ProptoView: AI-based digital exophthalmometry using multi-view facial images in a multinational validation study.
Authors
Affiliations (16)
Affiliations (16)
- State Key Laboratory of Eye Health, Department of Ophthalmology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200011, China.
- Hainan International Medical Center, Shanghai Jiao Tong University School of Medicine, Hainan, China.
- VoxelCloud, Inc., Shanghai, China.
- School of AI and Advanced Computing, Xi'an Jiaotong-Liverpool University, Suzhou, 215123, China.
- Shanghai Jiao Tong University School of Medicine, Shanghai, China.
- Department of Ophthalmology & Visual Sciences, Khoo Teck Puat Hospital, Singapore, Singapore.
- Department of Ophthalmology, National University Hospital, Singapore, Singapore.
- Department of Ophthalmology, Mettapracharak (Wat Rai Khing) Hospital, Nakhon Pathom, Thailand.
- Department of Ophthalmology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China.
- Department of Ophthalmology, The Second Hospital of Dalian Medical University, Dalian, China.
- Department of Ophthalmology, The Second Affiliated Hospital of Chengdu Medical College, Chengdu, China.
- Roski Eye Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA.
- Department of Electronic Engineering, Shanghai Jiao Tong University, Shanghai, China.
- School of AI and Advanced Computing, Xi'an Jiaotong-Liverpool University, Suzhou, 215123, China. [email protected].
- State Key Laboratory of Eye Health, Department of Ophthalmology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200011, China. [email protected].
- Hainan International Medical Center, Shanghai Jiao Tong University School of Medicine, Hainan, China. [email protected].
Abstract
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.