Interpretable Artificial Intelligence for Detecting Acute Heart Failure on Acute Chest CT Scans
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arXiv
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| Auteurs principaux: | , , , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866913938881380352 |
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| author | Ørting, Silas Nyboe Miger, Kristina Olesen, Anne Sophie Overgaard Boesen, Mikael Ploug Andersen, Michael Brun Petersen, Jens Nielsen, Olav W. de Bruijne, Marleen |
| author_facet | Ørting, Silas Nyboe Miger, Kristina Olesen, Anne Sophie Overgaard Boesen, Mikael Ploug Andersen, Michael Brun Petersen, Jens Nielsen, Olav W. de Bruijne, Marleen |
| contents | Introduction: Chest CT scans are increasingly used in dyspneic patients where acute heart failure (AHF) is a key differential diagnosis. Interpretation remains challenging and radiology reports are frequently delayed due to a radiologist shortage, although flagging such information for emergency physicians would have therapeutic implication. Artificial intelligence (AI) can be a complementary tool to enhance the diagnostic precision. We aim to develop an explainable AI model to detect radiological signs of AHF in chest CT with an accuracy comparable to thoracic radiologists.
Methods: A single-center, retrospective study during 2016-2021 at Copenhagen University Hospital - Bispebjerg and Frederiksberg, Denmark. A Boosted Trees model was trained to predict AHF based on measurements of segmented cardiac and pulmonary structures from acute thoracic CT scans. Diagnostic labels for training and testing were extracted from radiology reports. Structures were segmented with TotalSegmentator. Shapley Additive explanations values were used to explain the impact of each measurement on the final prediction.
Results: Of the 4,672 subjects, 49% were female. The final model incorporated twelve key features of AHF and achieved an area under the ROC of 0.87 on the independent test set. Expert radiologist review of model misclassifications found that 24 out of 64 (38%) false positives and 24 out of 61 (39%) false negatives were actually correct model predictions, with the errors originating from inaccuracies in the initial radiology reports.
Conclusion: We developed an explainable AI model with strong discriminatory performance, comparable to thoracic radiologists. The AI model's stepwise, transparent predictions may support decision-making. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_08952 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Interpretable Artificial Intelligence for Detecting Acute Heart Failure on Acute Chest CT Scans Ørting, Silas Nyboe Miger, Kristina Olesen, Anne Sophie Overgaard Boesen, Mikael Ploug Andersen, Michael Brun Petersen, Jens Nielsen, Olav W. de Bruijne, Marleen Image and Video Processing Computer Vision and Pattern Recognition Introduction: Chest CT scans are increasingly used in dyspneic patients where acute heart failure (AHF) is a key differential diagnosis. Interpretation remains challenging and radiology reports are frequently delayed due to a radiologist shortage, although flagging such information for emergency physicians would have therapeutic implication. Artificial intelligence (AI) can be a complementary tool to enhance the diagnostic precision. We aim to develop an explainable AI model to detect radiological signs of AHF in chest CT with an accuracy comparable to thoracic radiologists. Methods: A single-center, retrospective study during 2016-2021 at Copenhagen University Hospital - Bispebjerg and Frederiksberg, Denmark. A Boosted Trees model was trained to predict AHF based on measurements of segmented cardiac and pulmonary structures from acute thoracic CT scans. Diagnostic labels for training and testing were extracted from radiology reports. Structures were segmented with TotalSegmentator. Shapley Additive explanations values were used to explain the impact of each measurement on the final prediction. Results: Of the 4,672 subjects, 49% were female. The final model incorporated twelve key features of AHF and achieved an area under the ROC of 0.87 on the independent test set. Expert radiologist review of model misclassifications found that 24 out of 64 (38%) false positives and 24 out of 61 (39%) false negatives were actually correct model predictions, with the errors originating from inaccuracies in the initial radiology reports. Conclusion: We developed an explainable AI model with strong discriminatory performance, comparable to thoracic radiologists. The AI model's stepwise, transparent predictions may support decision-making. |
| title | Interpretable Artificial Intelligence for Detecting Acute Heart Failure on Acute Chest CT Scans |
| topic | Image and Video Processing Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2507.08952 |