Details

Autor(en) / Beteiligte

• Hennocq, Quentin
• Bongibault, Thomas
• Marlin, Sandrine
• Amiel, Jeanne
• Attie-Bitach, Tania
• Baujat, Geneviève
• Boutaud, Lucile
• Carpentier, Georges
• Corre, Pierre
• Denoyelle, Françoise
• Djate Delbrah, François
• Douillet, Maxime
• Galliani, Eva
• Kamolvisit, Wuttichart
• Lyonnet, Stanislas
• Milea, Dan
• Pingault, Véronique
• Porntaveetus, Thantrira
• Touzet-Roumazeille, Sandrine
• Willems, Marjolaine
• Picard, Arnaud
• Rio, Marlène
• Garcelon, Nicolas
• Khonsari, Roman H.

Titel

AI-based diagnosis in mandibulofacial dysostosis with microcephaly using external ear shapes

Ist Teil von

• Frontiers in pediatrics, 2023-08, Vol.11, p.1171277-1171277

Ort / Verlag

Frontiers

Erscheinungsjahr

2023

Link zum Volltext

Quelle

EZB Free E-Journals

Beschreibungen/Notizen

• Introduction Mandibulo-Facial Dysostosis with Microcephaly (MFDM) is a rare disease with a broad spectrum of symptoms, characterized by zygomatic and mandibular hypoplasia, microcephaly, and ear abnormalities. Here, we aimed at describing the external ear phenotype of MFDM patients, and train an Artificial Intelligence (AI)-based model to differentiate MFDM ears from non-syndromic control ears (binary classification), and from ears of the main differential diagnoses of this condition (multi-class classification): Treacher Collins (TC), Nager (NAFD) and CHARGE syndromes. Methods The training set contained 1,592 ear photographs, corresponding to 550 patients. We extracted 48 patients completely independent of the training set, with only one photograph per ear per patient. After a CNN-(Convolutional Neural Network) based ear detection, the images were automatically landmarked. Generalized Procrustes Analysis was then performed, along with a dimension reduction using PCA (Principal Component Analysis). The principal components were used as inputs in an eXtreme Gradient Boosting (XGBoost) model, optimized using a 5-fold cross-validation. Finally, the model was tested on an independent validation set. Results We trained the model on 1,592 ear photographs, corresponding to 1,296 control ears, 105 MFDM, 33 NAFD, 70 TC and 88 CHARGE syndrome ears. The model detected MFDM with an accuracy of 0.969 [0.838–0.999] ( p < 0.001) and an AUC (Area Under the Curve) of 0.975 within controls (binary classification). Balanced accuracies were 0.811 [0.648–0.920] ( p = 0.002) in a first multiclass design (MFDM vs. controls and differential diagnoses) and 0.813 [0.544–0.960] ( p = 0.003) in a second multiclass design (MFDM vs. differential diagnoses). Conclusion This is the first AI-based syndrome detection model in dysmorphology based on the external ear, opening promising clinical applications both for local care and referral, and for expert centers.