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.
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