首页|A Dual-Functional System for the Classification and Diameter Measurement of Aortic Dissections Using CTA Volumes via Deep Learning

A Dual-Functional System for the Classification and Diameter Measurement of Aortic Dissections Using CTA Volumes via Deep Learning

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Acute aortic dissection is one of the most life-threatening cardiovascular diseases,with a high mortality rate.Its prevalence ranges from 0.2%to 0.8%in humans,resulting in a significant number of deaths due to being missed in manual examinations.More importantly,the aortic diameter-a critical indicator for sur-gical selection-significantly influences the outcomes of surgeries post-diagnosis.Therefore,it is an urgent yet challenging mission to develop an automatic aortic dissection diagnostic system that can recognize and classify the aortic dissection type and measure the aortic diameter.This paper offers a dual-functional deep learning system called aortic dissections diagnosis-aiding system(DDAsys)that enables both accurate classification of aortic dissection and precise diameter measurement of the aorta.To this end,we created a dataset containing 61 190 computed tomography angiography(CTA)images from 279 patients from the Division of Cardiovascular Surgery at Tongji Hospital,Wuhan,China.The dataset provides a slice-level summary of difficult-to-identify features,which helps to improve the accu-racy of both recognition and classification.Our system achieves a recognition F1 score of 0.984,an average classification F1 score of 0.935,and the respective measurement precisions for ascending and descending aortic diameters are 0.994 mm and 0.767 mm root mean square error(RMSE).The high consistency(88.6%)between the recommended surgical treatments and the actual corresponding surgeries verifies the capability of our system to aid clinicians in developing a more prompt,precise,and consistent treatment strategy.

Aortic dissectionsComputed tomography angiographyClassificationDeep learning

Zhihui Huang、Rui Wang、Hui Yu、Yifan Xu、Cheng Cheng、Guangwei Wang、Haosen Cao、Xiang Wei、Hai-Tao Zhang

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School of Artificial Intelligence and Automation & the MOE Engineering Research Center of Autonomous Intelligent Unmanned Systems & the State Key Laboratory of Intelligent Manufacturing Equipment and Technology,Huazhong University of Science and Technology,Wuhan 430074,China

Division of Cardiovascular Surgery,Tongji Hospital,Tongji Medical College,Huazhong University of Science and Technology,Wuhan 430030,China

Wuhan Children's Hospital:Wuhan Women and Children Medical Care Center,Tongji Medical College,Huazhong University of Science and Technology,Wuhan 430010,China

National Natural Science Foundation of ChinaNational Natural Science Foundation of ChinaNational Natural Science Foundation of ChinaNational Natural Science Foundation of ChinaNatural Science Foundation of Hubei ProvinceGuangdong Basic and Applied Research Foundation

U21412356222530682070488820004402021CFB0842022B1515120069

2024

工程(英文)

工程(英文)

CSTPCDEI
ISSN:2095-8099
年,卷(期):2024.34(3)