人工智能辅助量化早期高近视期 fundus tessellation 的研究
Yi-Ming Guo1, Kexin Wu1, Juan Huang1
1Shaanxi Eye Hospital, Xi'an People's Hospital (Xi'an Fourth Hospital), Affiliated People's Hospital of Northwest University, Xi'an, China.
Frontiers in medicine
|January 26, 2026
概括
用AI测量早期高近视 (eo-HM) 的儿童的 fundus tessellation 密度 (FTD) 与轴长度 (AL) 相对应. 区域性FTD,特别是在鼻腔区域,可以帮助诊断和监测儿科高近视.
科学领域:
- 眼科医生 眼科 眼科
- 医疗成像医学成像
- 人工智能的人工智能
背景情况:
- 儿童早期高近视 (eo-HM) 存在视力损失的重大风险.
- 精确监测轴长度 (AL) 对于管理儿科近视进展至关重要.
- 需要新的生物标志物来早期检测和管理eO-HM.
研究的目的:
- 通过人工智能辅助分析,对患有e-HM的儿童进行定量评估底部形密度 (FTD).
- 在这个群体中调查FTD和轴长 (AL) 之间的关联.
主要方法:
- 一项横截面研究包括6岁以下的患有eohM的儿童.
- 人工智能 (AI) 量化了斑点区和部门的FTD.
- 进行了循环折射,AL测量和 fundus摄影.
- 相关性和回归分析探讨了FTD-AL关系.
主要成果:
- FTD从黄斑中心减少到外围.
- 在6mm的鼻腔和下部部门观察到更高的FTD.
- 总体而言,FTD与AL有显著的正相关性 (r=0.46,p<0.001).
- 在对年龄和性别进行调整后,鼻和鼻上部门的FTD独立预测了AL.
结论:
- 人工智能辅助的FTD量化提供了对视网膜EO-HM变化的客观测量.
- 区域FTD,特别是在鼻和周围的区域,与轴延长密切相关.
- FTD可以作为一个有价值的生物标志物,用于早期诊断和监测儿科高近视.
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