Objectives/Goals: This study evaluates the role of visual machine learning algorithms (VMLA) in automating a predictive model of central sarcopenia in geriatric trauma patients based on the psoas:lumbar vertebral index (PLVI) and trauma-specific frailty index (TSFI). Methods/Study Population: 150 trauma patients seen at Jon Michael Moore Trauma Center within J.W Ruby Memorial Hospital in rural West Virginia were included in this investigation across the life spectrum. The VMLA was trained on their standard of care trauma panoramic CT scans. Five expert reviewers segmented bilateral psoas muscles and the L4 vertebrae of each CT image at one slice inferior to the posterior elements of the L4 vertebrae. The data were read into a U-net convoluted neural network as ground truth. Labels were preprocessed to focus on the regions of interest and standardized into four classes: right psoas, left psoas, L4 vertebrae, and background. Performance was evaluated using accuracy, Dice coefficient, and F1 score. Results/Anticipated Results: Between our expert reviewer segmentations, we had significant inter-reader reliability with a Kappa greater than 0.8 and a mean standard deviation of the PLVI of 0.10mm^2. Preliminary VMLA testing on a subset of 70 patients yielded a validation accuracy of 88.5%, a Dice coefficient of 0.86, and an F1 score of 0.87 after 20 epochs. There was a moderate interclass correlation between PLVI and TSFI even though the TSFI lacks sensitivity. In fact, the PLVI is a more accurate predictor of frailty in trauma patients based on various outcome measures such as corrected length of stay. Our ongoing efforts are centered around improving the VMLA. Discussion/Significance of Impact: Our VMLA outperforms the current clinical standard, TSFI. Integration of our VMLA into the clinical workflow has the potential to revolutionize geriatric trauma care by providing rapid, accurate, identification of high-risk frail patients.