通过GPT-4V fundus图像分析检测和特征识别
Jalil Jalili1,2, Anuwat Jiravarnsirikul2,3, Christopher Bowd1,2
1Division of Ophthalmology Informatics and Data Science, Viterbi Family Department of Ophthalmology, Shiley Eye Institute, University of California, San Diego, La Jolla, California.
Ophthalmology science
|January 29, 2025
概括
GPT-4V (OpenAI) 显示了使用 fundus 图像检测青光眼的潜力,精度略低于专家,但在识别关键特征方面具有很高的一致性. 需要对数据集进行进一步的验证.
科学领域:
- 眼科医生 眼科 眼科
- 人工智能的人工智能
- 医疗成像医学成像
背景情况:
- 玻璃眼是全球不可逆转的失明的主要原因.
- 早期发现和准确的诊断对于有效的管理至关重要.
- 眼底图像分析是青光眼诊断的关键组成部分.
研究的目的:
- 为了评估GPT-4V (OpenAI) 在识别青光眼的诊断准确性.
- 与专家眼科医生相比,评估GPT-4V在检测与青光眼有关的特征方面的能力.
- 确定GPT-4V的评估的一致性和一致性.
主要方法:
- 从公共数据集 (ACRIMA,ORIGA,RIM-One v3) 中分析了300张 fundus 图像.
- GPT-4V评估了图像的质量,分级性,杯与盘的比率以及青光眼的状态.
- GPT-4V的评估与两个独立的专家评级者和数据集标签进行了比较.
主要成果:
- 在数据集中,GPT-4V的整体准确度略低于专家评分器 (范围从0.68到0.81).
- GPT-4V与专家之间的协议不同,科恩卡帕值从0.08到0.72.7不等.
- GPT-4V在图像分级性 (≥89%) 中表现出很高的一致性,并且在边框薄化和杯与盘的比率评估中存在很大的一致性.
结论:
- 通过 fundus 图像分析,GPT-4V 显示出作为补充眼查和检测的补充工具的前景.
- 该模型在识别关键的玻璃眼特征方面表现良好.
- 协议在数据集之间有所不同,表明需要进一步细化和验证.
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