Academic Journal
Axial and choroidal changes as key predictors of myopia control in red-light therapy: Evidence from machine learning models.
| Τίτλος: | Axial and choroidal changes as key predictors of myopia control in red-light therapy: Evidence from machine learning models. |
|---|---|
| Συγγραφείς: | Hu X; Wuhan Children's Hospital (Wuhan Maternal and Child Healthcare Hospital), Tongji Medical College, Huazhong University of Science & Technology, Wuhan, Hubei, China., Wang Z; Tianjin Key Laboratory of Retinal Functions and Diseases, Tianjin Branch of National Clinical Research Center for Ocular Disease, Eye Institute and School of Optometry, Tianjin Medical University Eye Hospital, Tianjin, China., Li R; Xiaogan Hospital Affiliated to Wuhan University of Science and Technology, Hubei, China., Sun B; Tianjin Key Laboratory of Retinal Functions and Diseases, Tianjin Branch of National Clinical Research Center for Ocular Disease, Eye Institute and School of Optometry, Tianjin Medical University Eye Hospital, Tianjin, China., Wang D; Wuhan Children's Hospital (Wuhan Maternal and Child Healthcare Hospital), Tongji Medical College, Huazhong University of Science & Technology, Wuhan, Hubei, China., Zong H; Wuhan Children's Hospital (Wuhan Maternal and Child Healthcare Hospital), Tongji Medical College, Huazhong University of Science & Technology, Wuhan, Hubei, China., Zhang X; Handan Eye Hospital (The Third Hospital of Handan), Hebei, China. Electronic address: zhangxiaoling793@tmu.edu.cn., Wei R; Tianjin Key Laboratory of Retinal Functions and Diseases, Tianjin Branch of National Clinical Research Center for Ocular Disease, Eye Institute and School of Optometry, Tianjin Medical University Eye Hospital, Tianjin, China. Electronic address: rwei@tmu.edu.cn., Jie H; Wuhan Children's Hospital (Wuhan Maternal and Child Healthcare Hospital), Tongji Medical College, Huazhong University of Science & Technology, Wuhan, Hubei, China. Electronic address: whetykjh@163.com. |
| Πηγή: | Photodiagnosis and photodynamic therapy [Photodiagnosis Photodyn Ther] 2026 Feb; Vol. 57, pp. 105342. Date of Electronic Publication: 2026 Jan 07. |
| Τύπος έκδοσης: | Journal Article; Multicenter Study |
| Γλώσσα: | English |
| Στοιχεία περιοδικού: | Publisher: Elsevier Country of Publication: Netherlands NLM ID: 101226123 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1873-1597 (Electronic) Linking ISSN: 15721000 NLM ISO Abbreviation: Photodiagnosis Photodyn Ther Subsets: MEDLINE |
| Imprint Name(s): | Original Publication: Amsterdam ; Boston : Elsevier, c2004- |
| Ιατρικοί όροι (MeSH): | Myopia*/radiotherapy , Choroid*/pathology , Red Light* , Machine Learning* , Axial Length, Eye*, Humans ; Predictive Learning Models ; Child ; Female ; Male ; Boosting Machine Learning Algorithms ; Adolescent ; Cohort Studies ; Sensitivity and Specificity |
| Περίληψη: | Background: Repeated low-level red-light (RLRL) therapy has emerged as a promising non-invasive intervention for myopia control. However, the predictive factors underlying its efficacy remain insufficiently explored. Methods: This multicenter cohort study included 538 pediatric patients who underwent RLRL treatment with a minimum follow-up of one year. Baseline ocular parameters and dynamic changes in axial length (AL) and choroidal thickness (ChT) over three months were collected. Multiple feature selection approaches-LASSO regression, Boruta, recursive elimination, and multivariate regression-were applied. Seven machine learning algorithms were trained, and their performance was evaluated using the area under the curve (AUC), sensitivity, specificity, and F1 score. SHAP and LIME analyses were utilized for interpretability. Results: Logistic regression and gradient boosting models demonstrated the highest discriminative ability. XGBoost achieved optimal performance (AUC: 0.90-0.92; accuracy: 87.7-88.2 %; F1 score: 0.72-0.86). Across one- and two-year prediction models, six stable predictors were identified: age, baseline AL, anterior chamber depth (ACD), RNFL thickness at the temporal inferior quadrant (TI), ChangeAL, and ChangeChT. SHAP analysis revealed that ChangeAL was the dominant short-term predictor, whereas ChangeChT was most influential for long-term outcomes. Older age and greater choroidal thickening were consistently associated with a protective effect against myopia progression. Conclusions: We developed and validated an interpretable machine learning model that accurately predicts short- and long-term outcomes of RLRL therapy in children. ChangeAL and ChangeChT serve as key dynamic biomarkers for treatment monitoring. These findings provide a foundation for personalized clinical decision-making in myopia management. (Copyright © 2026. Published by Elsevier B.V.) |
| Competing Interests: | Declaration of competing interest The authors have no competing interests to declare that are relevant to the content of this article. |
| Contributed Indexing: | Keywords: Axial length; Choroidal thickness; Low level light therapy; Machine learning; Myopia; Prediction model; Repeated low-level red light (RLRL) |
| Entry Date(s): | Date Created: 20260109 Date Completed: 20260202 Latest Revision: 20260626 |
| Update Code: | 20260626 |
| DOI: | 10.1016/j.pdpdt.2026.105342 |
| PMID: | 41513206 |
| Βάση Δεδομένων: | MEDLINE |
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