Abstract
The objective is to test automated in vivo estimation of active and passive skeletal muscle states using ultrasonic imaging. Current technology (electromyography, dynamometry, shear wave imaging) provides no general, non-invasive method for online estimation of skeletal muscle states. Ultrasound (US) allows non-invasive imaging of muscle, yet current computational approaches have never achieved simultaneous extraction or generalization of independently varying active and passive states. We use deep learning to investigate the generalizable content of two-dimensional (2D) US muscle images. US data synchronized with electromyography of the calf muscles, with measures of joint moment/angle, were recorded from 32 healthy participants (seven female; ages: 27.5, 19–65). We extracted a region of interest of medial gastrocnemius and soleus using our prior developed accurate segmentation algorithm. From the segmented images, a deep convolutional neural network was trained to predict three absolute, drift-free components of the neurobiomechanical state (activity, joint angle, joint moment) during experimentally designed, simultaneous independent variation of passive (joint angle) and active (electromyography) inputs. For all 32 held-out participants (16-fold cross-validation) the ankle joint angle, electromyography and joint moment were estimated to accuracy 55 ± 8%, 57 ± 11% and 46 ± 9%, respectively. With 2D US imaging, deep neural networks can encode, in generalizable form, the activity–length–tension state relationship of these muscles. Observation-only, low-power 2D US imaging can provide a new category of technology for non-invasive estimation of neural output, length and tension in skeletal muscle. This proof of principle has value for personalized muscle assessment in pain, injury, neurological conditions, neuropathies, myopathies and ageing.
Highlights
There is a current unmet medical demand for personalized in vivo skeletal muscle analysis
For the 100 randomly selected test images, segmentation agreed with the manual annotations to 0.3 mm2 and segmented at approximately 10 images per second
We have presented a novel experiment for the generation of hundreds of thousands of accurately labelled muscle US images for modelling functional muscle states using US
Summary
There is a current unmet medical demand for personalized in vivo skeletal muscle analysis. Muscle-related pain, injury and dysfunction represent an enormous socio-economic cost, including the cost of medical treatment, work absence and long-term decreased ability to perform activities of daily living which exceeds that estimated for heart disease, cancer or diabetes [1,2]. This need arises in conditions of pain/injury (work-related injury, neck–back–leg pain and injury), arthritic conditions, neurological conditions (dystonia, motor neuron disease), myopathies (myositis), neuropathies (nerve injury, spinal cord injury) and changes associated with ageing (motor unit loss) [3,4]. We hypothesize that the dynamic state of skeletal muscle is encoded by the three-dimensional collagenous structure, and is observable by two-dimensional (2D) US images [3,4]
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