A Multimodal Multitask Artificial Intelligence Model for Orthokeratology Contact Lens Fitting: An Integrated Framework to Enhance Lens Centration and Myopia Control Effect.

Purpose: To develop a multimodal, multitask artificial intelligence (AI) model to classify postfitting corneal topography patterns and predict axial length (AL) growth, while simultaneously outputting optimal orthokeratology (ortho-K) lens parameters and the predicted probability of axial growth to support clinical decisions. Design: A retrospective analysis. Subjects: Clinical data and corneal topography maps from 3529 myopic eyes fitted with ortho-K lenses (Euclid or Alpha designs) from 2018 to 2023 were collected. Methods: A novel AI model was built comprising (1) a ResNet50-based "recall" model to predict key lens parameters and (2) a multimodal multitask "ranking" model (comparing ResNet50, ViT-B/16, and CLIP-ViT architectures) to classify postlens corneal topography and predict axial elongation. Postlens topography pattern was classified by centration and plus power ring pattern. Annual axial elongation rate ≥0.3 mm/y is defined as fast myopia progression. The recall model output gave a list of optimal lens parameter candidates, and the ranking model output the likely topography classification and the probability of AL growth for each candidate at 1 year. Main Outcome Measures: Mean squared error, recall rate, and accuracy were measured to evaluate model performance. Results: Of the 3529 ortho-K fits, 2643 eyes (74.89%) wore Euclid lenses and 886 (25.11%) wore Alpha lenses. All subjects wore their ortho-K lenses over the study period without serious complications. The average spherical equivalent refraction was -2.91 ± 1.14 diopters, and mean lens wear period was 11 months (range 9-13 months). Uncorrected visual acuity ≥0.8 was achieved in 96.43% of eyes at 1 month. The overall annual axial elongation rate observed was 0.24 ± 0.20 mm/y. Class 1 axial growth (<0.3 mm/y) in 62.79% of eyes, and class 1 postlens topography was achieved in 80.0% of eyes at 1-year follow-up. All 3 ranking models performed comparably: topography classification accuracies were 0.95, 0.96, and 0.96 for ResNet50, ViT, and CLIP-ViT models, respectively; while axial growth prediction accuracies were 0.880, 0.882, and 0.883, respectively. Conclusions: The proposed multimodal multitask AI model performed well in classifying corneal topography patterns, predicting AL growth rate, and recommending lens parameters, offering valuable decision support in ortho-K lens fitting. Financial Disclosures: Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.