A Machine Learning Algorithm to Estimate Sarcopenia on Abdominal CT

Rationale and Objectives To assess whether a fully-automated deep learning system can accurately detect and analyze truncal musculature at multiple lumbar vertebral levels and muscle groupings on abdominal CT for potential use in the detection of central sarcopenia. Materials and Methods A computer system for automated segmentation of truncal musculature groups was designed and created. Abdominal CT scans of 102 sequential patients (mean age 68 years, range 59–81 years; 53 women, 49 men) conducted between January 2015 and February 2015 were assembled as a data set. Truncal musculature was manually segmented on axial CT images at multiple lumbar vertebral levels as reference standard data, divided into training and testing subsets, and analyzed by the system. Dice similarity coefficients were calculated to evaluate system performance. IRB approval was obtained, with waiver of informed consent in this retrospective study. Results System performance as gauged by the Dice coefficients, for detecting the total abdominal muscle cross-section at the level of the third and fourth lumbar vertebrae, were, respectively, 0.953 ± 0.015 and 0.953 ± 0.011 for the training set, and 0.938 ± 0.028 and 0.940 ± 0.026 for the testing set. Dice coefficients for detecting total psoas muscle cross-section at the level of the third and fourth lumbar vertebrae, were, respectively, 0.942 ± 0.040 and 0.951 ± 0.037 for the training set, and 0.939 ± 0.028 and 0.946 ± 0.032 for the testing set. Conclusion This system fully-automatically and accurately segments multiple muscle groups at all lumbar spine levels on abdominal CT for detection of sarcopenia.