{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T17:26:56Z","timestamp":1788283616436,"version":"build-2803163510"},"reference-count":20,"publisher":"Oxford University Press (OUP)","issue":"3","license":[{"start":{"date-parts":[[2025,2,24]],"date-time":"2025-02-24T00:00:00Z","timestamp":1740355200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,5,20]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Aims<\/jats:title>\n                  <jats:p>Artificial intelligence (AI) methods are being used increasingly for the automated segmentation of cine cardiac magnetic resonance (CMR) imaging. However, these methods have been shown to be subject to race bias; i.e. they exhibit different levels of performance for different races depending on the (im)balance of the data used to train the AI model. In this paper, we investigate the source of this bias, seeking to understand its root cause(s).<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Methods and results<\/jats:title>\n                  <jats:p>We trained AI models to perform race classification on cine CMR images and\/or segmentations from White and Black subjects from the UK Biobank and found that the classification accuracy for images was higher than for segmentations. Interpretability methods showed that the models were primarily looking at non-heart regions. Cropping images tightly around the heart caused classification accuracy to drop to almost chance level. Visualizing the latent space of AI segmentation models showed that race information was encoded in the models. Training segmentation models using cropped images reduced but did not eliminate the bias. A number of possible confounders for the bias in segmentation model performance were identified for Black subjects but none for White subjects.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Conclusion<\/jats:title>\n                  <jats:p>Distributional differences between annotated CMR data of White and Black races, which can lead to bias in trained AI segmentation models, are predominantly image-based, not segmentation-based. Most of the differences occur in areas outside the heart, such as subcutaneous fat. These findings will be important for researchers investigating performance of AI models on different races.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/ehjdh\/ztaf008","type":"journal-article","created":{"date-parts":[[2025,2,24]],"date-time":"2025-02-24T17:54:11Z","timestamp":1740419651000},"page":"350-358","source":"Crossref","is-referenced-by-count":10,"title":["An investigation into the causes of race bias in artificial intelligence\u2013based cine cardiac magnetic resonance segmentation"],"prefix":"10.1093","volume":"6","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5180-8884","authenticated-orcid":false,"given":"Tiarna","family":"Lee","sequence":"first","affiliation":[{"name":"School of Biomedical Engineering & Imaging Sciences , King's College London ,","place":["UK"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9789-6629","authenticated-orcid":false,"given":"Esther","family":"Puyol-Ant\u00f3n","sequence":"additional","affiliation":[{"name":"School of Biomedical Engineering & Imaging Sciences , King's College London ,","place":["UK"]},{"name":"HeartFlow Inc , London ,","place":["UK"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bram","family":"Ruijsink","sequence":"additional","affiliation":[{"name":"Guy's and St Thomas' Hospital , London ,","place":["UK"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sebastien","family":"Roujol","sequence":"additional","affiliation":[{"name":"School of Biomedical Engineering & Imaging Sciences , King's College London ,","place":["UK"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Theodore","family":"Barfoot","sequence":"additional","affiliation":[{"name":"School of Biomedical Engineering & Imaging Sciences , King's College London ,","place":["UK"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shaheim","family":"Ogbomo-Harmitt","sequence":"additional","affiliation":[{"name":"School of Biomedical Engineering & Imaging Sciences , King's College London ,","place":["UK"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Miaojing","family":"Shi","sequence":"additional","affiliation":[{"name":"College of Electronic and Information Engineering , Tongji University ,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Andrew","family":"King","sequence":"additional","affiliation":[{"name":"School of Biomedical Engineering & Imaging Sciences , King's College London ,","place":["UK"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2025,2,24]]},"reference":[{"key":"2025052713284483300_ztaf008-B1","doi-asserted-by":"crossref","first-page":"370","DOI":"10.1093\/ehjdh\/ztad044","article-title":"An artificial intelligence tool for automated analysis of large-scale unstructured clinical cine cardiac magnetic resonance databases","volume":"4","author":"Mariscal-Harana","year":"2023","journal-title":"Eur Heart J Digit Health"},{"key":"2025052713284483300_ztaf008-B2","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1186\/s12968-022-00846-4","article-title":"Precision measurement of cardiac structure and function in cardiovascular magnetic resonance using machine learning","volume":"24","author":"Davies","year":"2022","journal-title":"J Cardiovasc Magn Reson"},{"key":"2025052713284483300_ztaf008-B3","doi-asserted-by":"crossref","first-page":"684","DOI":"10.1016\/j.jcmg.2019.05.030","article-title":"Fully automated, quality-controlled cardiac analysis from CMR: validation and large-scale application to characterize cardiac function","volume":"13","author":"Ruijsink","year":"2020","journal-title":"JACC Cardiovasc Imaging"},{"key":"2025052713284483300_ztaf008-B4","first-page":"413","author":"Puyol-Ant\u00f3n","year":"2021"},{"key":"2025052713284483300_ztaf008-B5","doi-asserted-by":"crossref","first-page":"859310","DOI":"10.3389\/fcvm.2022.859310","article-title":"Fairness in cardiac magnetic resonance imaging: assessing sex and racial bias in deep learning-based segmentation","volume":"9","author":"Puyol-Ant\u00f3n","year":"2022","journal-title":"Front Cardiovasc Med"},{"key":"2025052713284483300_ztaf008-B6","author":"Lee","year":"2022"},{"key":"2025052713284483300_ztaf008-B7","first-page":"215","volume-title":"Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)","author":"Lee","year":"2023"},{"key":"2025052713284483300_ztaf008-B8","doi-asserted-by":"crossref","first-page":"12592","DOI":"10.1073\/pnas.1919012117","article-title":"Gender imbalance in medical imaging datasets produces biased classifiers for computer-aided diagnosis","volume":"117","author":"Larrazabal","year":"2020","journal-title":"Proc Natl Acad Sci U S A"},{"key":"2025052713284483300_ztaf008-B9","doi-asserted-by":"crossref","first-page":"e406","DOI":"10.1016\/S2589-7500(22)00063-2","article-title":"AI recognition of patient race in medical imaging: a modelling study","volume":"4","author":"Gichoya","year":"2022","journal-title":"Lancet Digit Health"},{"key":"2025052713284483300_ztaf008-B10","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1186\/s12968-016-0227-4","article-title":"UK Biobank\u2019s cardiovascular magnetic resonance protocol","volume":"18","author":"Petersen","year":"2016","journal-title":"J Cardiovasc Magn Reson"},{"key":"2025052713284483300_ztaf008-B11","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1186\/s12968-017-0327-9","article-title":"Reference ranges for cardiac structure and function using cardiovascular magnetic resonance (CMR) in Caucasians from the UK Biobank population cohort","volume":"19","author":"Petersen","year":"2016","journal-title":"J Cardiovasc Magn Reson"},{"key":"2025052713284483300_ztaf008-B12","first-page":"770","volume-title":"Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition","author":"He","year":"2016"},{"key":"2025052713284483300_ztaf008-B13","doi-asserted-by":"crossref","first-page":"336","DOI":"10.1007\/s11263-019-01228-7","article-title":"Grad-CAM: visual explanations from deep networks via gradient-based localization","volume":"128","author":"Selvaraju","year":"2020","journal-title":"Int J Comput Vis"},{"key":"2025052713284483300_ztaf008-B14","doi-asserted-by":"crossref","first-page":"203","DOI":"10.1038\/s41592-020-01008-z","article-title":"nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation","volume":"18","author":"Isensee","year":"2020","journal-title":"Nat Methods"},{"key":"2025052713284483300_ztaf008-B15","doi-asserted-by":"crossref","first-page":"104467","DOI":"10.1016\/j.ebiom.2023.104467","article-title":"Algorithmic encoding of protected characteristics in chest X-ray disease detection models","volume":"89","author":"Glocker","year":"2023","journal-title":"EBioMedicine"},{"key":"2025052713284483300_ztaf008-B16","doi-asserted-by":"crossref","first-page":"20150987","DOI":"10.1259\/bjr.20150987","article-title":"Current artefacts in cardiac and chest magnetic resonance imaging: tips and tricks","volume":"89","author":"Alfudhili","year":"2016","journal-title":"Br J Radiol"},{"key":"2025052713284483300_ztaf008-B17","doi-asserted-by":"crossref","first-page":"1392","DOI":"10.1093\/ajcn\/71.6.1392","article-title":"Measures of body composition in blacks and whites: a comparative review","volume":"71","author":"Wagner","year":"2000","journal-title":"Am J Clin Nutr"},{"key":"2025052713284483300_ztaf008-B18","doi-asserted-by":"crossref","first-page":"770","DOI":"10.1093\/ajh\/hpx093","article-title":"Differences in cardiac structure and function between black and white patients: another step in the evaluation of cardiovascular risk in chronic kidney disease","volume":"30","author":"Nardi","year":"2017","journal-title":"Am J Hypertens"},{"key":"2025052713284483300_ztaf008-B19","doi-asserted-by":"crossref","first-page":"386","DOI":"10.1109\/TPAMI.2018.2844175","article-title":"Mask R-CNN","volume":"42","author":"He","year":"2017","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"2025052713284483300_ztaf008-B20","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.1908.09635","article-title":"A survey on bias and fairness in machine learning","author":"Mehrabi","year":"2019","journal-title":"ArXiv"}],"container-title":["European Heart Journal - Digital Health"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/ehjdh\/advance-article-pdf\/doi\/10.1093\/ehjdh\/ztaf008\/62113043\/ztaf008.pdf","content-type":"application\/pdf","content-version":"am","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/ehjdh\/article-pdf\/6\/3\/350\/62113043\/ztaf008.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/ehjdh\/article-pdf\/6\/3\/350\/62113043\/ztaf008.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,5,27]],"date-time":"2025-05-27T17:28:54Z","timestamp":1748366934000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/ehjdh\/article\/6\/3\/350\/8038011"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,2,24]]},"references-count":20,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2025,2,24]]},"published-print":{"date-parts":[[2025,5,20]]}},"URL":"https:\/\/doi.org\/10.1093\/ehjdh\/ztaf008","relation":{},"ISSN":["2634-3916"],"issn-type":[{"value":"2634-3916","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2025,5]]},"published":{"date-parts":[[2025,2,24]]}}}