  Early diagnosis of Type 2 Diabetes Mellitus (T2DM) is crucial to enable
timely therapeutic interventions and lifestyle modifications. As the time
available for clinical office visits shortens and medical imaging data become
more widely available, patient image data could be used to opportunistically
identify patients for additional T2DM diagnostic workup by physicians. We
investigated whether image-derived phenotypic data could be leveraged in
tabular learning classifier models to predict T2DM risk in an automated fashion
to flag high-risk patients without the need for additional blood laboratory
measurements. In contrast to traditional binary classifiers, we leverage neural
networks 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 models
may generalize to other patient populations, we introduce a novel generalizable
metric that uses vanilla data augmentation techniques to predict model
performance on input out-of-domain covariates. We show that image-derived
phenotypes and physical examination data together can accurately predict
diabetes risk as a means of opportunistic risk stratification enabled by
artificial intelligence and medical imaging. Our code is available at
https://github.com/allisonjchae/DMT2RiskAssessment.
