Acta informatica medica : AIM : journal of the Society for Medical Informatics of Bosnia & Herzegovina : casopis Drustva za medicinsku informatiku BiH · 2025
IOL Power Calculation Formulas in Post-LASIK Eyes Using Two Biometry Systems in Vietnam.
СтатьяНазвание и аннотация: Перевод в очереди
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Khanh Tran Ngoc, Anh Bui Thi Van, Thuy Pham Thi Thu
Аннотация
BACKGROUND: Cataract surgery aims to restore clear vision by replacing the clouded crystalline lens with an intraocular lens (IOL). Accurate IOL power calculation is crucial to achieve desirable postoperative refractive outcomes.
OBJECTIVE: The aim of this study was to compare the predictive accuracy of multiple intraocular lens (IOL) power calculation formulas in eyes with prior LASIK surgery using two biometric devices, LenStar 900 and IOLMaster 500, combined with immersion ultrasound (US).
METHODS: This retrospective observational study included 37 eyes of 29 patients who underwent previous LASIK and subsequent cataract surgery. Biometric measurements included axial length, keratometry, anterior chamber depth, and lens thickness, which were obtained using LenStar 900 (19 eyes) or IOLMaster 500 combined with US (18 eyes). IOL power was calculated using the Shammas PL, Haigis-L, Barrett TK no history, Shammas Cooke, and EVO 2.0 (without PK1/PK2). The mean absolute error (MAE) at 3 months postoperatively was assessed for each formula within each device group.
RESULTS: For LenStar 900, the MAE ranged from 0.389 ± 0.329 D (Barrett TK no history) to 0.574 ± 0.689 D (Shammas Cooke), with no significant differences among the formulas. For IOLMaster 500 + US, the MAE ranged from 0.423 ± 0.210 D (Barrett TK no history) to 0.601 ± 0.510 D (Shammas Cooke). Comparisons between devices revealed significantly lower MAE with LenStar 900 for Shammas PL, Shammas Cooke, and EVO 2.0, whereas differences for Haigis-L and Barrett TK showed no significant differences.
CONCLUSION: IOL power prediction in post-LASIK eyes varied according to the biometry device. Barrett TK had no history, and EVO performed well with LenStar 900, while Haigis-L showed consistent accuracy across devices, suggesting that it may be the most reliable formula when using non-synchronous biometry systems.
OBJECTIVE: The aim of this study was to compare the predictive accuracy of multiple intraocular lens (IOL) power calculation formulas in eyes with prior LASIK surgery using two biometric devices, LenStar 900 and IOLMaster 500, combined with immersion ultrasound (US).
METHODS: This retrospective observational study included 37 eyes of 29 patients who underwent previous LASIK and subsequent cataract surgery. Biometric measurements included axial length, keratometry, anterior chamber depth, and lens thickness, which were obtained using LenStar 900 (19 eyes) or IOLMaster 500 combined with US (18 eyes). IOL power was calculated using the Shammas PL, Haigis-L, Barrett TK no history, Shammas Cooke, and EVO 2.0 (without PK1/PK2). The mean absolute error (MAE) at 3 months postoperatively was assessed for each formula within each device group.
RESULTS: For LenStar 900, the MAE ranged from 0.389 ± 0.329 D (Barrett TK no history) to 0.574 ± 0.689 D (Shammas Cooke), with no significant differences among the formulas. For IOLMaster 500 + US, the MAE ranged from 0.423 ± 0.210 D (Barrett TK no history) to 0.601 ± 0.510 D (Shammas Cooke). Comparisons between devices revealed significantly lower MAE with LenStar 900 for Shammas PL, Shammas Cooke, and EVO 2.0, whereas differences for Haigis-L and Barrett TK showed no significant differences.
CONCLUSION: IOL power prediction in post-LASIK eyes varied according to the biometry device. Barrett TK had no history, and EVO performed well with LenStar 900, while Haigis-L showed consistent accuracy across devices, suggesting that it may be the most reliable formula when using non-synchronous biometry systems.