Deep Learning-Based Automated Body Composition Analysis Predicts Mortality in Peritoneal Dialysis Patients: A Retrospective Cohort Study

Background Patients undergoing peritoneal dialysis (PD) frequently experience a progressive loss of lean mass and an increase in fat mass. Quantitative body composition analysis is critical for predicting mortality; In this study, we developed a deep learning model that can automatically segment abdominal CT images to quantify body composition and verified its clinical applicability. Material and Methods The deep learning model, which quantifies tissue components at the L1–L5 vertebral levels, was developed using data from outpatient and patient undergoing PD cohorts. To establish ground-truth labels, two specialists manually segmented visceral adipose tissue (VAT), subcutaneous adipose tissue (SAT), and skeletal muscle (SM). Sarcopenia was classified using accepted SM index cutoffs and SAT- and VAT-based median values. Cox regression analysis and Kaplan–Meier curves were utilized to determine the relationship between these variables and mortality. Results The mean dice similarity coefficient values for SM, SAT, and VAT were high in the test set. To evaluate their clinical utility, we assessed the predictive capacity of body composition measurements for clinical outcomes among patients undergoing PD. Upon adjusting for age, gender, SAT, and VAT, we found an independent association between skeletal muscle index (SMI) and mortality (P < 0.05). Conclusion This study provides an automated body composition estimation approach from the L1–L5 vertebral levels. Automated SMI assessments were found to be predictive of mortality in patients undergoing PD, providing evidence for the wider implementation potential of this methodology in clinical practice.