深度学习在冠状动脉狭窄鉴别诊断中的准确性:系统回顾和荟萃分析

IF 2.9 3区 医学 Q2 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING BMC Medical Imaging Pub Date : 2024-09-16 DOI:10.1186/s12880-024-01403-4
Li Tu, Ying Deng, Yun Chen, Yi Luo
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引用次数: 0

摘要

近年来,随着深度学习在心脏病领域受到广泛关注,一些研究探索了基于冠状动脉造影(CAG)或冠状动脉CT造影(CCTA)图像的深度学习在检测冠状动脉狭窄程度方面的潜力。然而,目前仍缺乏对其诊断准确性的系统了解,阻碍了冠状动脉狭窄智能诊断的发展。因此,我们开展了这项研究,以回顾基于图像的深度学习在检测冠状动脉狭窄方面的准确性。我们检索了截至 2023 年 4 月 11 日的 PubMed、Cochrane、Embase 和 Web of Science。我们使用 QUADAS-2 工具评估了纳入研究的偏倚风险。我们提取了深度学习在测试集中的准确性,并按二元分类和多类分类情况进行了分组分析。我们根据不同的狭窄程度进行了亚组分析,并应用双弧线变换来处理数据。我们的系统综述最终纳入了 18 项研究,涉及 3568 名患者和 13362 张图像。在纳入的研究中,我们基于 CAG 和 CCTA 构建了深度学习模型。在二元分类任务中,检测血管狭窄程度大于 25%、大于 50%、大于 70% 的准确率分别为 0.81(95% CI:0.71-0.85)、0.73(95% CI:0.58-0.88)和 0.61(95% CI:0.56-0.65)。在多类分类任务中,检测血管水平 0-25%、25-50%、50-70% 和 70-100% 狭窄度的准确率分别为 0.78(95% CI:0.73-0.84)、0.86(95% CI:0.78-0.93)、0.83(95% CI:0.70-0.97)和 0.70(95% CI:0.42-0.98)。我们的研究表明,基于 CAG 和 CCTA 的深度学习模型在诊断不同程度的冠状动脉狭窄方面似乎相对准确。然而,对于不同程度的狭窄,其准确性仍有待进一步提高。
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Accuracy of deep learning in the differential diagnosis of coronary artery stenosis: a systematic review and meta-analysis
In recent years, as deep learning has received widespread attention in the field of heart disease, some studies have explored the potential of deep learning based on coronary angiography (CAG) or coronary CT angiography (CCTA) images in detecting the extent of coronary artery stenosis. However, there is still a lack of a systematic understanding of its diagnostic accuracy, impeding the advancement of intelligent diagnosis of coronary artery stenosis. Therefore, we conducted this study to review the accuracy of image-based deep learning in detecting coronary artery stenosis. We retrieved PubMed, Cochrane, Embase, and Web of Science until April 11, 2023. The risk of bias in the included studies was appraised using the QUADAS-2 tool. We extracted the accuracy of deep learning in the test set and performed subgroup analyses by binary and multiclass classification scenarios. We performed a subgroup analysis based on different degrees of stenosis and applied a double arcsine transformation to process the data. The analysis was done by using R. Our systematic review finally included 18 studies, involving 3568 patients and 13,362 images. In the included studies, deep learning models were constructed based on CAG and CCTA. In binary classification tasks, the accuracy for detecting > 25%, > 50% and > 70% degrees of stenosis at the vessel level were 0.81 (95% CI: 0.71–0.85), 0.73 (95% CI: 0.58–0.88) and 0.61 (95% CI: 0.56–0.65), respectively. In multiclass classification tasks, the accuracy for detecting 0–25%, 25–50%, 50–70%, and 70–100% degrees of stenosis at the vessel level were 0.78 (95% CI: 0.73–0.84), 0.86 (95% CI: 0.78–0.93), 0.83 (95% CI: 0.70–0.97), and 0.70 (95% CI: 0.42–0.98), respectively. Our study shows that deep learning models based on CAG and CCTA appear to be relatively accurate in diagnosing different degrees of coronary artery stenosis. However, for various degrees of stenosis, their accuracy still needs to be further improved.
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来源期刊
BMC Medical Imaging
BMC Medical Imaging RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING-
CiteScore
4.60
自引率
3.70%
发文量
198
审稿时长
27 weeks
期刊介绍: BMC Medical Imaging is an open access journal publishing original peer-reviewed research articles in the development, evaluation, and use of imaging techniques and image processing tools to diagnose and manage disease.
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