Dental Enumeration and Diagnosis on Panoramic X-rays Challenge
Creators: name: Hamamci, Ibrahim Ethem
name: Mehl, Albert Christof
name: Stadlinger, Bernd
name: Bjoern Menze
name: Er, Sezgin
name: Simsar, Enis
name: Serife Damla Ozdemir
name: Gundogar, Mustafa
name: Yuksel, Atif Emre
name: Gultekin, Sadullah
Publisher: name: Zenodo
Year: 2023
Subjects: subject: Panoramic X-ray
subject: Dental Enumeration
subject: Diagnostic Detection
subject: Semi-Supervised Learning
subject: Object Detection
subject: Dentistry
subject: FOS: Clinical medicine
subject: MICCAI Challenges

description: panoramic X-ray is a two-dimensional (2-D) dental X-ray examination that captures the entire mouth in a single image, including the teeth, upper and lower jaws, surrounding structures, and tissues. It is the most commonly performed imaging modality by dentists and oral surgeons. Dental practitioners deal with radiology in their dayto-day practice by taking panoramic X-rays of the oral cavity before, during, and after treatments. This yields them with an essential diagnostic aid that provides evidence of pathologies that are not always seen with the naked eye such as dental caries, periapical lesions, or odontogenic cysts [1], [2]. Although dental X-ray is widespread and very useful in clinical dental practice, the multiple superimpositions and distortions depicted by numerous anatomical structures cause perceptual errors which account for most misdiagnosed cases [3], [4]. Also, common issues like miscommunication and exhaustion of overworking dentists may lead to wrong tooth treatment [5]. Therefore, misinterpretation during dental radiographic analysis is inevitable. However, it can be overcome if AI is utilized during diagnosis and treatment planning. For example, an AI system that could automatically number the dentition and point out the teeth in need of treatment could decrease the likelihood of executing the wrong therapy or even treating the wrong tooth. Thus, we highly believe that pairing an AI mechanism with dental radiology would help dental practitioners overcome these problems in the clinical setting as well as assist them in carrying out procedures in a more precise manner or with a low margin of error. In accordance with this, there has been increasing interest in the use of AI in dentistry as it has been featured in an increasing number of dental AI publications in recent years specifically in panoramic dental X-rays [6], [7], [8], [9]. Therefore, to promote the use of AI in dentistry, we propose a Dental Enumeration and Diagnosis on Panoramic X-rays Challenge (DENTEX). For the DENTEX challenge, we provide a new labeled dataset and a new unlabeled dataset. For the labeled dataset, we have 3 types of annotations which are quadrant segmentation, tooth detection with dental enumeration (FDI notation), and abnormal tooth detection with corresponding diagnosis and enumeration. Before understanding why we also provide quadrant segmentation dataset, we need to understand Federation Dentaire Internationale (FDI) Numbering System and how quadrant segmentation may help us number the dentition. The FDI system is a globally-used system that assigns each quadrant of the mouth a number 1 through 4. Then each of the eight teeth and each molar are numbers 1 through 8. So for example, the back tooth on the lower left side would be #48 according to FDI notation, which means quadrant #4, tooth #8 [10]. Therefore, providing a quadrant segmentation dataset may ease the dental enumeration task according to FDI notation. To be more specific, we provide 1571 unlabeled X-ray images in the pretraining set, 693 X-ray images with quadrant segmentation labels (which help us number the dentition), 634 X-ray images with tooth detection with enumeration labels, 705 X-ray images with abnormal tooth detection with diagnostic labels and their corresponding enumeration in the training set, 100 visible X-ray images for validation, 300 hidden X-ray images for testing. In the diagnosis detection labels, there are four specific classes corresponding to four different diagnoses: caries, deep caries, periapical lesions, and impacted teeth. In the overall challenge, we focus on the accuracy of abnormal tooth detection with dental enumeration and corresponding diagnoses. References:<br> [1 J. W. Choi, “Assessment of panoramic radiography as a national oral examination tool: Review of the literature,” Imaging Sci. Dent., 2011, doi: 10.5624/isd.2011.41.1.1.<br> [2] H. H. I. Kweon, J. H. Lee, T. mi Youk, B. A. Lee, and Y. T. Kim, “Panoramic radiography can be an effective diagnostic tool adjunctive to oral examinations in the national health checkup program,” J. Periodontal Implant Sci., 2018, doi: 10.5051/jpis.2018.48.5.317.<br> [3] M. A. Bruno, E. A. Walker, and H. H. Abujudeh, “Understanding and Confronting Our Mistakes: The Epidemiology of Error in Radiology and Strategies for Error Reduction,” RadioGraphics, vol. 35, no. 6, pp. 1668–1676, Oct. 2015, doi: 10.1148/rg.2015150023.<br> [4] S. Perschbacher, “Interpretation of panoramic radiographs,” Aust. Dent. J., vol. 57, no. s1, pp. 40–45, Mar. 2012, doi: https://doi.org/10.1111/j.1834-7819.2011.01655.x.<br> [5] A. M. Jan, R. Albenayan, D. Alsharkawi, and F. M. Jadu, “The prevalence and causes of wrong tooth extraction,” Niger. J. Clin. Pract., 2019, doi: 10.4103/njcp.njcp_206_19.<br> [6] A. E. Yüksel et al., “Dental enumeration and multiple treatment detection on panoramic X-rays using deep learning,” Sci. Rep., 2021, doi: 10.1038/s41598-021-90386-1.<br> [7] Zadrony et al., “Artificial Intelligence Application in Assessment of Panoramic Radiographs,” Diagnostics, 2022, doi: 10.3390/diagnostics12010224.<br> [8] A. F. Leite et al., “Artificial intelligence-driven novel tool for tooth detection and segmentation on panoramic radiographs,” Clin. Oral Investig., 2021, doi: 10.1007/s00784-020-03544-6.<br> [9] S. Lee, D. Kim, and H.-G. Jeong, “Detecting 17 fine-grained dental anomalies from panoramic dental radiography using artificial intelligence,” Sci. Rep., vol. 12, no. 1, p. 5172, 2022, doi: 10.1038/s41598-022-09083-2.<br> [10] International Dental Federation, “Designation system for teeth and areas of the oral cavity,” ISO (the International Organization for Standardization), 2009. .<br> [11] D. P. Kingma et al., “labelme: Image Polygonal Annotation with Python,” Chaos, Solitons &amp; Fractals, 2020.<br> [12] B. Wittmann, F. Navarro, S. Shit, and B. Menze, “Focused Decoding Enables 3D Anatomical Detection by Transformers.” arXiv, 2022, doi: 10.48550/ARXIV.2207.10774.<br> [13] S. Chen, P. Sun, Y. Song, and P. Luo, “DiffusionDet: Diffusion Model for Object Detection.” arXiv, 2022, doi:<br> 10.48550/ARXIV.2211.09788.<br> [14] T.-Y. Lin et al., “Microsoft COCO: Common Objects in Context.” arXiv, 2014, doi: 10.48550/ARXIV.1405.0312.<br> [15] A. Sekuboyina et al., “VerSe: A Vertebrae labelling and segmentation benchmark for multi-detector CT images.,” Med. Image Anal., vol. 73, p. 102166, Oct. 2021, doi: 10.1016/j.media.2021.102166.<br> [16] B. H. Menze et al., “The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS),” IEEE Trans. Med. Imaging, 2015, doi: 10.1109/TMI.2014.2377694.<br> [17] M. Antonelli et al., “The Medical Segmentation Decathlon,” Nat. Commun., vol. 13, no. 1, Jul. 2022, doi: 10.1038/s41467-022-30695-9.<br> [18] L. Maier-Hein et al., “Why rankings of biomedical image analysis competitions should be interpreted with care,” Nat. Commun., 2018, doi: 10.1038/s41467-018-07619-7.