Key result
The DDAsys deep learning system achieved a recognition F1 score of 0.984, an average classification F1 score of 0.937, and 88.6% consistency with recommended surgical treatments.
Why the study?
Manual examinations miss acute aortic dissections, and measuring aortic diameter is critical for surgical selection, making automated diagnostic and measurement systems an urgent clinical need.
Does a dual-functional deep learning system (DDAsys) accurately classify aortic dissections and measure aortic diameters on CTA volumes?
Does a dual-functional deep learning system (DDAsys) accurately classify aortic dissections and measure aortic diameters on CTA volumes?
A novel deep learning system (DDAsys) demonstrates high accuracy in classifying aortic dissections and measuring aortic diameters on CTA, showing 88.6% consistency with actual surgical treatments.
May aid aortic dissection assessment on CTA; hypothesis-generating and requires prospective validation before practice change.
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 surgical 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 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 Cardiothoracic and Vascular Surgery at Tongji Hospital, Wuhan, China. The dataset provides a slice-level summary of difficult-to-identify features, which helps to improve the accuracy of both recognition and classification. Our system achieves a recognition F1 score of 0.984, an average classification F1 score 0.937, 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.
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Huang et al. (2024) studied Acute aortic dissection (n=279). DDAsys deep learning system was evaluated on Recognition F1 score, classification F1 score, and measurement precision (RMSE). The DDAsys deep learning system achieved a recognition F1 score of 0.984, an average classification F1 score of 0.937, and 88.6% consistency with recommended surgical treatments.
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