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Early diagnosis of Type 2 Diabetes Mellitus (T2DM) is crucial to enabletimely therapeutic interventions and lifestyle modifications. As the timeavailable for clinical office visits shortens and medical imaging data becomemore widely available, patient image data could be used to opportunisticallyidentify patients for additional T2DM diagnostic workup by physicians. Weinvestigated whether image-derived phenotypic data could be leveraged intabular learning classifier models to predict T2DM risk in an automated fashionto flag high-risk patients without the need for additional blood laboratorymeasurements. In contrast to traditional binary classifiers, we leverage neuralnetworks and decision tree models to represent patient data as 'SynthA1c'latent variables, which mimic blood hemoglobin A1c empirical lab measurements,that achieve sensitivities as high as 87.6%. To evaluate how SynthA1c modelsmay generalize to other patient populations, we introduce a novel generalizablemetric that uses vanilla data augmentation techniques to predict modelperformance on input out-of-domain covariates. We show that image-derivedphenotypes and physical examination data together can accurately predictdiabetes risk as a means of opportunistic risk stratification enabled byartificial intelligence and medical imaging. Our code is available athttps://github.com/allisonjchae/DMT2RiskAssessment.

SynthA1c: Towards Clinically Interpretable Patient Representations for Diabetes Risk Stratification