A deep learning model for carotid plaques detection based on CTA images: a two stepwise early-stage clinical validation study
ObjectiveTo develop a deep learning (DL) model for carotid plaque detection based on CTA images and evaluate the clinical application feasibility and value of the model.MethodsWe retrospectively collected data from patients with carotid atherosclerotic plaques who underwent continuous CTA examinatio...
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Frontiers Media S.A.
2025-01-01
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| Series: | Frontiers in Neurology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fneur.2024.1480792/full |
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| author | Zhongping Guo Ying Liu Jingxu Xu Chencui Huang Fandong Zhang Chongchang Miao Yonggang Zhang Mengshuang Li Hangsheng Shan Yan Gu |
| author_facet | Zhongping Guo Ying Liu Jingxu Xu Chencui Huang Fandong Zhang Chongchang Miao Yonggang Zhang Mengshuang Li Hangsheng Shan Yan Gu |
| author_sort | Zhongping Guo |
| collection | DOAJ |
| description | ObjectiveTo develop a deep learning (DL) model for carotid plaque detection based on CTA images and evaluate the clinical application feasibility and value of the model.MethodsWe retrospectively collected data from patients with carotid atherosclerotic plaques who underwent continuous CTA examinations of the head and neck at a tertiary hospital from October 2020 to October 2022. The model combined ResUNet with the Pyramid Scene Parsing Network (PSPNet) to enhance plaque segmentation. Patient plaques were divided into training, validation, and testing sets in a ratio of 7:1.5:1.5. We analyzed recall (lesion-level sensitivity), sensitivity (patient-level), and precision to evaluate the model’s diagnostic performance for carotid plaques. The two stepwise early-stage clinical validation study (Comparison study and Model-human study) was used to simulate real clinical plaque diagnostic scenarios.ResultsIn total, 647 patients were included in the dataset, including 475 for training, 86 for validation, and 86 for testing. The DL model based on CTA images showed good precision in plaque diagnosis (validation set: precision = 80.49%, sensitivity = 90.70%, recall = 84.62%; test set: precision = 78.37%, sensitivity = 91.86%, recall = 84.58%). In addition, subgroup analysis of the plaque was carried out in the test set. The model had high accuracy in identifying plaques at different locations (Recall: 83.72, 76.32, 89.25, and 83.02%) and with different morphologies (Recall: 86.03, 79.17%). This model also analyzed the results of different types of plaques and showed good to moderate plaque diagnostic accuracy for different plaque types (Recall: 70.00, 86.87, 84.29%). Especially, in the clinical application scenario analysis, the model’s diagnostic results for plaques were found to be higher than those of 4 out of 6 radiologists (p < 0.001). Furthermore, in Model-human Real Clinical Scenarios study, we found that the model improved the radiologists’ sensitivity in diagnosing plaques. Additionally, the model’s diagnostic time for plaques (6 s) was found to be significantly shorter than that all of radiologists (p < 0.001).ConclusionThis AI model demonstrated strong clinical potential for carotid plaque detection with improved clinician diagnostic performance, shortening time, and practical implementation in real-world clinical cases. |
| format | Article |
| id | doaj-art-8d7457e4262041cd982171db70b16c95 |
| institution | Kabale University |
| issn | 1664-2295 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Neurology |
| spelling | doaj-art-8d7457e4262041cd982171db70b16c952025-01-13T05:10:18ZengFrontiers Media S.A.Frontiers in Neurology1664-22952025-01-011510.3389/fneur.2024.14807921480792A deep learning model for carotid plaques detection based on CTA images: a two stepwise early-stage clinical validation studyZhongping Guo0Ying Liu1Jingxu Xu2Chencui Huang3Fandong Zhang4Chongchang Miao5Yonggang Zhang6Mengshuang Li7Hangsheng Shan8Yan Gu9Department of Radiology, The First People’s Hospital of Lianyungang, Lianyungang Clinical College of Nanjing Medical University, Lianyungang, ChinaDepartment of Radiology, The First People’s Hospital of Lianyungang, Lianyungang Clinical College of Nanjing Medical University, Lianyungang, ChinaDepartment of Research Collaboration, R&D Center, Beijing Deepwise & League of PHD Technology Co., Ltd., Beijing, ChinaDepartment of Research Collaboration, R&D Center, Beijing Deepwise & League of PHD Technology Co., Ltd., Beijing, ChinaDeepwise Artificial Intelligence (AI) Lab, Deepwise, Beijing, ChinaDepartment of Radiology, The First People’s Hospital of Lianyungang, Lianyungang Clinical College of Nanjing Medical University, Lianyungang, ChinaDepartment of Radiology, The First People’s Hospital of Lianyungang, Lianyungang Clinical College of Nanjing Medical University, Lianyungang, ChinaDepartment of Radiology, The First People’s Hospital of Lianyungang, Lianyungang Clinical College of Nanjing Medical University, Lianyungang, ChinaDepartment of Radiology, The First People’s Hospital of Lianyungang, Lianyungang Clinical College of Nanjing Medical University, Lianyungang, ChinaDepartment of Radiology, The First People’s Hospital of Lianyungang, Lianyungang Clinical College of Nanjing Medical University, Lianyungang, ChinaObjectiveTo develop a deep learning (DL) model for carotid plaque detection based on CTA images and evaluate the clinical application feasibility and value of the model.MethodsWe retrospectively collected data from patients with carotid atherosclerotic plaques who underwent continuous CTA examinations of the head and neck at a tertiary hospital from October 2020 to October 2022. The model combined ResUNet with the Pyramid Scene Parsing Network (PSPNet) to enhance plaque segmentation. Patient plaques were divided into training, validation, and testing sets in a ratio of 7:1.5:1.5. We analyzed recall (lesion-level sensitivity), sensitivity (patient-level), and precision to evaluate the model’s diagnostic performance for carotid plaques. The two stepwise early-stage clinical validation study (Comparison study and Model-human study) was used to simulate real clinical plaque diagnostic scenarios.ResultsIn total, 647 patients were included in the dataset, including 475 for training, 86 for validation, and 86 for testing. The DL model based on CTA images showed good precision in plaque diagnosis (validation set: precision = 80.49%, sensitivity = 90.70%, recall = 84.62%; test set: precision = 78.37%, sensitivity = 91.86%, recall = 84.58%). In addition, subgroup analysis of the plaque was carried out in the test set. The model had high accuracy in identifying plaques at different locations (Recall: 83.72, 76.32, 89.25, and 83.02%) and with different morphologies (Recall: 86.03, 79.17%). This model also analyzed the results of different types of plaques and showed good to moderate plaque diagnostic accuracy for different plaque types (Recall: 70.00, 86.87, 84.29%). Especially, in the clinical application scenario analysis, the model’s diagnostic results for plaques were found to be higher than those of 4 out of 6 radiologists (p < 0.001). Furthermore, in Model-human Real Clinical Scenarios study, we found that the model improved the radiologists’ sensitivity in diagnosing plaques. Additionally, the model’s diagnostic time for plaques (6 s) was found to be significantly shorter than that all of radiologists (p < 0.001).ConclusionThis AI model demonstrated strong clinical potential for carotid plaque detection with improved clinician diagnostic performance, shortening time, and practical implementation in real-world clinical cases.https://www.frontiersin.org/articles/10.3389/fneur.2024.1480792/fullcomputed tomography angiographyartificial intelligencehead and neckcarotid plaquedeep learning |
| spellingShingle | Zhongping Guo Ying Liu Jingxu Xu Chencui Huang Fandong Zhang Chongchang Miao Yonggang Zhang Mengshuang Li Hangsheng Shan Yan Gu A deep learning model for carotid plaques detection based on CTA images: a two stepwise early-stage clinical validation study Frontiers in Neurology computed tomography angiography artificial intelligence head and neck carotid plaque deep learning |
| title | A deep learning model for carotid plaques detection based on CTA images: a two stepwise early-stage clinical validation study |
| title_full | A deep learning model for carotid plaques detection based on CTA images: a two stepwise early-stage clinical validation study |
| title_fullStr | A deep learning model for carotid plaques detection based on CTA images: a two stepwise early-stage clinical validation study |
| title_full_unstemmed | A deep learning model for carotid plaques detection based on CTA images: a two stepwise early-stage clinical validation study |
| title_short | A deep learning model for carotid plaques detection based on CTA images: a two stepwise early-stage clinical validation study |
| title_sort | deep learning model for carotid plaques detection based on cta images a two stepwise early stage clinical validation study |
| topic | computed tomography angiography artificial intelligence head and neck carotid plaque deep learning |
| url | https://www.frontiersin.org/articles/10.3389/fneur.2024.1480792/full |
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