Journal of cataract and refractive surgery · 2024
VAULT: vault accuracy using deep learning technology: new image-based artificial intelligence model for predicting implantable collamer lens postoperative vault.
СтатьяНазвание и аннотация: Перевод пока не подготовлен
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.
Nasser Taj, Hirabayashi Matthew, Virdi Gurpal, Abramson Andrew, Parkhurst Gregory
Аннотация
PURPOSE: To develop an accurate deep learning model to predict postoperative vault of phakic implantable collamer lenses (ICLs).
SETTING: Parkhurst NuVision LASIK Eye Surgery, San Antonio, Texas.
DESIGN: Retrospective machine learning study.
METHODS: 437 eyes of 221 consecutive patients who underwent ICL implantation were included. A neural network was trained on preoperative very high-frequency digital ultrasound images, patient demographics, and postoperative vault.
RESULTS: 3059 images from 437 eyes of 221 patients were used to train the algorithm on individual ICL sizes. The 13.7 mm size was excluded because of insufficient data. A mean absolute error of 66.3 μm, 103 μm, and 91.8 μm were achieved with 100%, 99.0%, and 96.6% of predictions within 500 μm for the 12.1 mm, 12.6 mm, and 13.2 mm sizes, respectively.
CONCLUSIONS: This deep learning model achieved a high level of accuracy of predicting postoperative ICL vault with the overwhelming majority of predictions successfully within a clinically acceptable margin of vault.
SETTING: Parkhurst NuVision LASIK Eye Surgery, San Antonio, Texas.
DESIGN: Retrospective machine learning study.
METHODS: 437 eyes of 221 consecutive patients who underwent ICL implantation were included. A neural network was trained on preoperative very high-frequency digital ultrasound images, patient demographics, and postoperative vault.
RESULTS: 3059 images from 437 eyes of 221 patients were used to train the algorithm on individual ICL sizes. The 13.7 mm size was excluded because of insufficient data. A mean absolute error of 66.3 μm, 103 μm, and 91.8 μm were achieved with 100%, 99.0%, and 96.6% of predictions within 500 μm for the 12.1 mm, 12.6 mm, and 13.2 mm sizes, respectively.
CONCLUSIONS: This deep learning model achieved a high level of accuracy of predicting postoperative ICL vault with the overwhelming majority of predictions successfully within a clinically acceptable margin of vault.