Opportunistic Detection of Type 2 Diabetes Using Deep Learning From Frontal Chest Radiographs

Deep learning (DL) models can harness electronic health records (EHRs) to predict diseases and extract radiologic findings for diagnosis. With ambulatory chest radiographs (CXRs) frequently ordered, we investigated detecting type 2 diabetes (T2D) by combining radiographic and EHR data using a DL model. Our model, developed from 271,065 CXRs and 160,244 patients, was tested on a prospective dataset of 9,943 CXRs. Here we show the model effectively detected T2D with a ROC AUC of 0.84 and a 16% prevalence. The algorithm flagged 1,381 cases (14%) as suspicious for T2D. External validation at a distinct institution yielded a ROC AUC of 0.77, with 5% of patients subsequently diagnosed with T2D. Explainable AI techniques revealed correlations between specific adiposity measures and high predictivity, suggesting CXRs\u27 potential for enhanced T2D screening

Validation of a deep learning, value-based care model to predict mortality and comorbidities from chest radiographs in COVID-19

We validate a deep learning model predicting comorbidities from frontal chest radiographs (CXRs) in patients with coronavirus disease 2019 (COVID-19) and compare the model’s performance with hierarchical condition category (HCC) and mortality outcomes in COVID-19. The model was trained and tested on 14,121 ambulatory frontal CXRs from 2010 to 2019 at a single institution, modeling select comorbidities using the value-based Medicare Advantage HCC Risk Adjustment Model. Sex, age, HCC codes, and risk adjustment factor (RAF) score were used. The model was validated on frontal CXRs from 413 ambulatory patients with COVID-19 (internal cohort) and on initial frontal CXRs from 487 COVID-19 hospitalized patients (external cohort). The discriminatory ability of the model was assessed using receiver operating characteristic (ROC) curves compared to the HCC data from electronic health records, and predicted age and RAF score were compared using correlation coefficient and absolute mean error. The model predictions were used as covariables in logistic regression models to evaluate the prediction of mortality in the external cohort. Predicted comorbidities from frontal CXRs, including diabetes with chronic complications, obesity, congestive heart failure, arrhythmias, vascular disease, and chronic obstructive pulmonary disease, had a total area under ROC curve (AUC) of 0.85 (95% CI: 0.85–0.86). The ROC AUC of predicted mortality for the model was 0.84 (95% CI,0.79–0.88) for the combined cohorts. This model using only frontal CXRs predicted select comorbidities and RAF score in both internal ambulatory and external hospitalized COVID-19 cohorts and was discriminatory of mortality, supporting its potential use in clinical decision making. Author summary Artificial Intelligence algorithms in Radiology can be used not only on standard imaging data like chest radiographs to predict diagnoses but can also incorporate other data. We wanted to find out if we could combine administrative and demographic data with chest radiographs to predict common comorbidities and mortality. Our deep learning algorithm was able to predict diabetes with chronic complications, obesity, congestive heart failure, arrythmias, vascular disease, and chronic obstructive pulmonary disease. The deep learning algorithm was also able to predict an administrative metric (RAF score) used in value-based Medicare Advantage plans. We used these predictions as biomarkers to predict mortality with a second statistical model using logistic regression in COVID-19 patients both in and out of the hospital. The degree of discrimination both the deep learning algorithm and statistical model provide would be considered ‘good’ by most, and certainly much better than chance alone. It was measured at 0.85 (95% CI: 0.85–0.86) by the area under the ROC curve method for the artificial intelligence algorithm, and 0.84 (95% CI:0.79–0.88) by the same method for the statistical mortality prediction model

Validation of a deep learning, value-based care model to predict mortality and comorbidities from chest radiographs in COVID-19.

We validate a deep learning model predicting comorbidities from frontal chest radiographs (CXRs) in patients with coronavirus disease 2019 (COVID-19) and compare the model's performance with hierarchical condition category (HCC) and mortality outcomes in COVID-19. The model was trained and tested on 14,121 ambulatory frontal CXRs from 2010 to 2019 at a single institution, modeling select comorbidities using the value-based Medicare Advantage HCC Risk Adjustment Model. Sex, age, HCC codes, and risk adjustment factor (RAF) score were used. The model was validated on frontal CXRs from 413 ambulatory patients with COVID-19 (internal cohort) and on initial frontal CXRs from 487 COVID-19 hospitalized patients (external cohort). The discriminatory ability of the model was assessed using receiver operating characteristic (ROC) curves compared to the HCC data from electronic health records, and predicted age and RAF score were compared using correlation coefficient and absolute mean error. The model predictions were used as covariables in logistic regression models to evaluate the prediction of mortality in the external cohort. Predicted comorbidities from frontal CXRs, including diabetes with chronic complications, obesity, congestive heart failure, arrhythmias, vascular disease, and chronic obstructive pulmonary disease, had a total area under ROC curve (AUC) of 0.85 (95% CI: 0.85-0.86). The ROC AUC of predicted mortality for the model was 0.84 (95% CI,0.79-0.88) for the combined cohorts. This model using only frontal CXRs predicted select comorbidities and RAF score in both internal ambulatory and external hospitalized COVID-19 cohorts and was discriminatory of mortality, supporting its potential use in clinical decision making

Domestic and transnational perspectives on democratization

The disciplinary separation between comparative politics and international relations is regularly challenged but persists as a result of institutional inertia and hiring practices. This essay uses the issue of democratization in an attempt to go beyond rhetoric and to develop a framework that integrates the role of transnational activism into the analysis of domestic regime change. Comparative research on democratization confirms that underlying socioeconomic conditions affect the long-term sustainability of democratic reforms. The initiation of such reforms, as well as the process they take, can best be understood using an agency-based framework that links domestic and transnational forces. Outside interventions are a potent factor in challenging authoritarian practices, but they do not simply displace existing domestic practices and conditions. Although transnational activists and scholars often celebrate the empowering role of networking and mobilization, the long-term effects of such interventions are still poorly understood. Transnational ties may distract domestic activists from building effective coalitions at home or undermine their legitimacy overall. Transnational scholars and activists can learn from comparative research how different domestic groups use outside interventions to promote their interests at home