Development of an artificial intelligence-based application for the diagnosis of sarcopenia: a retrospective cohort study using the health examination dataset.

BACKGROUND

Medical imaging techniques for diagnosing sarcopenia have been extensively investigated. Studies have proposed using the T-score and patient information as key diagnostic factors. However, these techniques have either been time-consuming or have required separate calculation processes after collecting each parameter. To address this gap, we propose an artificial intelligence (AI)-based web application that automates the collection of data, classification of the lumbar spine 3 (L3) slices, segmentation of the subcutaneous fat, visceral fat, and muscle areas in the classified L3 slices, and quantitative analysis of the segmented areas.

METHODS

We developed an automated lumbar spine slice classification model using the CNN (EfficientNetV2) algorithm and an automated domain segmentation model to identify the subcutaneous fat, visceral fat, and muscle areas using the U-NET algorithm. These models were used to identify L3 slices from abdominal computed tomography images and divide the images into the three-segmented domains for sarcopenia diagnosis. Additionally, we developed an algorithm for the calculation of T-Score calculated as (measurement value-Young adult mean)/(Young adult SD) using the Aggregation Pipeline by MongoDB, with the mean and standard deviation for skeletal muscle area (SMA), SMA/height, SMA/weight, and SMA/body mass index (BMI) for both sexes and different age groups.

RESULTS

The proposed system demonstrated high accuracy and precision, with an overall accuracy of 97.5% in classifying L3 slices and a segmentation accuracy of 92% for muscle, subcutaneous fat, and visceral fat areas. The T-Score-based analysis provided reliable diagnostic thresholds for sarcopenia, facilitating consistent and accurate assessments. Our diagnostic cutoff points for each index were as follows: SMA (-1.0: 152.55, -2.0: 125.89), SMA/height² (-1.0: 38.84, -2.0: 14.50), SMA/weight (-1.0: 2.14, -2.0: 1.89), and SMA/BMI (-1.0: 6.10, -2.0: 5.18) for men; SMA (-1.0: 96.08, -2.0: 76.96), SMA/height² (-1.0: 37.20, -2.0: 29.36), SMA/weight (-1.0: 1.80, -2.0: 1.61), and SMA/BMI (-1.0: 4.56, -2.0: 4.01) for women. SMA/BMI best reflected the loss of muscle mass in healthy populations by age, showing a more remarkable decrease in muscle mass in men than in women. The values for men gradually decreased after their 20s, and that for women gradually decreased after their 40s, which progressed to a more dramatic decline in the 70s for both sexes.

CONCLUSION

This AI-based web application addresses the limitations of previous diagnostic techniques by automatically analyzing medical images for the classification, segmentation, and calculation of T-scores. The study findings provide a more reliable and accurate diagnostic technique for sarcopenia that can consequently impact patient treatment and outcomes.