广东省人民医院眼科早产儿视网膜病变发病风险及严重程度预测数据集

该数据基于早产儿视网膜病变（ROP）筛查的眼底彩照和相关临床危险因素，通过神经网络进行深度学习，对矫正胎龄45周内ROP的发生和严重程度进行准确的预测。本数据主要适用于眼科医生，可应用于临床对ROP进行早期筛查，并对ROP的发生和严重程度进行预测，有助于及时对ROP患者进行治疗，解决了目前传统ROP筛查方法对新生儿有损伤性等弊端，有利于及早识别高危患儿，确保对患儿及时治疗，降低ROP致盲风险；同时减少低危患儿不必要筛查次数，节约医疗资源。

This dataset is developed based on fundus color photographs and relevant clinical risk factors for retinopathy of prematurity (ROP) screening, and adopts deep learning with neural networks to accurately predict the onset and severity of ROP within 45 weeks of postmenstrual age (PMA). Primarily intended for ophthalmologists, this dataset can be applied in clinical settings for early ROP screening and prediction of ROP onset and severity. It enables timely treatment for ROP patients, addressing the drawbacks of current conventional ROP screening methods including their invasiveness to newborns. It helps to identify high-risk infants early, ensure timely intervention for these patients, and reduce the risk of ROP-induced blindness; meanwhile, it cuts down unnecessary screening sessions for low-risk infants and conserves medical resources.

搜集汇总

背景与挑战

背景概述

该数据集利用早产儿视网膜病变（ROP）的眼底彩照和临床危险因素，通过深度学习神经网络技术，预测矫正胎龄45周内ROP的发病风险和严重程度。它专为眼科医生设计，支持早期筛查和精准预测，有助于及时治疗高危患儿、降低致盲风险，同时减少低危患儿的不必要筛查，从而提升医疗效率并节约资源。