Abstract：
OBJECTIVE: 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.
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.
摘要：
目的：探索人工智能（AI）辅助角膜塑形术（OK）镜片处方确定的潜力。
方法：人工智能算法开发，然后进行真实世界试验。从七个临床环境中收集了11,502个OK镜头的试镜记录，涵盖了主要品牌。记录被随机分成三个方向的数据分割。交叉验证用于确定最准确的算法，然后使用独立的测试数据集进行评估。在一项涉及四名初级和三名高级临床医生的真实世界试验中，实施和评估了一个在线人工智能辅助系统。
结果：主要结果指标是算法的准确性（ACC）。ACC算法的最佳性能来预测目标降低幅度，透镜直径,处方的排列曲线分别为0.80、0.82和0.83。在AI系统的帮助下，7名参与临床医生中的6名患者确定最终处方所需的试验数量显著减少(均P<0.01).与顾问相比，这种减少在初级临床医生中更为显著(0.76±0.60vs.0.32±0.60,P<0.001)。初级临床医生取得了与老年人相当的临床结果，分别为93.96%(140/149)和94.44%(119/126)，分别,合眼的裸眼视力不低于0.8（P=0.864）。
结论：AI可以提高处方效率，减少具有不同经验水平的临床医生的临床结果差异。AI在实践中的嵌入最终将有助于减轻医疗负担并提高全球近视热潮的服务质量。
方法：人工智能算法开发，然后进行真实世界试验。从七个临床环境中收集了11,502个OK镜头的试镜记录，涵盖了主要品牌。记录被随机分成三个方向的数据分割。交叉验证用于确定最准确的算法，然后使用独立的测试数据集进行评估。在一项涉及四名初级和三名高级临床医生的真实世界试验中，实施和评估了一个在线人工智能辅助系统。
结果：主要结果指标是算法的准确性（ACC）。ACC算法的最佳性能来预测目标降低幅度，透镜直径,处方的排列曲线分别为0.80、0.82和0.83。在AI系统的帮助下，7名参与临床医生中的6名患者确定最终处方所需的试验数量显著减少(均P<0.01).与顾问相比，这种减少在初级临床医生中更为显著(0.76±0.60vs.0.32±0.60,P<0.001)。初级临床医生取得了与老年人相当的临床结果，分别为93.96%(140/149)和94.44%(119/126)，分别,合眼的裸眼视力不低于0.8（P=0.864）。
结论：AI可以提高处方效率，减少具有不同经验水平的临床医生的临床结果差异。AI在实践中的嵌入最终将有助于减轻医疗负担并提高全球近视热潮的服务质量。
