# Duodenal Endoscopic Image Dataset for Celiac Disease Detection

Souaad Hamza-Cherif, Fethallah Gaouar, Zineb Aziza Elaouber, Nesma Settouti

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Souaad Hamza-Cherif, Fethallah Gaouar, Zineb Aziza Elaouber, Nesma Settouti. Duodenal Endoscopic Image Dataset for Celiac Disease Detection. 2025, 10.17632/t278gdxwzc. hal-05332884

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Submitted on 27 Oct 2025

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# Duodenal Endoscopic Image Dataset for Celiac Disease Detection

Souaad Hamza-Cherif¹,*, Fethallah Gaouar², Zineb Aziza El Auaber¹,  
Nesma Settuti ³

¹ Biomedical Engineering Laboratory, Faculty of Technology, University of Tlemcen, Algeria

² Service de gastroenterologie, Clinique Mimosa, Tlemcen, Algeria

³ LabISEN - Yncréa Ouest, Caen, France

* souad.hamzacherif@univ-tlemcen.dz

# 1 Background

Celiac disease is a chronic autoimmune disorder triggered by an abnormal immune response to gluten, a group of proteins found in wheat, barley, and rye. It primarily affects the small intestine and is often referred to as a "silent epidemic," as many affected individuals remain undiagnosed. This disease causes inflammation of the duodenal mucosa, gradually leading to villous atrophy, crypt hyperplasia, and intraepithelial lymphocyte infiltration. These structural changes impair nutrient absorption, causing symptoms such as chronic diarrhea, weight loss, nutritional deficiencies, and persistent fatigue [6].

Diagnosis involves a combination of methods, including serological tests, histological analysis of duodenal biopsies, and clinical assessment of the response to a gluten-free diet [2, 3, 1, 4]:

* **Serological tests:** Detection of anti-tissue transglutaminase IgA (tTG-IgA) antibodies is essential. A level greater than 10 times the normal value has a positive predictive value above 95%. Total IgA is also measured to exclude IgA deficiency.

* **Endoscopy and duodenal biopsy:** In the case of positive serology, an upper digestive endoscopy with duodenal biopsies is performed to observe characteristic histological anomalies.

* **Histological scores:** Marsh scores are used to quantify mucosal lesions and assess the severity of villous atrophy.

* **Gluten-Free diet:** Clinical and histological improvement following a strict gluten-free diet generally confirms the diagnosis.
While endoscopy is an essential tool for visualizing the intestinal mucosa, the interpretation of anomalies such as villous atrophy relies heavily on the clinician's visual assessment. This subjective evaluation can lead to inter-observer variability and potential misdiagnosis. Moreover, the average diagnostic delay of up to 11 years highlights the need for more reliable and efficient tools [5].

In this context, Artificial Intelligence (AI), and particularly Computer-Aided Detection (CAD) systems, are being explored to assist in the automatic analysis of endoscopic images. These systems, based on machine learning and deep learning techniques, are capable of extracting subtle features from video frames and identifying pathological patterns with greater consistency and objectivity than human observers.

# 2 Endoscopic image dataset collection and preparation

To train and evaluate machine learning models for the detection of duodenal anomalies related to celiac disease, we initially searched for publicly available datasets of duodenal endoscopic images. However, existing resources were limited in both quantity and quality, with only a small number of relevant images or short video segments, rendering them insufficient for robust model development.

To address this gap, we established a clinical collaboration with Dr. F. Gaouar, Hepato-Gastroenterologist at the Mimosa Private Clinic, Tlemcen (Algeria), who provided access to endoscopic images collected during routine upper gastrointestinal examinations. This collaboration enabled the acquisition of real-world, clinically validated duodenal images.

All images were reviewed under expert supervision to ensure their diagnostic relevance. The collected dataset was curated to include representative cases across a spectrum of mucosal presentations, facilitating the construction of meaningful classification tasks.

## 2.1 Dataset structuring and annotation

Following acquisition, the images were categorized based on their visual characteristics and diagnostic interpretation, with a focus on features associated with celiac disease (e.g., villous atrophy, scalloping, mosaic patterns). The dataset was organized into three primary categories:

<table><caption>Table 1: Collected database categories</caption><thead><tr><td>Category</td><td>Number of Images</td></tr></thead><tbody><tr><td>Normal</td><td>74</td></tr><tr><td>Celiac</td><td>63</td></tr><tr><td>Doubtful</td><td>23</td></tr><tr><td>Total</td><td>192</td></tr></tbody></table>

To ensure data quality and consistency, we documented the technical specifications of the endoscopy systems used during image acquisition. This information is critical for assessing intra-dataset variability due to differences in resolution and imaging technology:
<table><caption>Table 2: Image and endoscope specifications</caption><thead><tr><td>Endoscope Used</td><td>Resolution</td><td>Image Size (pixels)</td></tr></thead><tbody><tr><td>Fujifilm EG-600WR HD</td><td>HD</td><td>1280 x 1024</td></tr><tr><td>Pentax EG-2990k HD</td><td>HD</td><td>1296 x 990</td></tr><tr><td>Fujinon EPX-4400 SD</td><td>SD</td><td>720 x 576</td></tr></tbody></table>

This level of technical detail supports downstream analysis by enabling preprocessing strategies tailored to image resolution and source variability. Overall, the resulting dataset provides a well-annotated and clinically grounded resource for the development and benchmarking of computer vision models targeting the automated detection of celiac disease-related mucosal anomalies.

<figure><img src="image_2.png" /><figcaption>Figure 1: Example of endoscopic image of celiac disease</figcaption></figure>
<figure><img src="image_3.png" /><figcaption>Figure 2: Example of normal image</figcaption></figure>

<figure><img src="image_4.png" /><figcaption>Figure 3: Example of doubtful image”</figcaption></figure>
# 3 Challenges and potential of the dataset

Despite its relatively modest size, this dataset presents several distinctive features and opportunities:

* **Real-world clinical variability:** The images were acquired in actual diagnostic conditions, encompassing natural variability in lighting, angulation, and mucosal presentation—offering a realistic challenge for model generalization.

* **Expert-validated annotations:** Diagnostic categories were assigned under the supervision of an experienced gastroenterologist, enhancing the clinical relevance of the labels.

* **Multi-resolution data:** The inclusion of images from multiple endoscopy systems with varying resolutions encourages the development of robust preprocessing and normalization strategies.

* **Limited data regime:** The relatively small sample size provides a suitable benchmark for evaluating lightweight architectures, transfer learning techniques, and data augmentation strategies under constrained data conditions.

* **Clinical applicability:** By focusing on early and subtle mucosal signs of celiac disease, this dataset contributes to the development of decision support tools aimed at reducing diagnostic delays and inter-observer variability in endoscopic practice.

This curated dataset thus represents a valuable resource for the medical AI community, particularly for researchers working on image-based diagnosis in low-data or heterogeneous clinical environments.

# 4 Conclusion

We present a clinically annotated dataset of duodenal endoscopic images specifically curated for the investigation of celiac disease, with a focus on mucosal anomalies such as villous atrophy. While most existing studies emphasize algorithmic performance, our contribution addresses a critical upstream need: the availability of high-quality, structured, and diagnostically relevant data for training and validating computer-aided diagnostic systems.

This dataset offers a valuable foundation for the development of machine learning models in the domain of gastrointestinal endoscopy. By providing diverse image samples, expert annotations, and metadata regarding imaging equipment, it facilitates reproducible and clinically meaningful research. Furthermore, it opens new avenues for studying diagnostic variability, testing AI-driven feature extraction methods, and improving the early detection and classification of celiac disease in real-world clinical settings.

Ultimately, this resource aims to bridge the gap between clinical expertise and computational innovation, supporting future efforts to enhance the accuracy, objectivity, and accessibility of gastrointestinal diagnostics through artificial intelligence.
References

[1] Maladie Coeliaque. *Comment est diagnostiquée la maladie cœliaque ?* n.d. URL: https://maladie-coeliaque.com/comment-est-diagnostiquee-lamaladie-coeliaque/.

[2] S. Husby and all. "European Society for Paediatric Gastroenterology, Hepatology and Nutrition Guidelines for Diagnosing Coeliac Disease 2020". In: *J Pediatr Gastroenterol Nutr* 70.1 (2020), pp. 141-156. DOI: 10.1097/MPG.0000000000002526.

[3] Johns Hopkins Medicine. *Upper GI Endoscopy*. n.d. URL: https://www.hopkinsmedicine.org/health/treatment-tests-and-therapies/upper-gi-endoscopy.

[4] Ramsay Santé. *Echoendoscopie digestive*. n.d. URL: https://www.ramsaysante.fr/vous-etes-patient-ensavoir-plus-sur-ma-pathologie/echoendoscopie-digestive.

[5] Markus W. Scheppach and all. "Detection of duodenal villous atrophy on endoscopic images using a deep learning algorithm". In: *Gastrointestinal Endoscopy* 97.5 (2023), pp. 911-916. DOI: 10.1016/j.gie.2022.11.029.

[6] Cristian-Andrei Stoleru, Elena-Hortensia Dediu, and Cristina Lupu. "Automated detection of celiac disease using Machine Learning Algorithms". In: *Scientific Reports* 12 (2022). DOI: 10.1038/s41598-022-07422-w.