AI recognition of patient race in medical imaging: a modelling study.

BACKGROUND: Previous studies in medical imaging have shown disparate abilities of artificial intelligence (AI) to detect a person's race, yet there is no known correlation for race on medical imaging that would be obvious to human experts when interpreting the images. We aimed to conduct a comprehensive evaluation of the ability of AI to recognise a patient's racial identity from medical images. METHODS: Using private (Emory CXR, Emory Chest CT, Emory Cervical Spine, and Emory Mammogram) and public (MIMIC-CXR, CheXpert, National Lung Cancer Screening Trial, RSNA Pulmonary Embolism CT, and Digital Hand Atlas) datasets, we evaluated, first, performance quantification of deep learning models in detecting race from medical images, including the ability of these models to generalise to external environments and across multiple imaging modalities. Second, we assessed possible confounding of anatomic and phenotypic population features by assessing the ability of these hypothesised confounders to detect race in isolation using regression models, and by re-evaluating the deep learning models by testing them on datasets stratified by these hypothesised confounding variables. Last, by exploring the effect of image corruptions on model performance, we investigated the underlying mechanism by which AI models can recognise race. FINDINGS: In our study, we show that standard AI deep learning models can be trained to predict race from medical images with high performance across multiple imaging modalities, which was sustained under external validation conditions (x-ray imaging [area under the receiver operating characteristics curve (AUC) range 0·91-0·99], CT chest imaging [0·87-0·96], and mammography [0·81]). We also showed that this detection is not due to proxies or imaging-related surrogate covariates for race (eg, performance of possible confounders: body-mass index [AUC 0·55], disease distribution [0·61], and breast density [0·61]). Finally, we provide evidence to show that the ability of AI deep learning models persisted over all anatomical regions and frequency spectrums of the images, suggesting the efforts to control this behaviour when it is undesirable will be challenging and demand further study. INTERPRETATION: The results from our study emphasise that the ability of AI deep learning models to predict self-reported race is itself not the issue of importance. However, our finding that AI can accurately predict self-reported race, even from corrupted, cropped, and noised medical images, often when clinical experts cannot, creates an enormous risk for all model deployments in medical imaging. FUNDING: National Institute of Biomedical Imaging and Bioengineering, MIDRC grant of National Institutes of Health, US National Science Foundation, National Library of Medicine of the National Institutes of Health, and Taiwan Ministry of Science and Technology.

Cardiac imaging training in radiology residency programs: a survey of radiology chief residents.

RATIONALE AND OBJECTIVES: Comprehensive training in cardiac imaging during radiology residency is imperative if radiologists are to maintain a significant role in this rapidly growing field. In this study, radiology chief residents were surveyed to assess the current status of cardiac imaging training in radiology residency programs. The responses to this survey may be helpful in understanding current trends in cardiac imaging training and how such training can be improved in the future. MATERIALS AND METHODS: Chief residents at accredited radiology residency programs were sent an e-mail with a link to a 17-question Web-based survey. The survey assessed the organization of cardiac imaging training in each residency program, imaging modalities incorporated into cardiac imaging training, the role of residents on cardiac imaging rotations, and attitudes of residents about their cardiac imaging training and the future of cardiac imaging. RESULTS: Responses were obtained from 52 of 112 (46%) programs. Seventy-one percent had at least one dedicated cardiac imaging rotation during their residencies. Fifty-two percent and 62% of respondents reported <5 hours of cardiac imaging-related case conferences and didactic lectures per year, respectively. Most had cardiac computed tomography or magnetic resonance imaging incorporated into their cardiac imaging training. Although 92% felt that cardiac imaging training is important, only 17% felt that they currently received adequate training in cardiac imaging. CONCLUSIONS: The majority of residency programs represented in this survey had at least one dedicated cardiac imaging rotation for their residents. Most of these programs had few cardiac imaging-related conferences and lectures per year. Although most chief residents believed that cardiac imaging training is important, only a minority felt that they currently received adequate training in cardiac imaging.

Biliary dilatation and duodenal intussusception secondary to enteric duplication cyst: MDCT diagnosis.

Enteric duplication cysts are rare congenital anomalies that may occur anywhere along the gastrointestinal tract, most commonly involving the small bowel. The distal ileum, jejunum, and duodenum are affected in descending order of frequency. We describe a case of biliary dilatation and duodenal intussusception caused by an enteric duplication cyst in an adult patient. To our knowledge, there are no other reported cases of this entity in an adult in the English literature. Multidetector computed tomography (MDCT) findings are emphasized, and the value of multiplanar reformation (MPR) in forming a correct preoperative differential diagnosis is discussed.