Building a machine learning-based platform to analyze skeletal muscles using ultrasound and force sensor

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Low muscle mass is a prevalent body composition abnormality among older adults and people with acute and chronic conditions. Low muscle mass is a strong predictor of adverse health outcomes, including mortality, postoperative complications, length of hospital or intensive care unit stay, metabolic disorders, physical and cognitive dysfunction, and poor quality of life. In addition to stratifying individual health risks, body composition assessment also informs personalized nutrition and exercise interventions and evaluates treatment efficacy. Ultrasound is an emerging low-cost, non-invasive, portable, imaging modality. Ultrasound can accurately measure the cross-sectional area, thickness, and echo intensity of skeletal muscles at diverse body sites. These features make ultrasound an optimal choice for use in clinical practice compared to other techniques that expose subjects to radiation, need an allocated space, and are expensive (e.g., computed tomography, dual-energy x-ray absorptiometry). However, limitations, such as inconsistent tissue compression across operators and manual image segmentation, still preclude its widespread use. Responding to a direct clinical need, we propose to develop and test (1) a force sensor to standardize ultrasound transducer force and, consequently, tissue compression; and (2) a deep learning automated algorithm using machine learning to identify tissue features and automatically assess muscle parameters. To achieve this, we strategically established a partnership between research teams from the University of Alberta Human Nutrition Research Unit, a state-of-art research facility for body composition assessment and the best in Canada, medical imaging, and biomedical engineering. We hope to develop a technical solution for bridging the gap between research and technology validation so that healthcare providers can widely use ultrasound for muscle mass assessment to improve health outcomes in the foreseeable future.