{"data":{"id":"10.6084/m9.figshare.c.4814016.v1","type":"dois","attributes":{"doi":"10.6084/m9.figshare.c.4814016.v1","prefix":"10.6084","suffix":"m9.figshare.c.4814016.v1","identifiers":[{"identifier":"https://doi.org/10.6084/m9.figshare.c.4814016.v1","identifierType":"DOI"}],"alternateIdentifiers":[{"alternateIdentifierType":"DOI","alternateIdentifier":"https://doi.org/10.6084/m9.figshare.c.4814016.v1"}],"creators":[{"name":"Cunningham, Ryan J.","nameType":"Personal","givenName":"Ryan J.","familyName":"Cunningham","affiliation":[],"nameIdentifiers":[]},{"name":"Loram, Ian D.","nameType":"Personal","givenName":"Ian D.","familyName":"Loram","affiliation":[],"nameIdentifiers":[]}],"titles":[{"title":"Supplementary material from \"Estimation of absolute states of human skeletal muscle via standard B-mode ultrasound imaging and deep convolutional neural networks\""}],"publisher":"The Royal Society","container":{},"publicationYear":2020,"subjects":[{"subject":"110601 Biomechanics","subjectScheme":"FOR"},{"subject":"FOS: Health sciences","schemeUri":"http://www.oecd.org/science/inno/38235147.pdf","subjectScheme":"Fields of Science and Technology (FOS)"},{"subject":"111602 Human Biophysics","subjectScheme":"FOR"},{"subject":"FOS: Basic medicine","schemeUri":"http://www.oecd.org/science/inno/38235147.pdf","subjectScheme":"Fields of Science and Technology (FOS)"},{"subject":"90399 Biomedical Engineering not elsewhere classified","subjectScheme":"FOR"},{"subject":"FOS: Medical engineering","schemeUri":"http://www.oecd.org/science/inno/38235147.pdf","subjectScheme":"Fields of Science and Technology (FOS)"},{"subject":"80109 Pattern Recognition and Data Mining","subjectScheme":"FOR"},{"subject":"FOS: Computer and information sciences","schemeUri":"http://www.oecd.org/science/inno/38235147.pdf","subjectScheme":"Fields of Science and Technology (FOS)"}],"contributors":[],"dates":[{"date":"2020-01-11","dateType":"Created"},{"date":"2020-01-11","dateType":"Updated"},{"date":"2020","dateType":"Issued"}],"language":null,"types":{"ris":"GEN","bibtex":"misc","citeproc":"article","schemaOrg":"Collection","resourceType":"Collection","resourceTypeGeneral":"Collection"},"relatedIdentifiers":[{"relationType":"IsSupplementTo","relatedIdentifier":"10.1098/rsif.2019.0715","relatedIdentifierType":"DOI"},{"relationType":"IsIdenticalTo","relatedIdentifier":"10.6084/m9.figshare.c.4814016","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":[],"formats":[],"version":null,"rightsList":[{"rights":"CC BY 4.0","rightsUri":"https://creativecommons.org/licenses/by/4.0"}],"descriptions":[{"description":"The objective is to test automated \u003ci\u003ein vivo\u003c/i\u003e 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 nor 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.","descriptionType":"Abstract"}],"geoLocations":[],"fundingReferences":[],"xml":"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