基于人工智能的糖尿病视网膜眼底图像中心血管自主神经病变分类：西里西亚糖尿病心脏研究。
Artificial intelligence-based classification of cardiac autonomic neuropathy from retinal fundus images in patients with diabetes: The Silesia Diabetes Heart Study.
机构信息
Department of Internal Medicine, Diabetology and Nephrology, Faculty of Medical Sciences in Zabrze, Medical University of Silesia, Katowice, Poland.
Liverpool Centre for Cardiovascular Science at University of Liverpool, Liverpool John Moores University and Liverpool Heart and Chest Hospital, Liverpool, UK.
出版信息
Cardiovasc Diabetol. 2024 Aug 10;23(1):296. doi: 10.1186/s12933-024-02367-z.
BACKGROUND
Cardiac autonomic neuropathy (CAN) in diabetes mellitus (DM) is independently associated with cardiovascular (CV) events and CV death. Diagnosis of this complication of DM is time-consuming and not routinely performed in the clinical practice, in contrast to fundus retinal imaging which is accessible and routinely performed. Whether artificial intelligence (AI) utilizing retinal images collected through diabetic eye screening can provide an efficient diagnostic method for CAN is unknown.
METHODS
This was a single center, observational study in a cohort of patients with DM as a part of the Cardiovascular Disease in Patients with Diabetes: The Silesia Diabetes-Heart Project (NCT05626413). To diagnose CAN, we used standard CV autonomic reflex tests. In this analysis we implemented AI-based deep learning techniques with non-mydriatic 5-field color fundus imaging to identify patients with CAN. Two experiments have been developed utilizing Multiple Instance Learning and primarily ResNet 18 as the backbone network. Models underwent training and validation prior to testing on an unseen image set.
RESULTS
In an analysis of 2275 retinal images from 229 patients, the ResNet 18 backbone model demonstrated robust diagnostic capabilities in the binary classification of CAN, correctly identifying 93% of CAN cases and 89% of non-CAN cases within the test set. The model achieved an area under the receiver operating characteristic curve (AUCROC) of 0.87 (95% CI 0.74-0.97). For distinguishing between definite or severe stages of CAN (dsCAN), the ResNet 18 model accurately classified 78% of dsCAN cases and 93% of cases without dsCAN, with an AUCROC of 0.94 (95% CI 0.86-1.00). An alternate backbone model, ResWide 50, showed enhanced sensitivity at 89% for dsCAN, but with a marginally lower AUCROC of 0.91 (95% CI 0.73-1.00).
CONCLUSIONS
AI-based algorithms utilising retinal images can differentiate with high accuracy patients with CAN. AI analysis of fundus images to detect CAN may be implemented in routine clinical practice to identify patients at the highest CV risk.
TRIAL REGISTRATION
This is a part of the Silesia Diabetes-Heart Project (Clinical-Trials.gov Identifier: NCT05626413).
背景
糖尿病患者的心脏自主神经病变（CAN）与心血管（CV）事件和 CV 死亡独立相关。与眼底视网膜成像相比，这种 DM 的并发症的诊断既耗时又不在临床实践中常规进行，眼底视网膜成像在临床上可及且常规进行。利用通过糖尿病眼病筛查收集的视网膜图像的人工智能（AI）是否可以提供一种有效的 CAN 诊断方法尚不清楚。
方法
这是一项在心血管疾病患者中的糖尿病：西里西亚糖尿病-心脏项目（NCT05626413）的患者队列中的单中心观察性研究。为了诊断 CAN，我们使用了标准的 CV 自主反射测试。在这项分析中，我们使用基于 AI 的深度学习技术和非散瞳 5 区域彩色眼底成像来识别患有 CAN 的患者。利用多实例学习和主要的 ResNet 18 作为骨干网络，开发了两个实验。模型在测试未见图像集之前进行了训练和验证。
结果
在对 229 名患者的 2275 张视网膜图像的分析中，ResNet 18 骨干模型在 CAN 的二进制分类中表现出强大的诊断能力，在测试集中正确识别了 93%的 CAN 病例和 89%的非 CAN 病例。该模型的接收器工作特征曲线下面积（AUCROC）为 0.87（95%CI 0.74-0.97）。对于区分明确或严重的 CAN（dsCAN），ResNet 18 模型准确地将 78%的 dsCAN 病例和 93%的无 dsCAN 病例分类，AUCROC 为 0.94（95%CI 0.86-1.00）。另一个骨干模型 ResWide 50 的 dsCAN 敏感性提高到 89%，但 AUCROC 略有降低，为 0.91（95%CI 0.73-1.00）。
结论
利用视网膜图像的基于 AI 的算法可以高度准确地区分患有 CAN 的患者。使用眼底图像分析 AI 来检测 CAN 可能会在常规临床实践中实施，以识别处于最高 CV 风险的患者。
试验注册
这是西里西亚糖尿病-心脏项目的一部分（Clinical-Trials.gov 标识符：NCT05626413）。
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