.E. Bumann, S. Al-Qarni, G. Chandrashekar, R. Sabzian, B. Bohaty, Y. Lee, A novel collaborative learning model for mixed dentition and fillings segmentation in panoramic radiographs, J. Dent. 140 (2024) 104779.

[41] Y.C. Guo, M. Han, Y. Chi, H. Long, D. Zhang, J. Yang, Y. Yang, T. Chen, S. Du, Accurate age classification using manual method and deep convolutional neural network based on orthopantomogram images, Int. J. Leg. Med. 135 (4) (2021) 1589-1597.

[42] E.G. Mohamed, R.P. Diaz Redondo, A. Koura, M.S. El-Mofty, M. Kayed, Dental age estimation using deep learning: a comparative survey, Computation 11 (2) (2023) 18.

[43] S.M. Kahaki, M.J. Nordin, N.S. Ahmad, M. Arzoky, W. Ismail, Deep convolutional neural network designed for age assessment based on Orthopantomography Data, Neural Comput. Appl. 32 (13) (2019) 9357-9368.

[44] N. Vila-Blanco, M.J. Carreira, P. Varas-Quintana, C. Balsa-Castro, I. Tomas, Deep neural networks for chronic-logical age estimation from OPG images, IEEE Trans. Med. Imag. 39 (7) (2020) 2374-2384.

[45] S. Wallraff, S. Vesal, C. Syben, R. Lutz, A. Maier, Age estimation on panoramic dental X-ray images using Deep Learning, in: C. Palm, T.M. Deserno, H. Handels, A. Maier, K. Maier-Hein, T. Tolxdorff (Eds.), Bildverarbeitung Für Die Medi-Zin, Informatik Aktuell, Springer Vieweg, Wiesbaden, 2021, pp. 186-191.

[46] D. Milosević, M. Vodanović, I. Galić, M. Subasić, Automated estimation of chronological age from panoramic dental X-ray images using deep learning, Expert Syst. Appl. 189 (2022) 116038.

[47] S.J. AlQahtani, M.P. Hector, H.M. Liversidge, Brief communication: the London atlas of human tooth development and eruption, Am. J. Phys. Anthropol. 142 (3) (2010) 481-490.

[48] S.J. AlQahtani, M.P. Hector, H.M. Liversidge, Accuracy of dental age estimation charts: schour and massler, ubelager and the London atlas, Am. J. Phys. Anthropol. 154 (1) (2014) 70-78.

[49] A software publisher specializing in medical imaging and e-health, the e-media group develops, installs and maintains IT solutions dedicated to radiology for its clients. https://www.e-media.fr/.

[50] E.V. Ruiz-Mealin, S. Parekh, S.P. Jones, D.R. Moles, D.S. Gill, Radiographic study of delayed tooth development in patients with dental agenesis, Am. J. Orthod. Dentofacial Orthop. 141 (3) (2012) 307-314.

[51] E. Sivari, G.B. Senirkentli, E. Bostanci, M.S. Guzel, K. Acici, T. Asuroglu, Deep learning in diagnosis of dental anomalies and diseases: a systematic review, Diagnostics 13 (15) (2023) 2512.

[52] E. Kim, J.J. Hwang, B.H. Cho, E. Lee, J. Shin, Classification of presence of missing teeth in each quadrant using deep learning artificial intelligence on panoramic radiographs of pediatric patients, J. Clin. Pediatr. Dent 48 (3) (2024) 76-85.

[53] S.T. Hsieh, Y.A. Cheng, Multimodal feature fusion in deep learning for comprehensive dental condition classification, J. X Ray Sci. Technol. 32 (2) (2024) 303-321.

[54] K. Benz, R. Trapp, M. Voss, M. Hanisch, U. Geisthoff, J. Jackowski, Awareness and knowledge of rare diseases in German dentists, dental specialists and oral and maxillofacial surgeons: a country-wide survey, Medicina (Kaunas) 58 (8) (2022) 1114.

[55] J.T. Wright, M. Fete, H. Schneider, M. Zinser, M.I. Koster, A.J. Clarke, S. Hadj-Rabia, G. Tadini, N. Pagnan, A.F. Visinoni, B. Bergendał, B. Abbott, T. Fete, C. Stanford, C. Butcher, R.N. D'Souza, V.P. Sybert, M.I. Morasso, Ectodermal dysplasias: classification and organization by phenotype, genotype and molecular pathway, Am. J. Med. Genet. 179 (3) (2019) 442-447.

[56] L. Lammi, S. Arte, M. Somer, H. Jarvinen, P. Lahermo, I. Thesleff, S. Pirinen, P. Nieminen, Mutations in AXIN2 cause familial tooth agenesis and predispose to colorectal cancer, Am. J. Hum. Genet. 74 (5) (2004) 1043-1050.