The application and progress of machine learning and image recognition in predicting the prognosis of macular hiatus surgery
10.3760/cma.j.cn431274-20240331-00549
- VernacularTitle:机器学习和图像识别在黄斑裂孔手术预后预测中的应用与进展
- Author:
Tiantian CHENG
1
;
Ruoan HAN
;
Youxin CHEN
Author Information
1. 中国医学科学院北京协和医院眼科 中国医学科学院北京协和医学院，北京 100730
- Keywords:
Vitreoretinal surgery;
Macular hole;
Optical coherence tomography;
Image recognition
- From:
Journal of Chinese Physician
2024;26(5):656-662
- CountryChina
- Language:Chinese
-
Abstract:
Macular hiatus (MH) is a neuroepithelial deficiency that occurs in the macular area and can lead to severe central visual impairment. According to the different causes and stages of MH, clinical methods mainly include vitrectomy, inner limiting membrane dissection, and intraocular gas filling to achieve anatomical and functional recovery. The preoperative optical coherence tomography (OCT) parameters such as hole diameter, macular hole index, hole formation factor, and macular closure index, as well as patient baseline characteristics, are closely related to the prognosis of MH surgery. Artificial intelligence models based on machine learning and image recognition have shown potential in predicting surgical outcomes. This article aims to summarize the important parameters that affect the prognosis of MH surgery, analyze the current application status and possible improvement directions of machine learning and image recognition in MH surgery prediction, and provide a basis and new ideas for related research and clinical applications.
