Mushcab Hayat, Al Ramis Mohammed, AlRujaib Abdulrahman, Eskandarani Rawan, Sunbul Tamara, AlOtaibi Anwar, Obaidan Mohammed, Al Harbi Reman, Aljabri Duaa
Research Office, Johns Hopkins Aramco Healthcare, Medical Access Road 1, Dhahran, Saudi Arabia, 966 556373411.
College of Medicine, University College Dublin, Dublin, Ireland.
JMIR Cancer. 2025 May 9;11:e63964. doi: 10.2196/63964.
Artificial 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.
This 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.
We 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.
The 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.
Our 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.
人工智能（AI）是一种革命性工具，但尚未完全融入包括医学成像在内的多个医疗保健领域。人工智能可以改变医学成像的实施和解读方式，尤其是在心脏肿瘤学领域。
本研究旨在系统回顾关于人工智能在心脏肿瘤学成像中用于预测心脏毒性的现有文献，并描述如果人工智能成功应用于常规实践，不同成像方式可能实现的改进。
我们使用人工智能研究助手工具（Elicit）在PubMed、Ovid MEDLINE、Cochrane图书馆、CINAHL和谷歌学术搜索数据库，检索从创建至2023年的原始研究，这些研究报告了成年癌症患者接受心脏毒性评估时的人工智能结果。结果包括心脏毒性发生率、左心室射血分数、与心脏毒性相关的危险因素、心力衰竭、心肌功能障碍、癌症治疗相关心血管毒性的体征、超声心动图和心脏磁共振成像。记录每项研究的描述性信息，包括成像技术、人工智能模型、结果和局限性。
系统检索产生了2018年至2023年间进行的7项研究，均纳入本综述。这些研究大多在美国进行（71%），纳入了乳腺癌患者（86%），并使用磁共振成像作为成像方式（57%）。研究的质量评估在该工具的所有部分平均符合率为86%。总之，本系统综述证明了人工智能在增强心脏肿瘤学成像以预测癌症患者心脏毒性方面的潜力。
我们的研究结果表明，人工智能可以提高心脏毒性评估的准确性和效率。然而，需要通过更大规模的多中心试验进行进一步研究，以验证这些应用并完善人工智能技术以供常规使用，为改善有心脏毒性风险的癌症幸存者的患者预后铺平道路。
