Ai For Diagnosis Pathological Adenoid Hypertrophy Based On Face Images

A novel AI system, based on facial imaging for the diagnosis of pathological adenoid hypertrophy (AH) is introduced. The system is based on deep learning of face images for AI evaluation of pathological AH. The system utilizes internal and external databases, with 2,370 facial images from 790 patients (95 with pathological AH and 395 healthy controls), and 540 facial images from 180 patients (90 with pathological AH and 90 healthy controls). Pathological AH diagnosis was performed using an AI system based on multiple facial images, the results were compared with those from machine learning based approaches, including decision trees, K-nearest neighbors (KNN) and support vector machines (SVM), as well as with diagnoses from orthodontic experts. The AUC values for the internal and external validation sets were 0.891 and 0.797, respectively, with ACs of 0.805 and 0.726 and APs of 0.905 and 0.836. Compared to the machine learning methods including decision trees, KNN and SVM, the internal validation set showed improvements in AC, AUC and AP by 45.6%, 42.3% and 38.4%, while the external validation set showed improvements of 34.4%, 34.6% and 30.2%, respectively. Additionally, compared to human experts, the system improved the average AC by 30.6% and the harmonic mean of precision and recall, as reflected by the F1 score which was improved by 36.2%. This study proposes a fully automatic clinically to use deep learning system for pathological AH diagnosis from face images.