PDF(Pubmed)
Abstract：
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.
BACKGROUND: This is a part of the Silesia Diabetes-Heart Project (Clinical-Trials.gov Identifier: NCT05626413).
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.
BACKGROUND: This is a part of the Silesia Diabetes-Heart Project (Clinical-Trials.gov Identifier: NCT05626413).
摘要：
背景：糖尿病（DM）中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。这种糖尿病并发症的诊断是耗时的，在临床实践中不是常规的。与可获得和常规进行的眼底视网膜成像相反。利用通过糖尿病眼筛查收集的视网膜图像的人工智能（AI）是否可以为CAN提供有效的诊断方法尚不清楚。
方法：这是一个单一的中心，作为糖尿病患者心血管疾病一部分的糖尿病患者队列中的观察性研究:西里西亚糖尿病-心脏项目(NCT05626413)。要诊断CAN,我们使用标准的CV自主反射测试。在这项分析中，我们实施了基于AI的深度学习技术，使用非散瞳5场彩色眼底成像来识别CAN患者。已经利用多实例学习和主要ResNet18作为骨干网络开发了两个实验。在未见过的图像集上测试之前，模型经过了训练和验证。
结果：在对229例患者的2275张视网膜图像的分析中，ResNet18骨干模型在CAN的二元分类中展示了强大的诊断能力，正确识别测试集中93%的CAN案例和89%的非CAN案例。该模型获得的受试者工作特征曲线下面积(AUCROC)为0.87(95%CI0.74-0.97)。为了区分CAN(dsCAN)的确定阶段或严重阶段，ResNet18模型准确地分类了78%的dsCAN病例和93%的没有dsCAN的病例，AUCROC为0.94（95%CI0.86-1.00）。备用骨干模型，ResWide50，显示dsCAN的灵敏度提高了89%，但AUCROC略低，为0.91（95%CI0.73-1.00）。
结论：利用视网膜图像的基于AI的算法可以对CAN患者进行高精度区分。可以在常规临床实践中实施眼底图像的AI分析以检测CAN,以识别处于最高CV风险的患者。
背景：这是西里西亚糖尿病-心脏项目的一部分（Clinical-Trials.govIdentifier：NCT05626413）。
方法：这是一个单一的中心，作为糖尿病患者心血管疾病一部分的糖尿病患者队列中的观察性研究:西里西亚糖尿病-心脏项目(NCT05626413)。要诊断CAN,我们使用标准的CV自主反射测试。在这项分析中，我们实施了基于AI的深度学习技术，使用非散瞳5场彩色眼底成像来识别CAN患者。已经利用多实例学习和主要ResNet18作为骨干网络开发了两个实验。在未见过的图像集上测试之前，模型经过了训练和验证。
结果：在对229例患者的2275张视网膜图像的分析中，ResNet18骨干模型在CAN的二元分类中展示了强大的诊断能力，正确识别测试集中93%的CAN案例和89%的非CAN案例。该模型获得的受试者工作特征曲线下面积(AUCROC)为0.87(95%CI0.74-0.97)。为了区分CAN(dsCAN)的确定阶段或严重阶段，ResNet18模型准确地分类了78%的dsCAN病例和93%的没有dsCAN的病例，AUCROC为0.94（95%CI0.86-1.00）。备用骨干模型，ResWide50，显示dsCAN的灵敏度提高了89%，但AUCROC略低，为0.91（95%CI0.73-1.00）。
结论：利用视网膜图像的基于AI的算法可以对CAN患者进行高精度区分。可以在常规临床实践中实施眼底图像的AI分析以检测CAN,以识别处于最高CV风险的患者。
背景：这是西里西亚糖尿病-心脏项目的一部分（Clinical-Trials.govIdentifier：NCT05626413）。
