Sarcopenia is defined as an age associated decline in or loss of lean skeletal muscle mass. The pathophysiology can be multifactorial and the change in body composition may be difficult to detect due to obesity, changes in fat mass, or edema. Changes in weight, limb or waist circumference are not reliable indicators of muscle mass changes. Sarcopenia may also cause reduced strength, functional decline and increased risk of falling. Sarcopenia is otherwise asymptomatic and is often unrecognized.
Researchers from UC San Diego have developed a technology to quantify muscle quantity and quality that is also able to distinguish between healthy and older patients with chronic disease at risk for sarcopenia. The technology automatically selects and measures a patient's muscle quantity and quality on MRI & CT scans (full body, chest, abdomen, pelvis) using a system of convolutional neural networks (CNNs). This technology combines common convolutional neural networks to solve the unsolved clinical problem of standardizing diagnostics in evaluating Sarcopenia.
The software leverages convolutional neural networks (CNNs) to:
This invention combines common convolutional neural networks to solve the unsolved clinical problem of standardizing diagnostics in evaluating Sarcopenia.
Clinical Evaluation Stage
UC San Diego is seeking partners to commercialize this muscle quantification software code and help standardize diagnostics in evaluating Sarcopenia.