Abstract
Machine learning offers great opportunities to streamline and improve clinical care from the perspective of cardiac imagers, patients, and the industry and is a very active scientific research field. In light of these advances, the European Society of Cardiovascular Radiology (ESCR), a non-profit medical society dedicated to advancing cardiovascular radiology, has assembled a position statement regarding the use of machine learning (ML) in cardiovascular imaging. The purpose of this statement is to provide guidance on requirements for successful development and implementation of ML applications in cardiovascular imaging. In particular, recommendations on how to adequately design ML studies and how to report and interpret their results are provided. Finally, we identify opportunities and challenges ahead. While the focus of this position statement is ML development in cardiovascular imaging, most considerations are relevant to ML in radiology in general.Key Points• Development and clinical implementation of machine learning in cardiovascular imaging is a multidisciplinary pursuit.• Based on existing study quality standard frameworks such as SPIRIT and STARD, we propose a list of quality criteria for ML studies in radiology.• The cardiovascular imaging research community should strive for the compilation of multicenter datasets for the development, evaluation, and benchmarking of ML algorithms.
Highlights
Artificial intelligence (AI), machine learning (ML), and deep learning (DL) are currently getting a lot of attention in the public arena and in science [1]
Applications of ML extend beyond image analysis and can support many other tasks within the field of radiology such as triage of exams according to urgency or provision of a second reading to avoid missing relevant findings. They can help with predicting outcomes and extending the diagnostic capabilities of CT and MRI, e.g., by assessing the fractional flow reserve from cardiac CT angiography. In light of these advances, the European Society of Cardiovascular Radiology (ESCR), a non-profit medical society dedicated to advancing cardiovascular radiology, has assembled a position statement regarding the use of ML in cardiovascular imaging in close cooperation with other leading societies in the field
Based on existing study quality standard frameworks such as Standard Protocol Items: Recommendations for Interventional Trials (SPIRIT) and STARD, we propose a list of quality criteria for ML studies in radiology
Summary
Artificial intelligence (AI), machine learning (ML), and deep learning (DL) are currently getting a lot of attention in the public arena and in science [1]. If assessed as an effective solution to the clinical problem, translation into clinical practice follows This last step is at least as challenging as all previous steps and requires expertise in fields that are rarely covered by medical and ML experts, namely in user interface design, graphic design, regulatory matters, and in assuring compatibility with existing hospital IT environments that are subject to changes over time and location. Anonymization tools are important for ML projects in radiology, because sensitive patient information is part of the DICOM header of each image and data exchange is needed to build large databases with studies from multiple centers
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