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 constructed using fundus color photographs obtained from retinopathy of prematurity (ROP) screening and relevant clinical risk factors, and employs deep learning with neural networks to accurately predict the incidence and severity of ROP within 45 weeks of corrected gestational age. Targeted primarily at ophthalmologists, this dataset can be applied in clinical practice for early ROP screening and prediction of ROP incidence and severity. It supports timely treatment for ROP patients, addressing the invasive drawbacks of traditional ROP screening methods for neonates. It helps to early identify high-risk infants, ensure timely treatment for these patients, and lower the risk of blindness caused by ROP. Additionally, it reduces unnecessary screening procedures for low-risk infants, thereby conserving medical resources.

搜集汇总

数据集介绍

特点

该数据集通过深度学习技术，基于早产儿的眼底彩照和临床危险因素，预测早产儿视网膜病变的发病风险及严重程度。数据集适用于眼科医生进行早期筛查，有助于及时治疗ROP患者，减少致盲风险，并优化医疗资源的使用。