"Shortcuts" Causing Bias in Radiology Artificial Intelligence: Causes, Evaluation, and Mitigation.

Despite the expert-level performance of artificial intelligence (AI) models for various medical imaging tasks, real-world performance failures with disparate outputs for various subgroups limit the usefulness of AI in improving patients' lives. Many definitions of fairness have been proposed, with discussions of various tensions that arise in the choice of an appropriate metric to use to evaluate bias; for example, should one aim for individual or group fairness? One central observation is that AI models apply "shortcut learning" whereby spurious features (such as chest tubes and portable radiographic markers on intensive care unit chest radiography) on medical images are used for prediction instead of identifying true pathology. Moreover, AI has been shown to have a remarkable ability to detect protected attributes of age, sex, and race, while the same models demonstrate bias against historically underserved subgroups of age, sex, and race in disease diagnosis. Therefore, an AI model may take shortcut predictions from these correlations and subsequently generate an outcome that is biased toward certain subgroups even when protected attributes are not explicitly used as inputs into the model. As a result, these subgroups became nonprivileged subgroups. In this review, the authors discuss the various types of bias from shortcut learning that may occur at different phases of AI model development, including data bias, modeling bias, and inference bias. The authors thereafter summarize various tool kits that can be used to evaluate and mitigate bias and note that these have largely been applied to nonmedical domains and require more evaluation for medical AI. The authors then summarize current techniques for mitigating bias from preprocessing (data-centric solutions) and during model development (computational solutions) and postprocessing (recalibration of learning). Ongoing legal changes where the use of a biased model will be penalized highlight the necessity of understanding, detecting, and mitigating biases from shortcut learning and will require diverse research teams looking at the whole AI pipeline.

Evaluation of federated learning variations for COVID-19 diagnosis using chest radiographs from 42 US and European hospitals.

OBJECTIVE: Federated learning (FL) allows multiple distributed data holders to collaboratively learn a shared model without data sharing. However, individual health system data are heterogeneous. "Personalized" FL variations have been developed to counter data heterogeneity, but few have been evaluated using real-world healthcare data. The purpose of this study is to investigate the performance of a single-site versus a 3-client federated model using a previously described Coronavirus Disease 19 (COVID-19) diagnostic model. Additionally, to investigate the effect of system heterogeneity, we evaluate the performance of 4 FL variations. MATERIALS AND METHODS: We leverage a FL healthcare collaborative including data from 5 international healthcare systems (US and Europe) encompassing 42 hospitals. We implemented a COVID-19 computer vision diagnosis system using the Federated Averaging (FedAvg) algorithm implemented on Clara Train SDK 4.0. To study the effect of data heterogeneity, training data was pooled from 3 systems locally and federation was simulated. We compared a centralized/pooled model, versus FedAvg, and 3 personalized FL variations (FedProx, FedBN, and FedAMP). RESULTS: We observed comparable model performance with respect to internal validation (local model: AUROC 0.94 vs FedAvg: 0.95, P = .5) and improved model generalizability with the FedAvg model (P < .05). When investigating the effects of model heterogeneity, we observed poor performance with FedAvg on internal validation as compared to personalized FL algorithms. FedAvg did have improved generalizability compared to personalized FL algorithms. On average, FedBN had the best rank performance on internal and external validation. CONCLUSION: FedAvg can significantly improve the generalization of the model compared to other personalization FL algorithms; however, at the cost of poor internal validity. Personalized FL may offer an opportunity to develop both internal and externally validated algorithms.

Performance of a Chest Radiograph AI Diagnostic Tool for COVID-19: A Prospective Observational Study.

Purpose: To conduct a prospective observational study across 12 U.S. hospitals to evaluate real-time performance of an interpretable artificial intelligence (AI) model to detect COVID-19 on chest radiographs. Materials and Methods: A total of 95 363 chest radiographs were included in model training, external validation, and real-time validation. The model was deployed as a clinical decision support system, and performance was prospectively evaluated. There were 5335 total real-time predictions and a COVID-19 prevalence of 4.8% (258 of 5335). Model performance was assessed with use of receiver operating characteristic analysis, precision-recall curves, and F1 score. Logistic regression was used to evaluate the association of race and sex with AI model diagnostic accuracy. To compare model accuracy with the performance of board-certified radiologists, a third dataset of 1638 images was read independently by two radiologists. Results: Participants positive for COVID-19 had higher COVID-19 diagnostic scores than participants negative for COVID-19 (median, 0.1 [IQR, 0.0-0.8] vs 0.0 [IQR, 0.0-0.1], respectively; P < .001). Real-time model performance was unchanged over 19 weeks of implementation (area under the receiver operating characteristic curve, 0.70; 95% CI: 0.66, 0.73). Model sensitivity was higher in men than women (P = .01), whereas model specificity was higher in women (P = .001). Sensitivity was higher for Asian (P = .002) and Black (P = .046) participants compared with White participants. The COVID-19 AI diagnostic system had worse accuracy (63.5% correct) compared with radiologist predictions (radiologist 1 = 67.8% correct, radiologist 2 = 68.6% correct; McNemar P < .001 for both). Conclusion: AI-based tools have not yet reached full diagnostic potential for COVID-19 and underperform compared with radiologist prediction.Keywords: Diagnosis, Classification, Application Domain, Infection, Lung Supplemental material is available for this article.. © RSNA, 2022.

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.

Enhancing cancer prevention and survivorship care with a videoconferencing model for continuing education: a mixed-methods study to identify barriers and incentives to participation.

OBJECTIVE: To enhance cancer prevention and survivorship care by local health care providers, a school of public health introduced an innovative telelearning continuing education program using the Extension for Community Healthcare Outcomes (ECHO) model. In ECHO's hub and spoke structure, synchronous videoconferencing connects frontline health professionals at various locations ("spokes") with experts at the facilitation center ("hub"). Sessions include experts' didactic presentations and case discussions led by spoke site participants. The objective of this study was to gain a better understanding of the reasons individuals choose or decline to participate in the Cancer ECHO program and to identify incentives and barriers to doing so. MATERIALS AND METHODS: Study participants were recruited from the hub team, spoke site participants, and providers who attended another ECHO program but not this one. Participants chose to take a survey or be interviewed. The Consolidated Framework for Implementation Research guided qualitative data coding and analysis. RESULTS: We conducted 22 semistructured interviews and collected 30 surveys. Incentives identified included the program's high-quality design, supportive learning climate, and access to information. Barriers included a lack of external incentives to participate and limited time available. Participants wanted more adaptability in program timing to fit providers' busy schedules. CONCLUSION: Although the merits of the Cancer ECHO program were widely acknowledged, adaptations to facilitate participation and emphasize the program's benefits may help overcome barriers to attending. As the number of telelearning programs grows, the results of this study point to ways to expand participation and spread health benefits more widely.

A Prospective Observational Study to Investigate Performance of a Chest X-ray Artificial Intelligence Diagnostic Support Tool Across 12 U.S. Hospitals.

Importance: An artificial intelligence (AI)-based model to predict COVID-19 likelihood from chest x-ray (CXR) findings can serve as an important adjunct to accelerate immediate clinical decision making and improve clinical decision making. Despite significant efforts, many limitations and biases exist in previously developed AI diagnostic models for COVID-19. Utilizing a large set of local and international CXR images, we developed an AI model with high performance on temporal and external validation. Objective: Investigate real-time performance of an AI-enabled COVID-19 diagnostic support system across a 12-hospital system. Design: Prospective observational study. Setting: Labeled frontal CXR images (samples of COVID-19 and non-COVID-19) from the M Health Fairview (Minnesota, USA), Valencian Region Medical ImageBank (Spain), MIMIC-CXR, Open-I 2013 Chest X-ray Collection, GitHub COVID-19 Image Data Collection (International), Indiana University (Indiana, USA), and Emory University (Georgia, USA). Participants: Internal (training, temporal, and real-time validation): 51,592 CXRs; Public: 27,424 CXRs; External (Indiana University): 10,002 CXRs; External (Emory University): 2002 CXRs. Main Outcome and Measure: Model performance assessed via receiver operating characteristic (ROC), Precision-Recall curves, and F1 score. Results: Patients that were COVID-19 positive had significantly higher COVID-19 Diagnostic Scores (median .1 [IQR: 0.0-0.8] vs median 0.0 [IQR: 0.0-0.1], p < 0.001) than patients that were COVID-19 negative. Pre-implementation the AI-model performed well on temporal validation (AUROC 0.8) and external validation (AUROC 0.76 at Indiana U, AUROC 0.72 at Emory U). The model was noted to have unrealistic performance (AUROC > 0.95) using publicly available databases. Real-time model performance was unchanged over 19 weeks of implementation (AUROC 0.70). On subgroup analysis, the model had improved discrimination for patients with "severe" as compared to "mild or moderate" disease, p < 0.001. Model performance was highest in Asians and lowest in whites and similar between males and females. Conclusions and Relevance: AI-based diagnostic tools may serve as an adjunct, but not replacement, for clinical decision support of COVID-19 diagnosis, which largely hinges on exposure history, signs, and symptoms. While AI-based tools have not yet reached full diagnostic potential in COVID-19, they may still offer valuable information to clinicians taken into consideration along with clinical signs and symptoms.

Just in Time Radiology Decision Support Using Real-time Data Feeds.

Ready access to relevant real-time information in medical imaging offers several potential benefits. Knowing both when important information will be available and that important information is available can facilitate optimization of workflow and management of time. Unexpected findings, as well as deficiencies in reporting and documentation, can be immediately managed. Herein, we present our experience developing and implementing a real-time web-centric dashboard system for radiologists, clinicians, and support staff. The dashboards are driven by multi-sourced HL7 message streams that are monitored, analyzed, aggregated, and transformed into multiple real-time displays to improve operations within our department. We call this framework Pipeline. Ruby on Rails, JavaScript, HTML, and SQL serve as the foundations of the Pipeline application. HL7 messages are processed in real-time by a Mirth interface engine which posts exam data into SQL. Users utilize web browsers to visit the Ruby on Rails-based dashboards on any device connected to our hospital network. The dashboards will automatically refresh every 30 seconds using JavaScript. The Pipeline application has been well received by clinicians and radiologists.

Characterization of the nasal, sublingual, and oropharyngeal mucosa microbiota in cleft lip and palate individuals before and after surgical repair.

OBJECTIVE: To delineate inherent differences in the microbial milieu in cleft palate patients compared with cleft lip patients and to document changes in microbial flora before and after cleft lip and palate repair. DESIGN: A prospective study of preoperative and postoperative culture results from the nasal, sublingual, and oropharyngeal surfaces of patients undergoing primary cleft lip repair and palate closure. SETTING: Shriners Hospitals for Children, Galveston, Texas, and University of Texas Medical Branch, Galveston, Texas. PATIENTS: Seventy-nine patients were included in a 3-year period. Ten patients with isolated cleft lip underwent primary lip repair. Twenty-five patients with cleft lip and palate underwent primary lip repair, and 44 patients underwent palatoplasty. RESULTS: Cleft palate patients had a significantly higher rate of colonization by staphylococcal species, but not methicillin-resistant Staphylococcus aureus , when compared to cleft lip patients (p=.0298; chi-square test). Closure of the palatal cleft coincided with significant decline in the prevalence of Klebsiella and Enterobacter species (p<.05; McNemar test). The only major complication, palatal dehiscence, was believed to be directly related to infection with group A beta-hemolytic streptococci. CONCLUSIONS: Despite a high prevalence of potential pathogenic and enteric flora preoperatively in primary palate repair, postoperative wound infection is rare in the prospective study population. However, the presence of beta-hemolytic streptococci was associated with a higher risk of repair dehiscence; therefore, screening for Streptococci prior to surgery should be performed routinely.

Noninvasive body contouring by focused ultrasound: safety and efficacy of the Contour I device in a multicenter, controlled, clinical study.

BACKGROUND: The removal of unwanted body fat using a noninvasive technique is desirable to patients and physicians. The authors describe a controlled, multicenter, clinical trial assessing the safety and efficacy of a focused therapeutic ultrasound device for noninvasive body contouring. METHODS: Eligible healthy adult subjects were enrolled to the experimental group or the control group at five sites. The experimental group received one treatment with the Contour I device (UltraShape Ltd., Tel Aviv, Israel) in the abdomen, thighs, or flanks and were evaluated over a 12-week period. Efficacy outcomes were reduction of circumference and fat thickness. Circumference reduction was compared with the untreated group and with an untreated area (thigh) within the treated group. Safety monitoring included laboratory testing (including serum lipids), pulse oximetry, and liver ultrasound. RESULTS: One hundred sixty-four subjects participated in the study (137 subjects in the experimental group and 27 in the control, untreated group). A single Contour I treatment was safe and well tolerated and produced a mean reduction of approximately 2 cm in treatment area circumference and approximately 2.9 mm in skin fat thickness. The majority of the effect was achieved within 2 weeks and was sustained at 12 weeks. No clinically significant changes in the measured safety parameters were recorded. Seven adverse events were reported, all of which were anticipated, mild, and resolved within the study period. CONCLUSION: The Contour I device provides a safe and effective noninvasive technology for body contouring.

Impairments to wound healing.

Impaired wound healing is a complication faced by all physicians, regardless of their field of practice. Plastic surgeons are frequently called on to help treat patients who fail to heal properly. Therefore, plastic surgeons must be well versed in the intrinsic and extrinsic factors that can impair wound healing, such as nutrition, drugs, radiation, smoking, and hypoxia. Only by limiting detrimental factors can wound healing progress in a beneficial fashion.