Academic Journal
Development and Clinical Validation of an Artificial Intelligence-Based Automated Visual Acuity Testing System.
| Τίτλος: | Development and Clinical Validation of an Artificial Intelligence-Based Automated Visual Acuity Testing System. |
|---|---|
| Συγγραφείς: | Li, Kelvin Zhenghao, Oo, Hnin Hnin, Liang, Kenneth Chee Wei, Ismail, Najah, Chua, Jasmine Ling Ling, Chng, Jackson Jie Sheng, Wu, Yang, Wong, Daryl Wei Ren, Khan, Sumaya Rani, Yap, Boon Peng, Tong, Rong, Kiew, Choon Meng, Huang, Yufei, Chua, Chun Hau, Lim, Alva Khai Shin, Fan, Xiuyi |
| Πηγή: | Life (2075-1729); Feb2026, Vol. 16 Issue 2, p357, 13p |
| Θεματικοί όροι: | Visual acuity, Artificial intelligence, Automatic speech recognition, Test systems, Health outcome assessment, Image recognition (Computer vision) |
| Περίληψη: | Background: To develop and validate an automated visual acuity (VA) testing system integrating artificial intelligence (AI)–driven speech and image recognition technologies, enabling self-administered, clinic-based VA assessment; Methods: The system incorporated a fine-tuned Whisper speech-recognition model with Silero voice activity detection and pose estimation through facial landmark and ArUco marker detection. A state-driven interface guided users through sequential testing with and without a pinhole. Speech recognition was enhanced using a local Singaporean English dataset. Laboratory validation assessed speech and pose recognition performance, while clinical validation compared automated and manual VA testing at a tertiary eye clinic; Results: The fine-tuned model reduced word error rates from 17.83% to 9.81% for letters and 2.76% to 1.97% for numbers. Pose detection accurately identified valid occluder states. Among 72 participants (144 eyes), automated unaided VA showed good agreement with manual VA (ICC = 0.77, 95% CI 0.62–0.85), while pinhole VA demonstrated moderate agreement (ICC = 0.63, 95% CI 0.25–0.83). Automated testing took longer (132.1 ± 47.5 s vs. 97.1 ± 47.8 s; p < 0.001), but user experience remained positive (mean Likert scale score 4.3 ± 0.8); Conclusions: The AI-based automated VA system delivered accurate, reliable, and user-friendly performance, supporting its feasibility for clinical implementation. [ABSTRACT FROM AUTHOR] |
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| Βάση Δεδομένων: | Complementary Index |
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