Detection of femoropopliteal arterial steno-occlusion at MR angiography: initial experience with artificial intelligence.

IF 3.7 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING
Tri-Thien Nguyen, Lukas Folle, Thomas Bayer
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引用次数: 0

Abstract

Background: This study evaluated a deep learning (DL) algorithm for detecting vessel steno-occlusions in patients with peripheral arterial disease (PAD). It utilised a private dataset, which was acquired and annotated by the authors through their institution and subsequently validated by two blinded readers.

Methods: A single-centre retrospective study analysed 105 magnetic resonance angiography (MRA) images using an EfficientNet B0 DL model. Initially, inter-reader variability was assessed using the complete dataset. For a subset of these images (29 from the left side and 35 from the right side) where digital subtraction angiography (DSA) data was available as the ground truth, the model's accuracy and the area under the curve at receiver operating characteristics analysis (ROC-AUC) were evaluated.

Results: A total of 105 patient examinations (mean age, 75 years ±12 [mean ± standard deviation], 61 men) were evaluated. Radiologist-DL model agreement had a quadratic weighted Cohen κ ≥ 0.72 (left side) and ≥ 0.66 (right side). Radiologist inter-reader agreement was ≥ 0.90 (left side) and ≥ 0.87 (right side). The DL model achieved a 0.897 accuracy and a 0.913 ROC-AUC (left side) and 0.743 and 0.830 (right side). Radiologists achieved 0.931 and 0.862 accuracies, with 0.930 and 0.861 ROC-AUCs (left side), and 0.800 and 0.799 accuracies, with 0.771 ROC-AUCs (right side).

Conclusion: The DL model provided valid results in identifying arterial steno-occlusion in the superficial femoral and popliteal arteries on MRA among PAD patients. However, it did not reach the inter-reader agreement of two radiologists.

Relevance statement: The tested DL model is a promising tool for assisting in the detection of arterial steno-occlusion in patients with PAD, but further optimisation is necessary to provide radiologists with useful support in their daily routine diagnostics.

Key points: • This study focused on the application of DL for arterial steno-occlusion detection in lower extremities on MRA. • A previously developed DL model was tested for accuracy and inter-reader agreement. • While the model showed promising results, it does not yet replace human expertise in detecting arterial steno-occlusion on MRA.

Abstract Image

在 MR 血管造影术中检测股动脉狭窄闭塞:人工智能的初步经验。
研究背景本研究评估了一种用于检测外周动脉疾病(PAD)患者血管狭窄闭塞的深度学习(DL)算法。该研究利用了一个私人数据集,该数据集由作者通过其所在机构获取并注释,随后由两名盲人读者进行验证:一项单中心回顾性研究使用 EfficientNet B0 DL 模型分析了 105 幅磁共振血管造影 (MRA) 图像。首先,使用完整的数据集评估了读片者之间的差异性。对于这些图像的子集(29 幅来自左侧,35 幅来自右侧),以数字减影血管造影(DSA)数据作为基本事实,评估了模型的准确性和接收器操作特性分析的曲线下面积(ROC-AUC):共对 105 名患者(平均年龄为 75 岁 ±12 [平均 ± 标准差],61 名男性)的检查结果进行了评估。放射医师-DL模型一致性的二次加权科恩κ≥0.72(左侧)和≥0.66(右侧)。放射科医师读片者之间的一致性≥ 0.90(左侧)和≥ 0.87(右侧)。DL 模型的准确度为 0.897,ROC-AUC 为 0.913(左侧),ROC-AUC 为 0.743 和 0.830(右侧)。放射科医生的准确率分别为 0.931 和 0.862,ROC-AUC 分别为 0.930 和 0.861(左侧),准确率分别为 0.800 和 0.799,ROC-AUC 分别为 0.771(右侧):结论:DL模型能有效识别PAD患者MRA上的股浅动脉和腘动脉狭窄闭塞。然而,它并没有达到两位放射科医生读片的一致性:经过测试的 DL 模型是协助检测 PAD 患者动脉狭窄闭塞的有效工具,但仍需进一步优化,以便为放射医师的日常诊断提供有用的支持:- 本研究的重点是在 MRA 上应用 DL 检测下肢动脉狭窄闭塞。- 研究人员对之前开发的 DL 模型进行了准确性和读片者之间一致性的测试。- 虽然该模型显示出良好的效果,但还不能取代人类在 MRA 上检测动脉狭窄闭塞的专业知识。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
European Radiology Experimental
European Radiology Experimental Medicine-Radiology, Nuclear Medicine and Imaging
CiteScore
6.70
自引率
2.60%
发文量
56
审稿时长
18 weeks
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