International journal of computer assisted radiology and surgery · 2023
An automated screening model for aortic emergencies using convolutional neural networks and cropped computed tomography angiography images of the aorta.
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Wada Tomoki, Takahashi Masamichi, Matsunaga Hiroki, Kawai Go, Kaneshima Risa, Machida Munetaka, Fujita Nana, Matsuoka Yujiro
Аннотация
PURPOSE: Patients with aortic emergencies, such as aortic dissection and rupture, are at risk of rapid deterioration, necessitating prompt diagnosis. This study introduces a novel automated screening model for computed tomography angiography (CTA) of patients with aortic emergencies, utilizing deep convolutional neural network (DCNN) algorithms.
METHODS: Our model (Model A) initially predicted the positions of the aorta in the original axial CTA images and extracted the sections containing the aorta from these images. Subsequently, it predicted whether the cropped images showed aortic lesions. To compare the predictive performance of Model A in identifying aortic emergencies, we also developed Model B, which directly predicted the presence or absence of aortic lesions in the original images. Ultimately, these models categorized patients based on the presence or absence of aortic emergencies, as determined by the number of consecutive images expected to show the lesion.
RESULTS: The models were trained with 216 CTA scans and tested with 220 CTA scans. Model A demonstrated a higher area under the curve (AUC) for patient-level classification of aortic emergencies than Model B (0.995; 95% confidence interval [CI], 0.990-1.000 vs. 0.972; 95% CI, 0.950-0.994, respectively; p = 0.013). Among patients with aortic emergencies, the AUC of Model A for patient-level classification of aortic emergencies involving the ascending aorta was 0.971 (95% CI, 0.931-1.000).
CONCLUSION: The model utilizing DCNNs and cropped CTA images of the aorta effectively screened CTA scans of patients with aortic emergencies. This study would help develop a computer-aided triage system for CT scans, prioritizing the reading for patients requiring urgent care and ultimately promoting rapid responses to patients with aortic emergencies.
METHODS: Our model (Model A) initially predicted the positions of the aorta in the original axial CTA images and extracted the sections containing the aorta from these images. Subsequently, it predicted whether the cropped images showed aortic lesions. To compare the predictive performance of Model A in identifying aortic emergencies, we also developed Model B, which directly predicted the presence or absence of aortic lesions in the original images. Ultimately, these models categorized patients based on the presence or absence of aortic emergencies, as determined by the number of consecutive images expected to show the lesion.
RESULTS: The models were trained with 216 CTA scans and tested with 220 CTA scans. Model A demonstrated a higher area under the curve (AUC) for patient-level classification of aortic emergencies than Model B (0.995; 95% confidence interval [CI], 0.990-1.000 vs. 0.972; 95% CI, 0.950-0.994, respectively; p = 0.013). Among patients with aortic emergencies, the AUC of Model A for patient-level classification of aortic emergencies involving the ascending aorta was 0.971 (95% CI, 0.931-1.000).
CONCLUSION: The model utilizing DCNNs and cropped CTA images of the aorta effectively screened CTA scans of patients with aortic emergencies. This study would help develop a computer-aided triage system for CT scans, prioritizing the reading for patients requiring urgent care and ultimately promoting rapid responses to patients with aortic emergencies.