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

低肌肉质量是老年人和患有急性和慢性疾病的人中普遍存在的身体组成异常。低肌肉质量是不良健康结果的强有力预测因素，包括死亡率、术后并发症、住院或重症监护室住院时间、代谢紊乱、身体和认知功能障碍以及生活质量差。除了对个人健康风险进行分层外，身体成分评估还可以为个性化的营养和运动干预提供信息，并评估治疗效果。超声是一种新兴的低成本、非侵入性、便携式成像方式。超声波可以准确测量不同身体部位骨骼肌的横截面积、厚度和回声强度。这些特征使得超声与将受试者暴露于辐射、需要分配的空间并且昂贵（例如，计算机断层摄影术、双能X射线吸收测定法）。然而，局限性，如不一致的组织压缩操作员和手动图像分割，仍然妨碍其广泛使用。为了响应直接的临床需求，我们建议开发和测试（1）一种力传感器，以标准化超声换能器力，从而标准化组织压缩;以及（2）一种深度学习自动化算法，使用机器学习来识别组织特征并自动评估肌肉参数。为了实现这一目标，我们战略性地与来自阿尔伯塔大学人类营养研究中心的研究团队建立了合作伙伴关系，该中心是最先进的身体成分评估研究机构，也是加拿大最好的医学成像和生物医学工程研究机构。我们希望开发一种技术解决方案，弥合研究和技术验证之间的差距，以便医疗保健提供者可以在可预见的未来广泛使用超声进行肌肉质量评估，以改善健康结果。

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.