Development of a Deep Learning Model for Classification of Hepatic Steatosis from Clinical Standard Ultrasound

Early detection and monitoring of hepatic steatosis can help establish appropriate preventative measures against progression to more advanced disease. We aimed to develop a deep learning (DL) program for classification of hepatic steatosis from standard-of-care grayscale ultrasound (US) images. In this single-center retrospective study, we utilized grayscale US images from January 1, 2010, to October 23, 2022, labeled with magnetic resonance imaging (MRI) proton density fat fraction (MRI-PDFF) to develop a DL multi-instance program for differentiating normal (S0) from steatotic liver (S1/2/3) and normal/mild steatosis (S0/1) from moderate/severe steatosis (S2/3). Diagnostic performances were assessed with area under the receiver operating characteristic curves (AUC), sensitivity, specificity and balanced accuracy with 95% confidence interval (CI). A total of 403 patients with 403 US exams were included: 171 (42%) were normal (S0: MRI-PDFF 17.4%–22.1%) and 49 (12%) had severe steatosis (S3: MRI-PDFF >22.1%). The dataset was split to include 322 patients in train/validation and 81 patients in a holdout test set (kept blind). The S0 versus S1/2/3 model achieved 81.3% (95% CI 72.1–90.5) AUC, 81.1% (70.6–91.6) sensitivity, 71.4% (54.7–88.2) specificity and 76.3% (66.4–86.2) balanced accuracy. The S0/1 versus S2/3 model achieved 95.9% (89–100) AUC, 87.5% (71.3–100) sensitivity, 96.9% (92.7–100) specificity and 92.2% (83.8–100) balanced accuracy. A multi-class model achieved a sensitivity of 71.4% (54.7–88.2) for S0, 67.6% (52.5–82.7) for S1 and 87.5% (71.3–100) for S2/3; specificity for the same model was 81.1% (70.6–91.6) for S0, 77.3% (64.9–89.7) for S1 and 96.9% (92.7–100) for S2/3. Our DL program offered high sensitivity and accuracy in detecting and categorizing hepatic steatosis from standard-of-care ultrasound.