Abstract
BACKGROUND: Studies have demonstrated that a qualitatively and quantitatively assessed hyperechoic deltoid muscle on ultrasound (US) was accurate for the earlier detection of type 2 diabetes (T2D). We aim to demonstrate the utility of automated skeletal muscle US radiomics and machine learning for the earlier detection of T2D and prediabetes (PreD) as a supplement to traditional hemoglobin A(1c) (HbA(1c)) testing. METHODS: A sample of 1191 patients who underwent shoulder US was collected with five cohorts: 171 "normal" (without T2D), 69 "screening" (negative pre-US, but positive HbA(1c) post-US), 190 "risk" (negative, but clinically high-risk and referred for HbA(1c)), 365 with "PreD" (pre-US), and 396 with "diabetes" (pre-US). Analysis was performed on deltoid muscle US images. Automatic detection identified the deltoid region of interest. Radiomics features, race, age, and body mass index were input to a gradient-boosted decision tree model to predict if the patient was either low-risk or moderate/high-risk for T2D. RESULTS: Combining selected radiomics and clinical features resulted in a mean area under the receiver operating characteristic (AUROC) of 0.86 with 71% sensitivity and 96% specificity. In a subgroup of only patients with obesity, combining radiomics and clinical features achieved an AUROC of 0.92 with 82% sensitivity and 95% specificity. CONCLUSION: US radiomics and machine learning yielded promising results for the detection of T2D using skeletal muscle. Given the increasing use of shoulder US and the increasingly high number of undiagnosed patients with T2D, skeletal muscle US and radiomics analysis has the potential to serve as a supplemental noninvasive screening tool for the opportunistic earlier detection of T2D and PreD.