Abstract:
PURPOSE:The purpose of this study is to develop and evaluate a deep learning model to assist radiologists in classifying lower extremity arteries based on the degree of arterial stenosis caused by plaque in lower extremity computed tomography angiography (CTA) of patients with peripheral artery disease. METHODS:In this retrospective study, 265 patients who underwent lower-extremity CTA between January 1, 2016 and October 31, 2019 were selected. A total of 17050 axial images of iliac, femoropopliteal and infrapopliteal artery from these patients were used for the training and validation of the parallel efficient network (p-EffNet), a kind of supervised convolutional neural network, to classify the lower-extremity artery segments according to the degree of stenosis with digital subtraction angiography as reference standard. The classification results of the p-EffNet were then compared with those obtained from radiologists. Receiver operating characteristic curve (ROC) was used to evaluate the performance of the p-EffNet and accuracy, specificity, sensitivity and area under the curve (AUC) were used as measure metrics to compare the performance of the p-EffNet and that of radiologists. RESULTS:The p-EffNet exhibited a good performance of 91.5 % accuracy, 0.987 AUC and 90.2 % sensitivity and 97.7 % specificity in classifying above-knee artery and 90.9 % accuracy, 0.981 AUC, 91.3 % sensitivity and 95.2 % specificity in classifying below-knee artery. When compared with human readers, for both above-knee and below-knee artery, the p-EffNet had comparable accuracy (p = 0.266 and p = 0.808, respectively) and specificity (p = 0.118 and p = 0.971, respectively) but lower sensitivity (p < 0.001 and p = 0.022, respectively). CONCLUSIONS:The p-EffNet demonstrates promising diagnostic performance and has the potential to reduce the workload of radiologists and help to find the plaques that might otherwise have been missed or misjudged.
journal_name
Eur J Radioljournal_title
European journal of radiologyauthors
Dai L,Zhou Q,Zhou H,Zhang H,Cheng P,Ding M,Xu X,Zhang Xdoi
10.1016/j.ejrad.2021.109528subject
Has Abstractpub_date
2021-01-08 00:00:00pages
109528eissn
0720-048Xissn
1872-7727pii
S0720-048X(21)00008-5journal_volume
136pub_type
杂志文章abstract::Aspiration of the hip joint in the setting of a potentially infected total hip replacement is occasionally unsuccessful under fluoroscopic guidance because of periarticular heterotopic bone. Of 136 consecutive patients with total hip replacements who were referred to the radiology department for hip aspiration because...
journal_title:European journal of radiology
pub_type: 杂志文章
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journal_title:European journal of radiology
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