ArticleGraefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie2022
Machine learning predicting myopic regression after corneal refractive surgery using preoperative data and fundus photography.
Article in Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed, 24 citations in OpenAlex.
- A comparative study on the influence of age-specific ocular anatomical differences on the prediction of post-ICL surgery vault.BMC ophthalmology · 2026Article
- Artificial intelligence in refractive surgery: progress, challenges, and future directions.Frontiers in cell and developmental biology · 2026Review
- Predicting myopia risk using a machine learning model based on fundus imageomics.Scientific reports · 2025Article
- Prediction of long-term uncorrected distance visual acuity in surgically SMILE corrected myopic eyes using machine learning.BMJ open ophthalmology · 2025Article
- Epidemiological insights into complication and outcomes in corneal refractive surgery population: findings from KNHANES 2010-2012.BMC ophthalmology · 2025Article
- Retinal features as predictive indicators for high myopia: insights from explainable multi-machine learning models.Frontiers in bioengineering and biotechnology · 2025Article
- Factors affecting long-term myopic regression after corneal refractive surgery for civilian pilots in southwest China.BMC ophthalmology · 2024Article
- Deep learning prediction of steep and flat corneal curvature using fundus photography in post-COVID telemedicine era.Medical & biological engineering & computing · 2024Article
- Prediction of multiclass surgical outcomes in glaucoma using multimodal deep learning based on free-text operative notes and structured EHR data.Journal of the American Medical Informatics Association : JAMIA · 2024Article
- Bibliometric analysis of hotspots and trends of global myopia research.International journal of ophthalmology · 2024Article
- Insights into artificial intelligence in myopia management: from a data perspective.Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie · 2024Review
- Myopia prediction for children and adolescents via time-aware deep learning.Scientific reports · 2023Article
- Artificial intelligence-aided diagnosis and treatment in the field of optometry.International journal of ophthalmology · 2023Article
- Artificial Intelligence in Ophthalmic Surgery: Current Applications and Expectations.Clinical ophthalmology (Auckland, N.Z.) · 2023Review
- Artificial intelligence and digital solutions for myopia.Taiwan journal of ophthalmologyReview
- Shaping the future of myopia with artificial intelligence: Mapping trends and promising directions.Advances in ophthalmology practice and researchArticle
- Deep learning models for predicting opaque bubble layer morphology of keratorefractive lenticule extraction before laser scanning.Advances in ophthalmology practice and researchArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeMyopic regression after surgery is the most common long-term complication of refractive surgery, but it is difficult to identify myopic regression without long-term observation. This study aimed to develop machine learning models to identify high-risk patients for refractive regression based on preoperative data and fundus photography.
methodsThis retrospective study assigned subjects to the training (n = 1606 eyes) and validation (n = 403 eyes) datasets with chronological data splitting. Machine learning models with ResNet50 (for image analysis) and XGBoost (for integration of all variables and fundus photography) were developed based on subjects who underwent corneal refractive surgery. The primary outcome was the predictive performance for the presence of myopic regression at 4 years of follow-up examination postoperatively.
resultsBy integrating all factors and fundus photography, the final combined machine learning model showed good performance to predict myopic regression of more than 0.5 D (area under the receiver operating characteristic curve [ROC-AUC], 0.753; 95% confidence interval [CI], 0.710-0.793). The performance of the final model was better than the single ResNet50 model only using fundus photography (ROC-AUC, 0.673; 95% CI, 0.627-0.716). The top-five most important input features were fundus photography, preoperative anterior chamber depth, planned ablation thickness, age, and preoperative central corneal thickness.
conclusionOur machine learning algorithm provides an efficient strategy to identify high-risk patients with myopic regression without additional labor, cost, and time. Surgeons might benefit from preoperative risk assessment of myopic regression, patient counseling before surgery, and surgical option decisions.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.