Early diagnosis of vascular Ehlers-Danlos syndrome through AI-powered facial analysis: Results from the Montalcino Aortic Consortium.

Purpose: Vascular Ehlers-Danlos syndrome (vEDS), which is caused by Methods: We implemented an artificial intelligence (AI) facial analysis model based on the PhenoScore framework using a support vector machine trained on facial images of 30 individuals, aged 6 to 65 years, with vEDS from the Montalcino Aortic Consortium, control images from the Chicago Face Database, and publicly available images of individuals with hEDS. Cross-validation was used to train the support vector machine, and statistical measures to evaluate the model performance were calculated. Local Interpretable Model-agnostic Explanations was used to generate facial heatmaps highlighting the features driving the model's predictions. Results: The AI classifier showed excellent performance with as few as 13 vEDS training images and distinguished vEDS from both controls and individuals with hEDS with high accuracy, achieving an area under the receiver operating characteristic curve ≥ 0.97. Local Interpretable Model-agnostic Explanations highlighted facial regions already established to characterize the facial features of vEDS patients (eg, prominent eyes). Conclusion: Our results demonstrate the potential of AI-based facial analysis for diagnosing vEDS. This method democratizes the early diagnosis of vEDS by reducing dependence on genetic testing, enabling optimal management and improved outcomes, particularly in resource-limited areas.