Application of Artificial Intelligence in Cardio-Oncology Imaging for Cancer Therapy–Related Cardiovascular Toxicity: Systematic Review
Abstract BackgroundArtificial intelligence (AI) is a revolutionary tool yet to be fully integrated into several health care sectors, including medical imaging. AI can transform how medical imaging is conducted and interpreted, especially in cardio-oncology. Objecti...
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| Format: | Article |
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JMIR Publications
2025-05-01
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| Series: | JMIR Cancer |
| Online Access: | https://cancer.jmir.org/2025/1/e63964 |
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| author | Hayat Mushcab Mohammed Al Ramis Abdulrahman AlRujaib Rawan Eskandarani Tamara Sunbul Anwar AlOtaibi Mohammed Obaidan Reman Al Harbi Duaa Aljabri |
| author_facet | Hayat Mushcab Mohammed Al Ramis Abdulrahman AlRujaib Rawan Eskandarani Tamara Sunbul Anwar AlOtaibi Mohammed Obaidan Reman Al Harbi Duaa Aljabri |
| author_sort | Hayat Mushcab |
| collection | DOAJ |
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Abstract
BackgroundArtificial intelligence (AI) is a revolutionary tool yet to be fully integrated into several health care sectors, including medical imaging. AI can transform how medical imaging is conducted and interpreted, especially in cardio-oncology.
ObjectiveThis study aims to systematically review the available literature on the use of AI in cardio-oncology imaging to predict cardiotoxicity and describe the possible improvement of different imaging modalities that can be achieved if AI is successfully deployed to routine practice.
MethodsWe conducted a database search in PubMed, Ovid MEDLINE, Cochrane Library, CINAHL, and Google Scholar from inception to 2023 using the AI research assistant tool (Elicit) to search for original studies reporting AI outcomes in adult patients diagnosed with any cancer and undergoing cardiotoxicity assessment. Outcomes included incidence of cardiotoxicity, left ventricular ejection fraction, risk factors associated with cardiotoxicity, heart failure, myocardial dysfunction, signs of cancer therapy–related cardiovascular toxicity, echocardiography, and cardiac magnetic resonance imaging. Descriptive information about each study was recorded, including imaging technique, AI model, outcomes, and limitations.
ResultsThe systematic search resulted in 7 studies conducted between 2018 and 2023, which are included in this review. Most of these studies were conducted in the United States (71%), included patients with breast cancer (86%), and used magnetic resonance imaging as the imaging modality (57%). The quality assessment of the studies had an average of 86% compliance in all of the tool’s sections. In conclusion, this systematic review demonstrates the potential of AI to enhance cardio-oncology imaging for predicting cardiotoxicity in patients with cancer.
ConclusionsOur findings suggest that AI can enhance the accuracy and efficiency of cardiotoxicity assessments. However, further research through larger, multicenter trials is needed to validate these applications and refine AI technologies for routine use, paving the way for improved patient outcomes in cancer survivors at risk of cardiotoxicity. |
| format | Article |
| id | doaj-art-80bf5a2f42a14f209ea4aea0044ea7ce |
| institution | OA Journals |
| issn | 2369-1999 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | JMIR Publications |
| record_format | Article |
| series | JMIR Cancer |
| spelling | doaj-art-80bf5a2f42a14f209ea4aea0044ea7ce2025-08-20T01:51:00ZengJMIR PublicationsJMIR Cancer2369-19992025-05-0111e63964e6396410.2196/63964Application of Artificial Intelligence in Cardio-Oncology Imaging for Cancer Therapy–Related Cardiovascular Toxicity: Systematic ReviewHayat Mushcabhttp://orcid.org/0000-0003-4398-7020Mohammed Al Ramishttp://orcid.org/0009-0003-3616-6694Abdulrahman AlRujaibhttp://orcid.org/0000-0002-1729-9091Rawan Eskandaranihttp://orcid.org/0000-0003-2055-0053Tamara Sunbulhttp://orcid.org/0009-0003-2785-6618Anwar AlOtaibihttp://orcid.org/0000-0003-4827-0731Mohammed Obaidanhttp://orcid.org/0009-0003-8496-2804Reman Al Harbihttp://orcid.org/0009-0006-1782-2176Duaa Aljabrihttp://orcid.org/0000-0002-2161-9932 Abstract BackgroundArtificial intelligence (AI) is a revolutionary tool yet to be fully integrated into several health care sectors, including medical imaging. AI can transform how medical imaging is conducted and interpreted, especially in cardio-oncology. ObjectiveThis study aims to systematically review the available literature on the use of AI in cardio-oncology imaging to predict cardiotoxicity and describe the possible improvement of different imaging modalities that can be achieved if AI is successfully deployed to routine practice. MethodsWe conducted a database search in PubMed, Ovid MEDLINE, Cochrane Library, CINAHL, and Google Scholar from inception to 2023 using the AI research assistant tool (Elicit) to search for original studies reporting AI outcomes in adult patients diagnosed with any cancer and undergoing cardiotoxicity assessment. Outcomes included incidence of cardiotoxicity, left ventricular ejection fraction, risk factors associated with cardiotoxicity, heart failure, myocardial dysfunction, signs of cancer therapy–related cardiovascular toxicity, echocardiography, and cardiac magnetic resonance imaging. Descriptive information about each study was recorded, including imaging technique, AI model, outcomes, and limitations. ResultsThe systematic search resulted in 7 studies conducted between 2018 and 2023, which are included in this review. Most of these studies were conducted in the United States (71%), included patients with breast cancer (86%), and used magnetic resonance imaging as the imaging modality (57%). The quality assessment of the studies had an average of 86% compliance in all of the tool’s sections. In conclusion, this systematic review demonstrates the potential of AI to enhance cardio-oncology imaging for predicting cardiotoxicity in patients with cancer. ConclusionsOur findings suggest that AI can enhance the accuracy and efficiency of cardiotoxicity assessments. However, further research through larger, multicenter trials is needed to validate these applications and refine AI technologies for routine use, paving the way for improved patient outcomes in cancer survivors at risk of cardiotoxicity.https://cancer.jmir.org/2025/1/e63964 |
| spellingShingle | Hayat Mushcab Mohammed Al Ramis Abdulrahman AlRujaib Rawan Eskandarani Tamara Sunbul Anwar AlOtaibi Mohammed Obaidan Reman Al Harbi Duaa Aljabri Application of Artificial Intelligence in Cardio-Oncology Imaging for Cancer Therapy–Related Cardiovascular Toxicity: Systematic Review JMIR Cancer |
| title | Application of Artificial Intelligence in Cardio-Oncology Imaging for Cancer Therapy–Related Cardiovascular Toxicity: Systematic Review |
| title_full | Application of Artificial Intelligence in Cardio-Oncology Imaging for Cancer Therapy–Related Cardiovascular Toxicity: Systematic Review |
| title_fullStr | Application of Artificial Intelligence in Cardio-Oncology Imaging for Cancer Therapy–Related Cardiovascular Toxicity: Systematic Review |
| title_full_unstemmed | Application of Artificial Intelligence in Cardio-Oncology Imaging for Cancer Therapy–Related Cardiovascular Toxicity: Systematic Review |
| title_short | Application of Artificial Intelligence in Cardio-Oncology Imaging for Cancer Therapy–Related Cardiovascular Toxicity: Systematic Review |
| title_sort | application of artificial intelligence in cardio oncology imaging for cancer therapy related cardiovascular toxicity systematic review |
| url | https://cancer.jmir.org/2025/1/e63964 |
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