Artificial Intelligence-Based Prediction of Cardiovascular Diseases from Chest Radiography
Chest radiography (CXR) is the most frequently performed radiological test worldwide because of its wide availability, non-invasive nature, and low cost. The ability of CXR to diagnose cardiovascular diseases, give insight into cardiac function, and predict cardiovascular events is often underutiliz...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-10-01
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| Series: | Journal of Imaging |
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| Online Access: | https://www.mdpi.com/2313-433X/9/11/236 |
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| author | Juan M. Farina Milagros Pereyra Ahmed K. Mahmoud Isabel G. Scalia Mohammed Tiseer Abbas Chieh-Ju Chao Timothy Barry Chadi Ayoub Imon Banerjee Reza Arsanjani |
| author_facet | Juan M. Farina Milagros Pereyra Ahmed K. Mahmoud Isabel G. Scalia Mohammed Tiseer Abbas Chieh-Ju Chao Timothy Barry Chadi Ayoub Imon Banerjee Reza Arsanjani |
| author_sort | Juan M. Farina |
| collection | DOAJ |
| description | Chest radiography (CXR) is the most frequently performed radiological test worldwide because of its wide availability, non-invasive nature, and low cost. The ability of CXR to diagnose cardiovascular diseases, give insight into cardiac function, and predict cardiovascular events is often underutilized, not clearly understood, and affected by inter- and intra-observer variability. Therefore, more sophisticated tests are generally needed to assess cardiovascular diseases. Considering the sustained increase in the incidence of cardiovascular diseases, it is critical to find accessible, fast, and reproducible tests to help diagnose these frequent conditions. The expanded focus on the application of artificial intelligence (AI) with respect to diagnostic cardiovascular imaging has also been applied to CXR, with several publications suggesting that AI models can be trained to detect cardiovascular conditions by identifying features in the CXR. Multiple models have been developed to predict mortality, cardiovascular morphology and function, coronary artery disease, valvular heart diseases, aortic diseases, arrhythmias, pulmonary hypertension, and heart failure. The available evidence demonstrates that the use of AI-based tools applied to CXR for the diagnosis of cardiovascular conditions and prognostication has the potential to transform clinical care. AI-analyzed CXRs could be utilized in the future as a complimentary, easy-to-apply technology to improve diagnosis and risk stratification for cardiovascular diseases. Such advances will likely help better target more advanced investigations, which may reduce the burden of testing in some cases, as well as better identify higher-risk patients who would benefit from earlier, dedicated, and comprehensive cardiovascular evaluation. |
| first_indexed | 2024-03-09T16:43:30Z |
| format | Article |
| id | doaj.art-ea82cebd80784bd689853f0b56244b51 |
| institution | Directory Open Access Journal |
| issn | 2313-433X |
| language | English |
| last_indexed | 2024-03-09T16:43:30Z |
| publishDate | 2023-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Imaging |
| spelling | doaj.art-ea82cebd80784bd689853f0b56244b512023-11-24T14:50:03ZengMDPI AGJournal of Imaging2313-433X2023-10-0191123610.3390/jimaging9110236Artificial Intelligence-Based Prediction of Cardiovascular Diseases from Chest RadiographyJuan M. Farina0Milagros Pereyra1Ahmed K. Mahmoud2Isabel G. Scalia3Mohammed Tiseer Abbas4Chieh-Ju Chao5Timothy Barry6Chadi Ayoub7Imon Banerjee8Reza Arsanjani9Department of Cardiovascular Medicine, Mayo Clinic, Phoenix, AZ 85054, USADepartment of Cardiovascular Medicine, Mayo Clinic, Phoenix, AZ 85054, USADepartment of Cardiovascular Medicine, Mayo Clinic, Phoenix, AZ 85054, USADepartment of Cardiovascular Medicine, Mayo Clinic, Phoenix, AZ 85054, USADepartment of Cardiovascular Medicine, Mayo Clinic, Phoenix, AZ 85054, USADepartment of Cardiovascular Medicine, Mayo Clinic, Rochester, MN 55905, USADepartment of Cardiovascular Medicine, Mayo Clinic, Phoenix, AZ 85054, USADepartment of Cardiovascular Medicine, Mayo Clinic, Phoenix, AZ 85054, USADepartment of Radiology, Mayo Clinic, Phoenix, AZ 85054, USADepartment of Cardiovascular Medicine, Mayo Clinic, Phoenix, AZ 85054, USAChest radiography (CXR) is the most frequently performed radiological test worldwide because of its wide availability, non-invasive nature, and low cost. The ability of CXR to diagnose cardiovascular diseases, give insight into cardiac function, and predict cardiovascular events is often underutilized, not clearly understood, and affected by inter- and intra-observer variability. Therefore, more sophisticated tests are generally needed to assess cardiovascular diseases. Considering the sustained increase in the incidence of cardiovascular diseases, it is critical to find accessible, fast, and reproducible tests to help diagnose these frequent conditions. The expanded focus on the application of artificial intelligence (AI) with respect to diagnostic cardiovascular imaging has also been applied to CXR, with several publications suggesting that AI models can be trained to detect cardiovascular conditions by identifying features in the CXR. Multiple models have been developed to predict mortality, cardiovascular morphology and function, coronary artery disease, valvular heart diseases, aortic diseases, arrhythmias, pulmonary hypertension, and heart failure. The available evidence demonstrates that the use of AI-based tools applied to CXR for the diagnosis of cardiovascular conditions and prognostication has the potential to transform clinical care. AI-analyzed CXRs could be utilized in the future as a complimentary, easy-to-apply technology to improve diagnosis and risk stratification for cardiovascular diseases. Such advances will likely help better target more advanced investigations, which may reduce the burden of testing in some cases, as well as better identify higher-risk patients who would benefit from earlier, dedicated, and comprehensive cardiovascular evaluation.https://www.mdpi.com/2313-433X/9/11/236artificial intelligencechest radiographycardiovascular diseasesdeep learning |
| spellingShingle | Juan M. Farina Milagros Pereyra Ahmed K. Mahmoud Isabel G. Scalia Mohammed Tiseer Abbas Chieh-Ju Chao Timothy Barry Chadi Ayoub Imon Banerjee Reza Arsanjani Artificial Intelligence-Based Prediction of Cardiovascular Diseases from Chest Radiography Journal of Imaging artificial intelligence chest radiography cardiovascular diseases deep learning |
| title | Artificial Intelligence-Based Prediction of Cardiovascular Diseases from Chest Radiography |
| title_full | Artificial Intelligence-Based Prediction of Cardiovascular Diseases from Chest Radiography |
| title_fullStr | Artificial Intelligence-Based Prediction of Cardiovascular Diseases from Chest Radiography |
| title_full_unstemmed | Artificial Intelligence-Based Prediction of Cardiovascular Diseases from Chest Radiography |
| title_short | Artificial Intelligence-Based Prediction of Cardiovascular Diseases from Chest Radiography |
| title_sort | artificial intelligence based prediction of cardiovascular diseases from chest radiography |
| topic | artificial intelligence chest radiography cardiovascular diseases deep learning |
| url | https://www.mdpi.com/2313-433X/9/11/236 |
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