Li-Ting Huang1, Yi-Shan Tsai1,2, Cheng-Fu Liou3, Tsung-Han Lee3, Po-Tsun Paul Kuo4,5, Han-Sheng Huang1, Chien-Kuo Wang6. 1. Department of Medical Imaging, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, 704, No. 138 Sheng-Li Road, Tainan, Taiwan. 2. Clinical Innovation and Research Center, National Cheng Kung University Hospital, Tainan, Taiwan. 3. Graduate Degree Program of College of Electrical and Computer Engineering, National Yang Ming Chiao Tung University, Hsinchu, Taiwan. 4. College of Artificial Intelligence, National Yang Ming Chiao Tung University, Hsinchu, Taiwan. 5. AI Research Center, Advantech Co., Ltd, Taipei, Taiwan. 6. Department of Medical Imaging, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, 704, No. 138 Sheng-Li Road, Tainan, Taiwan. n625396@mail.hosp.ncku.edu.tw.
Abstract
OBJECTIVES: This study aimed to evaluate the feasibility of automatic Stanford classification of classic aortic dissection (AD) using a 2-step hierarchical neural network. METHODS: Between 2015 and 2019, 130 arterial phase series (57 type A, 43 type B, and 30 negative cases) in aortic CTA were collected for the training and validation. A 2-step hierarchical model was built including the first step detecting AD and the second step predicting the probability (0-1) of Stanford types. The model's performance was evaluated with an off-line prospective test in 2020. The sensitivity and specificity for Stanford type A, type B, and no AD (Sens A, B, N and Spec A, B, N, respectively) and Cohen's kappa were reported. RESULTS: Of 298 cases (22 with type A, 29 with type B, and 247 without AD) in the off-line prospective test, the Sens A, Sens B, and Sens N were 95.45% (95% confidence interval [CI], 77.16-99.88%), 79.31% (95% CI, 60.28-92.01%), and 93.52% (95% CI, 89.69-96.25%), respectively. The Spec A, Spec B, and Spec N were 98.55% (95% CI, 96.33-99.60%), 94.05% (95% CI, 90.52-96.56%), and 94.12% (95% CI, 83.76-98.77%), respectively. The classification rate achieved 92.28% (95% CI, 88.64-95.04%). The Cohen's kappa was 0.766 (95% CI, 0.68-0.85; p < 0.001). CONCLUSIONS: Stanford classification of classic AD can be determined by a 2-step hierarchical neural network with high sensitivity and specificity of type A and high specificity in type B and no AD. KEY POINTS: • The Stanford classification for aortic dissection is widely adopted and divides it into Stanford type A and type B based on the ascending thoracic aorta dissected or not. • The 2-step hierarchical neural network for Stanford classification of classic aortic dissection achieved high sensitivity (95.45%) and specificity (98.55%) of type A and high specificity in type B and no aortic dissection (94.05% and 94.12%, respectively) in 298 test cases. • The 2-step hierarchical neural network demonstrated moderate agreement (Cohen's kappa: 0.766, p < 0.001) with cardiovascular radiologists in detection and Stanford classification of classic aortic dissection in 298 test cases.
OBJECTIVES: This study aimed to evaluate the feasibility of automatic Stanford classification of classic aortic dissection (AD) using a 2-step hierarchical neural network. METHODS: Between 2015 and 2019, 130 arterial phase series (57 type A, 43 type B, and 30 negative cases) in aortic CTA were collected for the training and validation. A 2-step hierarchical model was built including the first step detecting AD and the second step predicting the probability (0-1) of Stanford types. The model's performance was evaluated with an off-line prospective test in 2020. The sensitivity and specificity for Stanford type A, type B, and no AD (Sens A, B, N and Spec A, B, N, respectively) and Cohen's kappa were reported. RESULTS: Of 298 cases (22 with type A, 29 with type B, and 247 without AD) in the off-line prospective test, the Sens A, Sens B, and Sens N were 95.45% (95% confidence interval [CI], 77.16-99.88%), 79.31% (95% CI, 60.28-92.01%), and 93.52% (95% CI, 89.69-96.25%), respectively. The Spec A, Spec B, and Spec N were 98.55% (95% CI, 96.33-99.60%), 94.05% (95% CI, 90.52-96.56%), and 94.12% (95% CI, 83.76-98.77%), respectively. The classification rate achieved 92.28% (95% CI, 88.64-95.04%). The Cohen's kappa was 0.766 (95% CI, 0.68-0.85; p < 0.001). CONCLUSIONS: Stanford classification of classic AD can be determined by a 2-step hierarchical neural network with high sensitivity and specificity of type A and high specificity in type B and no AD. KEY POINTS: • The Stanford classification for aortic dissection is widely adopted and divides it into Stanford type A and type B based on the ascending thoracic aorta dissected or not. • The 2-step hierarchical neural network for Stanford classification of classic aortic dissection achieved high sensitivity (95.45%) and specificity (98.55%) of type A and high specificity in type B and no aortic dissection (94.05% and 94.12%, respectively) in 298 test cases. • The 2-step hierarchical neural network demonstrated moderate agreement (Cohen's kappa: 0.766, p < 0.001) with cardiovascular radiologists in detection and Stanford classification of classic aortic dissection in 298 test cases.
Authors: Loren F Hiratzka; George L Bakris; Joshua A Beckman; Robert M Bersin; Vincent F Carr; Donald E Casey; Kim A Eagle; Luke K Hermann; Eric M Isselbacher; Ella A Kazerooni; Nicholas T Kouchoukos; Bruce W Lytle; Dianna M Milewicz; David L Reich; Souvik Sen; Julie A Shinn; Lars G Svensson; David M Williams Journal: Circulation Date: 2010-03-16 Impact factor: 29.690