{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,7]],"date-time":"2026-07-07T06:21:25Z","timestamp":1783405285489,"version":"3.54.6"},"reference-count":43,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,7,7]],"date-time":"2026-07-07T00:00:00Z","timestamp":1783382400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Cell Dev. Biol."],"abstract":"<jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>To develop and validate machine learning models for predicting postoperative anterior chamber depth (ACD) in highly myopic cataract patients based on preoperative biometric parameters.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      This prospective study enrolled 203 eyes of 127 highly myopic patients who underwent phacoemulsification and intraocular lens (IOL) implantation between January 2024 and December 2025. Ocular biometric parameters were measured preoperatively and at 3 months postoperatively. A dual feature selection strategy combining Least Absolute Shrinkage and Selection Operator (LASSO) regression and Boruta algorithm was employed to identify important predictors of postoperative ACD in these samples. We compared five machine learning algorithms and evaluated their performance using the coefficient of determination (\n                      <jats:italic>R<\/jats:italic>\n                      <jats:sup>2<\/jats:sup>\n                      ), mean absolute error (MAE), root mean square error (RMSE), and accuracy within \u00b10.1\u00a0mm and \u00b10.2\u00a0mm. Subsequently, Shapley Additive Explanations (SHAP) method was applied to interpret the optimal model\u2019s feature importance.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Six predictors were identified for model construction: axial length ACD\/lens thickness ratio (ACD\/LT), white-to-white distance (WTW), ACD at 90\u00b0 (ACD90), horizontal position angle, and ACD + LT\/2. Among all models, Random Forest algorithm demonstrated the best predictive performance, achieving an\n                      <jats:italic>R<\/jats:italic>\n                      <jats:sup>2<\/jats:sup>\n                      of 0.8259, mean absolute error of 0.0604 mm, and root mean square error of 0.0722\u00a0mm in the test set. The accuracy within \u00b10.1\u00a0mm reached 80.49%, and within \u00b10.2\u00a0mm reached 100%. SHAP analysis revealed that ACD + LT\/2 was the most important predictor, followed by WTW, ACD90, and horizontal position angle.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>This study successfully developed and validated an explainable RF-based machine learning model for the prediction of postoperative ACD in highly myopic cataract patients, which supports more reliable IOL power calculation and offers a practical tool for optimizing surgical planning in highly myopic eyes.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fcell.2026.1876904","type":"journal-article","created":{"date-parts":[[2026,7,7]],"date-time":"2026-07-07T05:48:27Z","timestamp":1783403307000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["An explainable machine learning framework for accurate prediction of postoperative anterior chamber depth in highly myopic cataract surgery"],"prefix":"10.3389","volume":"14","author":[{"given":"Yuyang","family":"Yang","sequence":"first","affiliation":[{"name":"Department of Ophthalmology, Shengli Clinical Medical College of Fujian Medical University, Fujian Provincial Hospital, Fuzhou University Affiliated Provincial Hospital","place":["Fuzhou, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Haoqiang","family":"Cui","sequence":"additional","affiliation":[{"name":"Department of Ophthalmology and Optometry, Fujian Medical University","place":["Fuzhou, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiajia","family":"Gao","sequence":"additional","affiliation":[{"name":"Department of Ophthalmology, Shengli Clinical Medical College of Fujian Medical University, Fujian Provincial Hospital, Fuzhou University Affiliated Provincial Hospital","place":["Fuzhou, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuqing","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Basic Medical Sciences, Fujian Medical University","place":["Fuzhou, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Beiou","family":"Zhang","sequence":"additional","affiliation":[{"name":"Institute for Health and Sport, Victoria University","place":["Melbourne, VIC, Australia"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiahao","family":"Liu","sequence":"additional","affiliation":[{"name":"Centre for Eye Research Australia, Royal Victorian Eye and Ear Hospital","place":["East Melbourne, VIC, Australia"]},{"name":"Ophthalmology, Department of Surgery, The University of Melbourne","place":["Melbourne, VIC, Australia"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Honghua","family":"Yu","sequence":"additional","affiliation":[{"name":"Joint Shantou International Eye Center of Shantou University and the Chinese University of Hong Kong, Shantou, Guangdong, China; Guangdong Provincial People\u2019s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University","place":["Guangzhou, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wenjie","family":"Wu","sequence":"additional","affiliation":[{"name":"Department of Ophthalmology, Shengli Clinical Medical College of Fujian Medical University, Fujian Provincial Hospital, Fuzhou University Affiliated Provincial Hospital","place":["Fuzhou, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Li","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Ophthalmology, Shengli Clinical Medical College of Fujian Medical University, Fujian Provincial Hospital, Fuzhou University Affiliated Provincial Hospital","place":["Fuzhou, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,7,7]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"0","DOI":"10.48130\/vns-0025-0027","article-title":"Artificial intelligence in predicting anti-VEGF treatment response in diabetic macular edema: current progress and future directions","volume":"42","author":"Cao","year":"2025","journal-title":"VNS"},{"key":"B2","doi-asserted-by":"publisher","first-page":"100","DOI":"10.1016\/j.ajo.2020.09.019","article-title":"Accuracy of artificial intelligence formulas and axial length adjustments for highly myopic eyes","volume":"223","author":"Cheng","year":"2020","journal-title":"Am. 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