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

• Published in: Computers in biology and medicine 2024-09, Vol.180, p.108927, Article 108927
• Main Authors: Kadi, Hocine, Kawczynski, Marzena, Bendjama, Sara, Flores, Jesus Zegarra, Leong-Hoi, Audrey, de Lastic, Hugues, Balbierer, Julien, Mabileau, Claire, Radoux, Jean Pierre, Grollemund, Bruno, Jaegle, Jean, Guebert, Christophe, Bisch, Bertrand, Bloch-Zupan, Agnès
• Format: Article
• Language: English
• Subjects: Adolescent; Age; Algorithms; Artificial intelligence; Child; Datasets; Deep learning; Dental age assessment; Dental disorders; Dentists; Dentures; Diagnosis; Female; Genetic disorders; Humans; Image processing; Image segmentation; Male; Mask R–CNN; Medical diagnosis; Panoramic radiograph processing; Patients; Project management; Radiography, Panoramic; Rare diseases; Rare Diseases - diagnostic imaging; Rare Diseases - genetics; Rare genetic diseases; Teeth; Teeth segmentation; Transplants & implants; U-Net; X-rays
• ISSN: 0010-4825; 1879-0534; 1879-0534
• DOI: 10.1016/j.compbiomed.2024.108927

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 %. •Rare genetic diseases are difficult to diagnose leading to patient's diagnostic odyssey!.•The i-Dent project conceived a pre-diagnostic tool to detect rare diseases with tooth agenesis.•To identify missing teeth on X-rays, image segmentation models (Mask R–CNN, U-Net) have been trained.•Challenges were: small dataset, age-related dentition, and missing teeth pattern recognition.•Artificial Intelligence was successful and reliable for detecting rare genetic oral diseases.