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通过自动视网膜图像分析系统检测到的活性糖尿病黄斑的错误分类率和预测因素
Lamberto La Franca1, Carola Rutigliani2, Lisa Checchin1
1Department of Ophthalmology, IRCCS San Raffaele Scientific Institute, IRCCS Ospedale San Raffaele, University Vita-Salute, Via Olgettina 60, 20132, Milan, Italy.
Ophthalmology and therapy
|April 8, 2024
概括
这项研究评估了用于诊断糖尿病黄斑胀 (DME) 的自动视网膜图像分析系统 (ARIAS),发现它在查新病例方面有效,但在监测不活跃的DME方面不太可靠.
科学领域:
- 眼科医生 眼科 眼科
- 医疗成像医学成像
- 医疗保健中的人工智能
背景情况:
- 糖尿病黄斑 (DME) 是糖尿病患者视力丧失的主要原因.
- 准确及时诊断活跃的DME对于有效的治疗和维护视力至关重要.
- 自动视网膜图像分析系统 (ARIAS) 提供了有效查和诊断的潜力.
研究的目的:
- 评估ARIAS在检测活性DME时的灵敏度,特异性和错误分类率.
- 通过ARIAS识别与真假阳性诊断相关的临床和成像因素.
主要方法:
- 一项涉及298只糖尿病患者眼睛的横截面研究,用于DME评估.
- 由ARIAS (EyeArt V.2.1.0) 分析的基底照片.
- 通过光谱域光学连贯性断层扫描 (SD-OCT) 定义的活性DME.
主要成果:
- 对于活跃的DME,ARIAS的敏感度为82.61%,特异性为84.47%.
- 总体错误分类率为16%.
- 年龄较小和糖尿病持续时间较短等因素与真实阳性有关,而较长的糖尿病持续时间和失活的DME病史与虚假阳性有关.
结论:
- ARIAS对于查未经治疗的DME是有效的,但对于监测不活跃的DME不太适合.
- 老年患者和缺乏与DME相关的底部病变的患者的敏感性较低.
- 在有非活化DME史的患者中,特异性下降.
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