Evidence map›Paper›PMID 41697299›Full record

ArticleJournal of cataract and refractive surgery2026

Predicting vault after implantable collamer lens implantation using intraoperative OCT: establishment and application of models.

Xueyu Du, Hong Yan

Abstract read
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Article in Journal of cataract and refractive surgery, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

2 authors.

Xueyu DuFrom the Shaanxi Eye Hospital, Xi'an People's Hospital (Xi'an Fourth Hospital), Affiliated People's Hospital of Northwest University, Xi'an, Shaanxi Province, China (Du, Yan); Department of Ophthalmology, Hanzhong Central Hospital, Hanzhong, Shaanxi Province, China (Du).ORCID 0000-0002-1371-7698
Hong Yan

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeTo develop a machine learning (ML) model using intraoperative optical coherence tomography (iOCT) data to predict postoperative vault and guide real-time intraocular collamer lens (ICL) orientation adjustments for optimal vault outcomes.

settingShaanxi Eye Hospital, Xi'an People's Hospital (Xi'an Fourth Hospital), Affiliated People's Hospital of Northwest University, Xi'an, Shaanxi Province, China.

designProspective case series.

methodsData was prospectively collected from patients undergoing ICL implantation surgery. Intraoperative vault measurements were acquired in real time using iOCT immediately after ICL placement. Postoperative vault dimensions were then assessed using anterior segment OCT at 1-day, 1-week, and 1-month follow-up visits. These measurements were used to train and validate ML models for predicting postoperative vault based on intraoperative iOCT data.

resultsThe study included 106 patients (158 eyes). Among the evaluated ML models, the random forest algorithm demonstrated superior performance, yielding a mean absolute error of 106.48 ± 19.23 μm and a root mean square error of 141.37 ± 24.87 μm. Prediction accuracy was high across incremental error thresholds: 62.9% (±50 μm), 85.9% (±100 μm), 94.9% (±150 μm), 97.2% (±200 μm), and 98.9% (±250 μm). All models showed strong agreement between predicted and observed values, with the random forest model exhibiting minimal systematic bias.

conclusionsThe findings demonstrate that iOCT enables accurate real-time intraoperative vault measurement, which reliably predicts postoperative vault dimensions. The developed ML model provides an objective, data-driven approach to optimize ICL orientation during surgery, potentially improving clinical outcomes.

Indexed as

Anterior Eye SegmentLens Implantation, IntraocularMachine LearningMyopiaPhakic Intraocular LensesTomography, Optical CoherenceAdultFemaleHumansIntraoperative PeriodMalePredictive Learning ModelsProspective StudiesVisual AcuityYoung Adult

Identifiers

PMID41697299
PMCPMC13387687

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