Xin Wei1, Jing Jiang2, Wenting Cao3, Han Yu4, Hao Deng5, Jinhua Chen6, Shanwei Bai7, Zhiming Zhou8. 1. Department of Radiology, The Second Affiliated Hospital of Chongqing Medical University, Yuzhong District, 400010 Chongqing, China. Electronic address: weixin731824258@163.com. 2. Department of Radiology, The Second Affiliated Hospital of Chongqing Medical University, Yuzhong District, 400010 Chongqing, China. Electronic address: 568835068@qq.com. 3. Department of Radiology, The Second Affiliated Hospital of Chongqing Medical University, Yuzhong District, 400010 Chongqing, China. Electronic address: 2738702563@qq.com. 4. Department of Radiology, The Second Affiliated Hospital of Chongqing Medical University, Yuzhong District, 400010 Chongqing, China. Electronic address: 402714471@qq.com. 5. Department of Radiology, The Second Affiliated Hospital of Chongqing Medical University, Yuzhong District, 400010 Chongqing, China. Electronic address: 405326876@qq.com. 6. Department of Radiology, The Second Affiliated Hospital of Chongqing Medical University, Yuzhong District, 400010 Chongqing, China. Electronic address: 13908391746@163.com. 7. Department of Radiology, The Second Affiliated Hospital of Chongqing Medical University, Yuzhong District, 400010 Chongqing, China. Electronic address: baishanwei1990@163.com. 8. Department of Radiology, The Second Affiliated Hospital of Chongqing Medical University, Yuzhong District, 400010 Chongqing, China. Electronic address: zhouzhiming1127@cqmu.edu.cn.
Abstract
PURPOSE: The aim of this study was to evaluate whether a novel head and neck artificial intelligence (AI)-assisted diagnostic system based on a three-dimensional convolutional neural network (3D-CNN) could improve the accuracy, efficiency and working mode of intracranial aneurysm (IA) detection. METHODS: A total of 212 patients who underwent computed tomography angiography (CTA) and digital subtraction angiography (DSA) were retrospectively included. We used three diagnostic modes to detect IAs with CTA: AI, physicians and AI + physicians. Taking the diagnostic results of DSA as the gold standard, the sensitivity, specificity, accuracy, mean reporting time, and interobserver consistency of the three diagnostic modes were calculated and compared at the patient and lesion levels. RESULTS: Of 212 patients, 179 were diagnosed with IAs by DSA, and 224 IAs were analyzed. The sensitivity, specificity and accuracy of the AI system in diagnosing aneurysms were 84.9% (95% confidence interval [CI], 78.9-89.5%), 18.2% (95% CI, 8.2-34.8%) and 74.5% (95% CI, 68.3-80.0%) at the patient-level, and 77.2% (95% CI, 71.3-82.3%), 14.0% (95% CI, 6.2-27.6%) and 67.0% (95% CI, 61.2-72.4%) at the lesion-level, respectively. With AI assistance, junior physicians had the similar diagnostic performance as senior physicians at the patient (sensitivity 95.0% vs. 95.0%, specificity 48.5% vs. 57.6%, accuracy 87.7% vs. 89.2%, p = 0.690) and lesion levels (sensitivity 88.0% vs. 89.7%, specificity 32.0% vs. 38.0%, accuracy 77.8% vs. 80.3%, p = 1.000), especially for aneurysms < 5 mm (sensitivity 83.2% vs. 87.6%, specificity 60.0% vs. 63.2%, accuracy 75.4% vs. 78.9%, p = 0.424). The reporting efficiency of junior and senior physicians improved by 20.7% (141.1 ± 52.6 s to 111.9 ± 46.3 s, p = 0.004) and 18.8% (113.2 ± 42.5 s to 91.9 ± 41.2 s, p = 0.011), respectively. CONCLUSIONS: This 3D-CNN-based AI system significantly improved the accuracy and efficiency of physician detection of IA. The AI + physicians work mode could have a major influence on daily clinical practice and clinical research.
PURPOSE: The aim of this study was to evaluate whether a novel head and neck artificial intelligence (AI)-assisted diagnostic system based on a three-dimensional convolutional neural network (3D-CNN) could improve the accuracy, efficiency and working mode of intracranial aneurysm (IA) detection. METHODS: A total of 212 patients who underwent computed tomography angiography (CTA) and digital subtraction angiography (DSA) were retrospectively included. We used three diagnostic modes to detect IAs with CTA: AI, physicians and AI + physicians. Taking the diagnostic results of DSA as the gold standard, the sensitivity, specificity, accuracy, mean reporting time, and interobserver consistency of the three diagnostic modes were calculated and compared at the patient and lesion levels. RESULTS: Of 212 patients, 179 were diagnosed with IAs by DSA, and 224 IAs were analyzed. The sensitivity, specificity and accuracy of the AI system in diagnosing aneurysms were 84.9% (95% confidence interval [CI], 78.9-89.5%), 18.2% (95% CI, 8.2-34.8%) and 74.5% (95% CI, 68.3-80.0%) at the patient-level, and 77.2% (95% CI, 71.3-82.3%), 14.0% (95% CI, 6.2-27.6%) and 67.0% (95% CI, 61.2-72.4%) at the lesion-level, respectively. With AI assistance, junior physicians had the similar diagnostic performance as senior physicians at the patient (sensitivity 95.0% vs. 95.0%, specificity 48.5% vs. 57.6%, accuracy 87.7% vs. 89.2%, p = 0.690) and lesion levels (sensitivity 88.0% vs. 89.7%, specificity 32.0% vs. 38.0%, accuracy 77.8% vs. 80.3%, p = 1.000), especially for aneurysms < 5 mm (sensitivity 83.2% vs. 87.6%, specificity 60.0% vs. 63.2%, accuracy 75.4% vs. 78.9%, p = 0.424). The reporting efficiency of junior and senior physicians improved by 20.7% (141.1 ± 52.6 s to 111.9 ± 46.3 s, p = 0.004) and 18.8% (113.2 ± 42.5 s to 91.9 ± 41.2 s, p = 0.011), respectively. CONCLUSIONS: This 3D-CNN-based AI system significantly improved the accuracy and efficiency of physician detection of IA. The AI + physicians work mode could have a major influence on daily clinical practice and clinical research.