Ophthalmology science · 2025
OCT-PRO: A Multimodal Model Integrating OCT and Clinical Traits to Predict Postoperative Outcomes in Cataract Patients.
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Liu Lixue, Li Mingyuan, Wu Yuxuan, Cao Zizheng, Shang Yuanjun, Zhao Lanqin, Wang Zhenyu, Tan Junwei, Yuan Yan, Huang Wenbin, Wang Jinghui, Li Jianqiao, Xu Fabao, Lian Zhangkai, Pang Jianyu, Xu Fan, Tang Ningning, He Xingru, Xu Yan, Zeng Kun, Luo Lishi, Wang Mingwei, Xu Ruiwen, Tu Zhenjun, Chen Xi, Chen Hui, Liu Zhenzhen, Tao Jing, Wu Xiaohang, Lin Haotian
Аннотация
PURPOSE: To develop and validate OCT-PRO, a multimodal machine learning model integrating OCT images and clinical traits to predict postoperative visual outcomes in cataract patients.
DESIGN: Multicenter prospective cohort study.
PARTICIPANTS: A total of 2225 eyes from 1911 cataract patients were enrolled, including 1304 participants from Zhongshan Ophthalmic Center for model development and 607 from 6 hospitals across China for external testing.
METHODS: All participants underwent standardized preoperative examinations including macular OCT and clinical data collection, followed by phacoemulsification and intraocular lens implantation. Postoperative best-corrected visual acuity (BCVA) was assessed at 4 weeks after surgery. A multimodal model was constructed using deep learning techniques, combining image features extracted via InceptionResNetV2 and structured metadata processed by fully connected layers. Model performance was assessed using mean absolute error (MAE) and root mean square error (RMSE) and compared with traditional laser interferometry and ophthalmologist predictions. Subgroup analysis and explainability assessments were conducted to evaluate generalizability and model attention.
MAIN OUTCOME MEASURES: Prediction error of postoperative BCVA (logarithm of the minimum angle of resolution [logMAR]) measured by MAE and RMSE.
RESULTS: In the internal test data set, OCT-PRO achieved improved performance, with lower MAE and RMSE (0.128 and 0.211 logMAR) compared with the OCT-only model (0.138 and 0.226 logMAR), metadata-only model (0.161 and 0.234 logMAR) and laser interferometry (0.381 and 0.554 logMAR). In the external test data set, OCT-PRO achieved an MAE of 0.168 logMAR, significantly outperforming the OCT-only (0.183 logMAR, P = 0.003) and metadata-only models (0.229 logMAR, P < 0.001). Subgroup analyses confirmed consistent advantages of OCT-PRO across different cataract subtypes and baseline preoperative BCVA groups. Model interpretability analysis highlighted the importance of preoperative BCVA, age, and macular foveal structure, with greater reliance on OCT features than clinical metadata-especially in complex or low preoperative BCVA cases. In a head-to-head comparison, the model consistently outperformed both junior and senior ophthalmologists in predictive accuracy across various clinical subtypes.
CONCLUSIONS: OCT-PRO enables accurate prediction of postoperative visual outcomes in cataract surgery, outperforming conventional methods and ophthalmologists. It holds promise as a valuable decision-support tool to assist surgical decision-making and improve health care resource allocation.
FINANCIAL DISCLOSURES: The author has no/the authors have no proprietary or commercial interest in any materials discussed in this article.
DESIGN: Multicenter prospective cohort study.
PARTICIPANTS: A total of 2225 eyes from 1911 cataract patients were enrolled, including 1304 participants from Zhongshan Ophthalmic Center for model development and 607 from 6 hospitals across China for external testing.
METHODS: All participants underwent standardized preoperative examinations including macular OCT and clinical data collection, followed by phacoemulsification and intraocular lens implantation. Postoperative best-corrected visual acuity (BCVA) was assessed at 4 weeks after surgery. A multimodal model was constructed using deep learning techniques, combining image features extracted via InceptionResNetV2 and structured metadata processed by fully connected layers. Model performance was assessed using mean absolute error (MAE) and root mean square error (RMSE) and compared with traditional laser interferometry and ophthalmologist predictions. Subgroup analysis and explainability assessments were conducted to evaluate generalizability and model attention.
MAIN OUTCOME MEASURES: Prediction error of postoperative BCVA (logarithm of the minimum angle of resolution [logMAR]) measured by MAE and RMSE.
RESULTS: In the internal test data set, OCT-PRO achieved improved performance, with lower MAE and RMSE (0.128 and 0.211 logMAR) compared with the OCT-only model (0.138 and 0.226 logMAR), metadata-only model (0.161 and 0.234 logMAR) and laser interferometry (0.381 and 0.554 logMAR). In the external test data set, OCT-PRO achieved an MAE of 0.168 logMAR, significantly outperforming the OCT-only (0.183 logMAR, P = 0.003) and metadata-only models (0.229 logMAR, P < 0.001). Subgroup analyses confirmed consistent advantages of OCT-PRO across different cataract subtypes and baseline preoperative BCVA groups. Model interpretability analysis highlighted the importance of preoperative BCVA, age, and macular foveal structure, with greater reliance on OCT features than clinical metadata-especially in complex or low preoperative BCVA cases. In a head-to-head comparison, the model consistently outperformed both junior and senior ophthalmologists in predictive accuracy across various clinical subtypes.
CONCLUSIONS: OCT-PRO enables accurate prediction of postoperative visual outcomes in cataract surgery, outperforming conventional methods and ophthalmologists. It holds promise as a valuable decision-support tool to assist surgical decision-making and improve health care resource allocation.
FINANCIAL DISCLOSURES: The author has no/the authors have no proprietary or commercial interest in any materials discussed in this article.