通过使用深度学习模型,自动诊断加形式和ROP早期阶段
Mahdi Vahidmoghadam1, Parisa Ghorbani1, Mohammad Javad Ahmadi1
1Applied Robotics and AI Solutions (ARAS), Faculties of Electrical and Computer Engineering, K.N. Toosi University of Technology, Tehran, Iran.
Scientific reports
|February 4, 2026
概括
一个自动化系统准确地检测到早产儿的Plus病和早产儿视网膜病 (ROP) 阶段. 这种人工智能工具有助于早期诊断,并有可能预防脆弱新生儿的视力丧失.
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:
- 早产视网膜病变 (ROP) 是早产婴儿视力丧失的重要原因之一.
- 低出生体重 (BW) 和妊娠年龄 (GA) 与增加ROP严重程度有关.
- 早期检测和干预对于管理ROP和预防不可逆转的视力损伤至关重要.
研究的目的:
- 开发和评估一个自动诊断系统来分类Plus疾病和ROP阶段.
- 评估系统在识别异常视网膜血管和疾病进展方面的准确性.
- 探索AI在支持ROP的临床查方面的潜力.
主要方法:
- 利用一个精心策划的视网膜底图像数据集.
- 开发了一个用于加症疾病 (加/正常) 二元分类的模型.
- 实施了ROP阶段 (0-3阶段) 的多类分类模型.
主要成果:
- 实现了高的诊断准确度:0.996 对于Plus疾病检测.
- 在ROP阶段分类中表现出强的表现,准确度为0.98.
- 该模型显示了自动化ROP选的巨大潜力.
结论:
- 该自动化系统在分类Plus疾病和ROP阶段方面表现出高准确度.
- 这种人工智能驱动的方法可能会提高ROP的早期诊断和及时干预.
- 建议进行进一步的多中心验证,以确认其在减少早产婴儿视力障碍方面的临床效用.
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