Deep learning–assisted diagnosis of acute mesenteric ischemia based on CT angiography images
PurposeAcute Mesenteric Ischemia (AMI) is a critical condition marked by restricted blood flow to the intestine, which can lead to tissue necrosis and fatal outcomes. We aimed to develop a deep learning (DL) model based on CT angiography (CTA) imaging and clinical data to diagnose AMI.MethodsA retro...
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Frontiers Media S.A.
2025-01-01
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| Series: | Frontiers in Medicine |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fmed.2025.1510357/full |
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| author | Lei Song Xuesong Zhang Jian Zhang Jie Wu Jinkai Wang Feng Wang |
| author_facet | Lei Song Xuesong Zhang Jian Zhang Jie Wu Jinkai Wang Feng Wang |
| author_sort | Lei Song |
| collection | DOAJ |
| description | PurposeAcute Mesenteric Ischemia (AMI) is a critical condition marked by restricted blood flow to the intestine, which can lead to tissue necrosis and fatal outcomes. We aimed to develop a deep learning (DL) model based on CT angiography (CTA) imaging and clinical data to diagnose AMI.MethodsA retrospective study was conducted on 228 patients suspected of AMI, divided into training and test sets. Clinical data (medical history and laboratory indicators) was included in a multivariate logistic regression analysis to identify the independent factors associated with AMI and establish a clinical factors model. The arterial and venous CTA images were utilized to construct DL model. A Fusion Model was constructed by integrating clinical factors into the DL model. The performance of the models was assessed using receiver operating characteristic (ROC) curves and decision curve analysis (DCA).ResultsAlbumin and International Normalized Ratio (INR) were associated with AMI by univariate and multivariate logistic regression (P < 0.05). In the test set, the area under ROC curve (AUC) of the clinical factor model was 0.60 (sensitivity 0.47, specificity 0.86). The AUC of the DL model based on CTA images reached 0.90, which was significantly higher than the AUC values of the clinical factor model, as confirmed by the DeLong test (P < 0.05). The Fusion Model also showed exceptional performance in terms of AUC, accuracy, sensitivity, specificity, and precision, with values of 0.96, 0.94, 0.94, 0.95, and 0.98, respectively. DCA indicated that the Fusion Model provided a greater net benefit than those of models based solely on imaging and clinical information across the majority of the reasonable threshold probabilities.ConclusionThe incorporation of CTA images and clinical information into the model markedly enhances the diagnostic accuracy and efficiency of AMI. This approach provides a reliable tool for the early diagnosis of AMI and the subsequent implementation of appropriate clinical intervention. |
| format | Article |
| id | doaj-art-25f4a17d937b4f07aac87a34bafb4560 |
| institution | Kabale University |
| issn | 2296-858X |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Medicine |
| spelling | doaj-art-25f4a17d937b4f07aac87a34bafb45602025-01-24T07:13:55ZengFrontiers Media S.A.Frontiers in Medicine2296-858X2025-01-011210.3389/fmed.2025.15103571510357Deep learning–assisted diagnosis of acute mesenteric ischemia based on CT angiography imagesLei Song0Xuesong Zhang1Jian Zhang2Jie Wu3Jinkai Wang4Feng Wang5Department of Interventional Therapy, The First Affiliated Hospital of Dalian Medical University, Dalian, Liaoning, ChinaDepartment of Interventional Therapy, The Second Affiliated Hospital of Dalian Medical University, Dalian, ChinaDepartment of Interventional Therapy, The First Affiliated Hospital of Dalian Medical University, Dalian, Liaoning, ChinaDepartment of Interventional Therapy, The Second Affiliated Hospital of Dalian Medical University, Dalian, ChinaDepartment of Interventional Therapy, The Second Affiliated Hospital of Dalian Medical University, Dalian, ChinaDepartment of Interventional Therapy, The First Affiliated Hospital of Dalian Medical University, Dalian, Liaoning, ChinaPurposeAcute Mesenteric Ischemia (AMI) is a critical condition marked by restricted blood flow to the intestine, which can lead to tissue necrosis and fatal outcomes. We aimed to develop a deep learning (DL) model based on CT angiography (CTA) imaging and clinical data to diagnose AMI.MethodsA retrospective study was conducted on 228 patients suspected of AMI, divided into training and test sets. Clinical data (medical history and laboratory indicators) was included in a multivariate logistic regression analysis to identify the independent factors associated with AMI and establish a clinical factors model. The arterial and venous CTA images were utilized to construct DL model. A Fusion Model was constructed by integrating clinical factors into the DL model. The performance of the models was assessed using receiver operating characteristic (ROC) curves and decision curve analysis (DCA).ResultsAlbumin and International Normalized Ratio (INR) were associated with AMI by univariate and multivariate logistic regression (P < 0.05). In the test set, the area under ROC curve (AUC) of the clinical factor model was 0.60 (sensitivity 0.47, specificity 0.86). The AUC of the DL model based on CTA images reached 0.90, which was significantly higher than the AUC values of the clinical factor model, as confirmed by the DeLong test (P < 0.05). The Fusion Model also showed exceptional performance in terms of AUC, accuracy, sensitivity, specificity, and precision, with values of 0.96, 0.94, 0.94, 0.95, and 0.98, respectively. DCA indicated that the Fusion Model provided a greater net benefit than those of models based solely on imaging and clinical information across the majority of the reasonable threshold probabilities.ConclusionThe incorporation of CTA images and clinical information into the model markedly enhances the diagnostic accuracy and efficiency of AMI. This approach provides a reliable tool for the early diagnosis of AMI and the subsequent implementation of appropriate clinical intervention.https://www.frontiersin.org/articles/10.3389/fmed.2025.1510357/fullacute mesenteric ischemiamultiphase CT angiographyartificial intelligencedeep learningdisease diagnosis |
| spellingShingle | Lei Song Xuesong Zhang Jian Zhang Jie Wu Jinkai Wang Feng Wang Deep learning–assisted diagnosis of acute mesenteric ischemia based on CT angiography images Frontiers in Medicine acute mesenteric ischemia multiphase CT angiography artificial intelligence deep learning disease diagnosis |
| title | Deep learning–assisted diagnosis of acute mesenteric ischemia based on CT angiography images |
| title_full | Deep learning–assisted diagnosis of acute mesenteric ischemia based on CT angiography images |
| title_fullStr | Deep learning–assisted diagnosis of acute mesenteric ischemia based on CT angiography images |
| title_full_unstemmed | Deep learning–assisted diagnosis of acute mesenteric ischemia based on CT angiography images |
| title_short | Deep learning–assisted diagnosis of acute mesenteric ischemia based on CT angiography images |
| title_sort | deep learning assisted diagnosis of acute mesenteric ischemia based on ct angiography images |
| topic | acute mesenteric ischemia multiphase CT angiography artificial intelligence deep learning disease diagnosis |
| url | https://www.frontiersin.org/articles/10.3389/fmed.2025.1510357/full |
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