Abstract
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.
M. S. Yao and A. Chae—Equal contribution as co-first authors.
W. R. Witschey and H. Sagreiya—Equal contribution as co-senior authors.
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Acknowledgment
MSY is supported by NIH T32 EB009384. AC is supported by the A\(\Upomega \)A Carolyn L. Kuckein Student Research Fellowship and the University of Pennsylvania Diagnostic Radiology Research Fellowship. WRW is supported by NIH R01 HL137984. MTM received funding from the Sarnoff Cardiovascular Research Foundation. HS received funding from the RSNA Scholar Grant.
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Yao, M.S. et al. (2023). SynthA1c: Towards Clinically Interpretable Patient Representations for Diabetes Risk Stratification. In: Rekik, I., Adeli, E., Park, S.H., Cintas, C., Zamzmi, G. (eds) Predictive Intelligence in Medicine. PRIME 2023. Lecture Notes in Computer Science, vol 14277. Springer, Cham. https://doi.org/10.1007/978-3-031-46005-0_5
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