Deep learning-aided diagnosis of acute abdominal aortic dissection by ultrasound images.

IF 1.7 Q3 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING Emergency Radiology Pub Date : 2025-01-17 DOI:10.1007/s10140-025-02311-y

Zhanye Lin, Jian Zheng, Yaohong Deng, Lingyue Du, Fan Liu, Zhengyi Li

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Abstract

Purpose: Acute abdominal aortic dissection (AD) is a serious disease. Early detection based on ultrasound (US) can improve the prognosis of AD, especially in emergency settings. We explored the ability of deep learning (DL) to diagnose abdominal AD in US images, which may help the diagnosis of AD by novice radiologists or non-professionals.

Methods: There were 374 US images from patients treated before June 30, 2022. The images were classified as AD-positive and AD-negative images. Among them, 90% of images were used as the training set, and 10% of images were used as the test set. A Densenet-169 model and a VGG-16 model were used in this study and compared with two human readers.

Results: DL models demonstrated high sensitivity and AUC for diagnosing abdominal AD in US images, and DL models showed generally better performance than human readers.

Conclusion: Our findings demonstrated the efficacy of DL-aided diagnosis of abdominal AD in US images, which can be helpful in emergency settings.

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超声图像深度学习辅助诊断急性腹主动脉夹层。

目的：急性腹主动脉夹层（AD）是一种严重的疾病。基于超声（US）的早期检测可以改善AD的预后，特别是在紧急情况下。我们探讨了深度学习（DL）在美国影像中诊断腹部AD的能力，这可能有助于新手放射科医生或非专业人员诊断AD。方法：在2022年6月30日之前接受治疗的患者的374张美国图像。图像分为ad阳性和ad阴性图像。其中，90%的图像作为训练集，10%的图像作为测试集。本研究采用Densenet-169模型和VGG-16模型，并与两名人类读者进行比较。结果：DL模型在诊断腹部AD的US图像中具有较高的灵敏度和AUC，且DL模型的表现普遍优于人类阅读器。结论：我们的研究结果证明了超声图像中dl辅助诊断腹部AD的有效性，这在急诊情况下是有帮助的。

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

Emergency Radiology RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING-

CiteScore

4.60

自引率

4.50%

发文量

期刊介绍： To advance and improve the radiologic aspects of emergency careTo establish Emergency Radiology as an area of special interest in the field of diagnostic imagingTo improve methods of education in Emergency RadiologyTo provide, through formal meetings, a mechanism for presentation of scientific papers on various aspects of Emergency Radiology and continuing educationTo promote research in Emergency Radiology by clinical and basic science investigators, including residents and other traineesTo act as the resource body on Emergency Radiology for those interested in emergency patient care Members of the American Society of Emergency Radiology (ASER) receive the Emergency Radiology journal as a benefit of membership!