Machine learning adaptation of intraocular lens power calculation for a patient group

Abstract Background To examine the effectiveness of the use of machine learning for adapting an intraocular lens (IOL) power calculation for a patient group. Methods In this retrospective study, the clinical records of 1,611 eyes of 1,169 Japanese patients who received a single model of monofocal IO...

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Autores principales:	Yosai Mori, Tomofusa Yamauchi, Shota Tokuda, Keiichiro Minami, Hitoshi Tabuchi, Kazunori Miyata
Formato:	article
Lenguaje:	EN
Publicado:	BMC 2021
Materias:	Machine learning Adaptation Intraocular lens power calculation Patient ethnicity Patient race Region of patient Ophthalmology RE1-994
Acceso en línea:	https://doaj.org/article/206d49a64a5f43b88e854bc56d7fdf82
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spelling	oai:doaj.org-article:206d49a64a5f43b88e854bc56d7fdf822021-11-21T12:25:29ZMachine learning adaptation of intraocular lens power calculation for a patient group10.1186/s40662-021-00265-z2326-0254https://doaj.org/article/206d49a64a5f43b88e854bc56d7fdf822021-11-01T00:00:00Zhttps://doi.org/10.1186/s40662-021-00265-zhttps://doaj.org/toc/2326-0254Abstract Background To examine the effectiveness of the use of machine learning for adapting an intraocular lens (IOL) power calculation for a patient group. Methods In this retrospective study, the clinical records of 1,611 eyes of 1,169 Japanese patients who received a single model of monofocal IOL (SN60WF, Alcon) at Miyata Eye Hospital were reviewed and analyzed. Using biometric metrics and postoperative refractions of 1211 eyes of 769 patients, constants of the SRK/T and Haigis formulas were optimized. The SRK/T formula was adapted using a support vector regressor. Prediction errors in the use of adapted formulas as well as the SRK/T, Haigis, Hill-RBF and Barrett Universal II formulas were evaluated with data from 395 eyes of 395 distinct patients. Mean prediction errors, median absolute errors, and percentages of eyes within ± 0.25 D, ± 0.50 D, and ± 1.00 D, and over + 0.50 D of errors were compared among formulas. Results The mean prediction errors in the use of the SRT/K and adapted formulas were smaller than the use of other formulas (P < 0.001). In the absolute errors, the Hill-RBF and adapted methods were better than others. The performance of the Barrett Universal II was not better than the others for the patient group. There were the least eyes with hyperopic refractive errors (16.5%) in the use of the adapted formula. Conclusions Adapting IOL power calculations using machine learning technology with data from a particular patient group was effective and promising.Yosai MoriTomofusa YamauchiShota TokudaKeiichiro MinamiHitoshi TabuchiKazunori MiyataBMCarticleMachine learningAdaptationIntraocular lens power calculationPatient ethnicityPatient raceRegion of patientOphthalmologyRE1-994ENEye and Vision, Vol 8, Iss 1, Pp 1-9 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Machine learning Adaptation Intraocular lens power calculation Patient ethnicity Patient race Region of patient Ophthalmology RE1-994
spellingShingle	Machine learning Adaptation Intraocular lens power calculation Patient ethnicity Patient race Region of patient Ophthalmology RE1-994 Yosai Mori Tomofusa Yamauchi Shota Tokuda Keiichiro Minami Hitoshi Tabuchi Kazunori Miyata Machine learning adaptation of intraocular lens power calculation for a patient group
description	Abstract Background To examine the effectiveness of the use of machine learning for adapting an intraocular lens (IOL) power calculation for a patient group. Methods In this retrospective study, the clinical records of 1,611 eyes of 1,169 Japanese patients who received a single model of monofocal IOL (SN60WF, Alcon) at Miyata Eye Hospital were reviewed and analyzed. Using biometric metrics and postoperative refractions of 1211 eyes of 769 patients, constants of the SRK/T and Haigis formulas were optimized. The SRK/T formula was adapted using a support vector regressor. Prediction errors in the use of adapted formulas as well as the SRK/T, Haigis, Hill-RBF and Barrett Universal II formulas were evaluated with data from 395 eyes of 395 distinct patients. Mean prediction errors, median absolute errors, and percentages of eyes within ± 0.25 D, ± 0.50 D, and ± 1.00 D, and over + 0.50 D of errors were compared among formulas. Results The mean prediction errors in the use of the SRT/K and adapted formulas were smaller than the use of other formulas (P < 0.001). In the absolute errors, the Hill-RBF and adapted methods were better than others. The performance of the Barrett Universal II was not better than the others for the patient group. There were the least eyes with hyperopic refractive errors (16.5%) in the use of the adapted formula. Conclusions Adapting IOL power calculations using machine learning technology with data from a particular patient group was effective and promising.
format	article
author	Yosai Mori Tomofusa Yamauchi Shota Tokuda Keiichiro Minami Hitoshi Tabuchi Kazunori Miyata
author_facet	Yosai Mori Tomofusa Yamauchi Shota Tokuda Keiichiro Minami Hitoshi Tabuchi Kazunori Miyata
author_sort	Yosai Mori
title	Machine learning adaptation of intraocular lens power calculation for a patient group
title_short	Machine learning adaptation of intraocular lens power calculation for a patient group
title_full	Machine learning adaptation of intraocular lens power calculation for a patient group
title_fullStr	Machine learning adaptation of intraocular lens power calculation for a patient group
title_full_unstemmed	Machine learning adaptation of intraocular lens power calculation for a patient group
title_sort	machine learning adaptation of intraocular lens power calculation for a patient group
publisher	BMC
publishDate	2021
url	https://doaj.org/article/206d49a64a5f43b88e854bc56d7fdf82
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Machine learning adaptation of intraocular lens power calculation for a patient group

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