Next generation phenotyping for diagnosis and phenotype–genotype correlations in Kabuki syndrome

Abstract

The field of dysmorphology has been changed by the use Artificial Intelligence (AI) and the development of Next Generation Phenotyping (NGP). The aim of this study was to propose a new NGP model for predicting KS (Kabuki Syndrome) on 2D facial photographs and distinguish KS1 (KS type 1, KMT2D-related) from KS2 (KS type 2, KDM6A-related). We included retrospectively and prospectively, from 1998 to 2023, all frontal and lateral pictures of patients with a molecular confirmation of KS. After automatic preprocessing, we extracted geometric and textural features. After incorporation of age, gender, and ethnicity, we used XGboost (eXtreme Gradient Boosting), a supervised machine learning classifier. The model was tested on an independent validation set. Finally, we compared the performances of our model with DeepGestalt (Face2Gene). The study included 1448 frontal and lateral facial photographs from 6 centers, corresponding to 634 patients (527 controls, 107 KS); 82 (78%) of KS patients had a variation in the KMT2D gene (KS1) and 23 (22%) in the KDM6A gene (KS2). We were able to distinguish KS from controls in the independent validation group with an accuracy of 95.8% (78.9–99.9%, p < 0.001) and distinguish KS1 from KS2 with an empirical Area Under the Curve (AUC) of 0.805 (0.729–0.880, p < 0.001). We report an automatic detection model for KS with high performances (AUC 0.993 and accuracy 95.8%). We were able to distinguish patients with KS1 from KS2, with an AUC of 0.805. These results outperform the current commercial AI-based solutions and expert clinicians.

Humanitarian Facial Recognition for Rare Craniofacial Malformations

“…Following dimension reduction using principal component analysis and incorporation of metadata, the algorithm uses eXtreme Gradient Boosting, a supervised machine learning classifier. 6,7 The application of AIDY to this public photograph showed a very high probability for Apert syndrome and very low concordance with controls, based on a specific model taking both facial features and age into account in a cohort of 541 patients with syndromic craniosynostoses. 8 We thus supported the initial hypothesis based on expert opinions (Fig.…”

GestaltMatcher Database - A global reference for facial phenotypic variability in rare human diseases

“…So far, few studies exist about the performance of NGP tools where the ancestry composition of individuals in the training and test set differs. Literature suggests that AIs trained on individuals of European ancestry perform better on a test set of Asian rather than African ancestry 21 – 24 that may be explained by their closer genetic relatedness 25 . This raises the question of whether AIs need to be trained for different ancestries or whether a similar performance can be achieved by sufficiently increasing the ancestral diversity in the joint training set.…”