{"title":"Advanced Artificial-Intelligence-Based Jiang Formula for Intraocular Lens Power in Congenital Ectopia Lentis.","authors":"Yan Liu, Xinyue Wang, Linghao Song, Yang Sun, Zexu Chen, Wannan Jia, Xin Shen, Yalei Wang, Xinyao Chen, Qiuyi Huo, Pranav Prakash Edavi, Tianhui Chen, Yongxiang Jiang","doi":"10.1167/tvst.14.2.5","DOIUrl":null,"url":null,"abstract":"<p><strong>Purpose: </strong>The purpose of this study was to develop an artificial intelligence (AI)-based intraocular lens (IOLs) power calculation formula for improving the accuracy of IOLs power calculations in patients with congenital ectopia lentis (CEL).</p><p><strong>Methods: </strong>A total of 651 eyes with CEL that underwent IOLs implantation surgery were included in this study. An AI-based ensemble formula-the Jiang Formula, was developed using a training dataset of 520 eyes and evaluated on a testing dataset of 131 eyes. A five-fold cross-validation and a two-layer ensemble learning model were constructed. The formula was then tested in a test set and compared against five current classic formulas.</p><p><strong>Results: </strong>The cohort included young patients (mean age = 14.38 ± 13.35 years). The Jiang Formula showed the lowest prediction error (PE; = 0.08 ± 1.01 diopters [D]), absolute error (AE; = 0.77 ± 0.65 D), median absolute error (MedAE; = 0.66 D), and root mean square error (RMSE; = 1.02 D) among six formulas (P < 0.001). Moreover, 68.00% of the eyes in the test set had AE within 1.0 D in the Jiang Formula.</p><p><strong>Conclusions: </strong>AI-integrated two-layer ensemble learning model demonstrates promising applications in IOLs power calculations for patients with CEL, not only providing higher predictive accuracy than current classic formulas but also accommodating extreme values and variations in surgical techniques.</p><p><strong>Translational relevance: </strong>The Jiang Formula, an AI-integrated two-layer ensemble learning model, enhances IOLs power calculation accuracy in patients with CEL, ultimately improving surgical outcomes and supporting more effective, personalized treatment in this unique patient group.</p>","PeriodicalId":23322,"journal":{"name":"Translational Vision Science & Technology","volume":"14 2","pages":"5"},"PeriodicalIF":2.6000,"publicationDate":"2025-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Translational Vision Science & Technology","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1167/tvst.14.2.5","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"OPHTHALMOLOGY","Score":null,"Total":0}
引用次数: 0
Abstract
Purpose: The purpose of this study was to develop an artificial intelligence (AI)-based intraocular lens (IOLs) power calculation formula for improving the accuracy of IOLs power calculations in patients with congenital ectopia lentis (CEL).
Methods: A total of 651 eyes with CEL that underwent IOLs implantation surgery were included in this study. An AI-based ensemble formula-the Jiang Formula, was developed using a training dataset of 520 eyes and evaluated on a testing dataset of 131 eyes. A five-fold cross-validation and a two-layer ensemble learning model were constructed. The formula was then tested in a test set and compared against five current classic formulas.
Results: The cohort included young patients (mean age = 14.38 ± 13.35 years). The Jiang Formula showed the lowest prediction error (PE; = 0.08 ± 1.01 diopters [D]), absolute error (AE; = 0.77 ± 0.65 D), median absolute error (MedAE; = 0.66 D), and root mean square error (RMSE; = 1.02 D) among six formulas (P < 0.001). Moreover, 68.00% of the eyes in the test set had AE within 1.0 D in the Jiang Formula.
Conclusions: AI-integrated two-layer ensemble learning model demonstrates promising applications in IOLs power calculations for patients with CEL, not only providing higher predictive accuracy than current classic formulas but also accommodating extreme values and variations in surgical techniques.
Translational relevance: The Jiang Formula, an AI-integrated two-layer ensemble learning model, enhances IOLs power calculation accuracy in patients with CEL, ultimately improving surgical outcomes and supporting more effective, personalized treatment in this unique patient group.
期刊介绍：
Translational Vision Science & Technology (TVST), an official journal of the Association for Research in Vision and Ophthalmology (ARVO), an international organization whose purpose is to advance research worldwide into understanding the visual system and preventing, treating and curing its disorders, is an online, open access, peer-reviewed journal emphasizing multidisciplinary research that bridges the gap between basic research and clinical care. A highly qualified and diverse group of Associate Editors and Editorial Board Members is led by Editor-in-Chief Marco Zarbin, MD, PhD, FARVO.
The journal covers a broad spectrum of work, including but not limited to:
Applications of stem cell technology for regenerative medicine,
Development of new animal models of human diseases,
Tissue bioengineering,
Chemical engineering to improve virus-based gene delivery,
Nanotechnology for drug delivery,
Design and synthesis of artificial extracellular matrices,
Development of a true microsurgical operating environment,
Refining data analysis algorithms to improve in vivo imaging technology,
Results of Phase 1 clinical trials,
Reverse translational ("bedside to bench") research.
TVST seeks manuscripts from scientists and clinicians with diverse backgrounds ranging from basic chemistry to ophthalmic surgery that will advance or change the way we understand and/or treat vision-threatening diseases. TVST encourages the use of color, multimedia, hyperlinks, program code and other digital enhancements.
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