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Artificial Intelligence-Assisted Prescription Determination for Orthokeratology Lens Fitting: From Algorithm to
Wei-Zhong Lan1, He Tang, Long-Bo Wen
1Guangzhou Aier Eye Hospital (W.-Z.L.), Jinan University, Guanghzou, China; School of Stomatology and Ophthalmology (W.-Z.L., X.L., Z.Y.), Xianning Medical College, Hubei University of Science and Technology, Xianing, China; SoC Design Center (H.T.), University of Electronic Science and Technology of China, Chengdu, China; School of Electronic Science and Engineering (H.T.), University of Electronic Science and Technology of China, Chengdu, China; Aier School of Ophthalmology (L.-B.W., Z.C., Y.Z., Z.Y.), Central South University, Changsha, China; Information Center (W.D., M.W., F.T.), Aier Eye Hospital Group, Changsha, China; School of Information and Software Engineering (W.-J.W.), University of Electronic Science and Technology of China, Chengdu, China; School of Computer Science and Engineering (Y.T.), University of Electronic Science and Technology of China, Chengdu, China; Hunan Province Optometry Engineering and Technology Research Center (W.-Z.L., L.-B.W., Z.C., X.L., Z.Y.), Changsha, China; and Hunan Province International Cooperation Base for Optometry Science and Technology (W.-Z.L., L.-B.W., Z.C., X.L., Z.Y.), Changsha, China.
Objectives:
To explore the potential of artificial intelligence (AI) to assist prescription determination for orthokeratology (OK) lenses.
Methods:
Artificial intelligence algorithm development followed by a real-world trial. A total of 11,502 OK lenses fitting records collected from seven clinical environments covering major brands. Records were randomly divided in a three-way data split. Cross-validation was used to identify the most accurate algorithm, followed by an evaluation using an independent test data set. An online AI-assisted system was implemented and assessed in a real-world trial involving four junior and three senior clinicians.
Results:
The primary outcome measure was the algorithm's accuracy (ACC). The ACC of the best performance of algorithms to predict the targeted reduction amplitude, lens diameter, and alignment curve of the prescription was 0.80, 0.82, and 0.83, respectively. With the assistance of the AI system, the number of trials required to determine the final prescription significantly decreased for six of the seven participating clinicians (all P <0.01). This reduction was more significant among junior clinicians compared with consultants (0.76±0.60 vs. 0.32±0.60, P <0.001). Junior clinicians achieved clinical outcomes comparable to their seniors, as 93.96% (140/149) and 94.44% (119/126), respectively, of the eyes fitted achieved unaided visual acuity no worse than 0.8 ( P =0.864).
Conclusions:
AI can improve prescription efficiency and reduce discrepancies in clinical outcomes among clinicians with differing levels of experience. Embedment of AI in practice should ultimately help lessen the medical burden and improve service quality for myopia boom emerging worldwide.
