Emergency radiology · 2025
Deep learning-aided diagnosis of acute abdominal aortic dissection by ultrasound images.
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Lin Zhanye, Zheng Jian, Deng Yaohong, Du Lingyue, Liu Fan, Li Zhengyi
Аннотация
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.
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.