ProptoView: AI-based digital exophthalmometry using multi-view facial images in a multinational validation study

Accurate proptosis measurement is vital for managing thyroid eye disease (TED) and other orbital conditions. However, current approaches have certain limitations: the Hertel exophthalmometer is convenient but imprecise, while computed tomography (CT) is accurate but costly and exposes patients to radiation.

We developed ProptoView, an AI-based digital exophthalmometer, using 5676 images from 2516 eyes across 1258 visits of 763 TED patients with CT and Hertel measurements. For external validation, we used an additional 644 images from 648 eyes of 324 patients with TED and other orbital diseases, collected across three countries and five hospitals. Patients provided up to five images from four views. A three-stage deep learning approach, optimized with Adam and validated via five-fold cross-validation, helped develop three AI models: single-view, multi-view, and dynamic input.

Compared with CT, the single-view model achieved an intraclass correlation coefficient (ICC) of 0.859, slightly lower than the Hertel exophthalmometer's ICC of 0.888. The multi-view model achieved an ICC of 0.890, surpassing the Hertel exophthalmometer (0.871). The dynamic input model achieved the highest accuracy with an ICC of 0.901. Among two-view combinations, pairing the frontal view with another angle showed the highest agreement when paired with the upward gaze view (ICC = 0.855). In external validation, ProptoView showed robust concordance with the Hertel exophthalmometer (ICC = 0.845), comparable to its agreement in the development dataset. Additionally, ProptoView reduced misclassification at the 19-mm threshold (14.7% vs. 20.5% with the Hertel exophthalmometer).

ProptoView provides an accurate, non-contact, and cost-effective solution for proptosis measurement. Its flexibility and precision suggest significant potential for streamlining clinical workflows and enabling telemedicine applications.