{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,8]],"date-time":"2026-07-08T05:24:22Z","timestamp":1783488262929,"version":"3.55.0"},"reference-count":91,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2022,9,23]],"date-time":"2022-09-23T00:00:00Z","timestamp":1663891200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Med."],"abstract":"<jats:sec><jats:title>Background<\/jats:title><jats:p>Ultrasound (US) is a valuable technique to detect degenerative findings and intrasubstance tears in lateral elbow tendinopathy (LET). Machine learning methods allow supporting this radiological diagnosis.<\/jats:p><\/jats:sec><jats:sec><jats:title>Aim<\/jats:title><jats:p>To assess multilabel classification models using machine learning models to detect degenerative findings and intrasubstance tears in US images with LET diagnosis.<\/jats:p><\/jats:sec><jats:sec><jats:title>Materials and methods<\/jats:title><jats:p>A retrospective study was performed. US images and medical records from patients with LET diagnosis from January 1st, 2017, to December 30th, 2018, were selected. Datasets were built for training and testing models. For image analysis, features extraction, texture characteristics, intensity distribution, pixel-pixel co-occurrence patterns, and scales granularity were implemented. Six different supervised learning models were implemented for binary and multilabel classification. All models were trained to classify four tendon findings (hypoechogenicity, neovascularity, enthesopathy, and intrasubstance tear). Accuracy indicators and their confidence intervals (<jats:italic>CI<\/jats:italic>) were obtained for all models following a K-fold-repeated-cross-validation method. To measure multilabel prediction, multilabel accuracy, sensitivity, specificity, and receiver operating characteristic (ROC) with 95% <jats:italic>CI<\/jats:italic> were used.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>A total of 30,007 US images (4,324 exams, 2,917 patients) were included in the analysis. The RF model presented the highest mean values in the area under the curve (AUC), sensitivity, and also specificity by each degenerative finding in the binary classification. The AUC and sensitivity showed the best performance in intrasubstance tear with 0.991 [95% <jats:italic>CI<\/jats:italic>, 099, 0.99], and 0.775 [95% <jats:italic>CI<\/jats:italic>, 0.77, 0.77], respectively. Instead, specificity showed upper values in hypoechogenicity with 0.821 [95% <jats:italic>CI<\/jats:italic>, 0.82, \u22120.82]. In the multilabel classifier, RF also presented the highest performance. The accuracy was 0.772 [95% <jats:italic>CI<\/jats:italic>, 0.771, 0.773], a great macro of 0.948 [95% <jats:italic>CI<\/jats:italic>, 0.94, 0.94], and a micro of 0.962 [95% <jats:italic>CI<\/jats:italic>, 0.96, 0.96] AUC scores were detected. Diagnostic accuracy, sensitivity, and specificity with 95% <jats:italic>CI<\/jats:italic> were calculated.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>Machine learning algorithms based on US images with LET presented high diagnosis accuracy. Mainly the random forest model shows the best performance in binary and multilabel classifiers, particularly for intrasubstance tears.<\/jats:p><\/jats:sec>","DOI":"10.3389\/fmed.2022.945698","type":"journal-article","created":{"date-parts":[[2022,9,23]],"date-time":"2022-09-23T06:05:55Z","timestamp":1663913155000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":16,"title":["Lateral elbow tendinopathy and artificial intelligence: Binary and multilabel findings detection using machine learning algorithms"],"prefix":"10.3389","volume":"9","author":[{"given":"Guillermo","family":"Droppelmann","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Manuel","family":"Tello","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nicol\u00e1s","family":"Garc\u00eda","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Crist\u00f3bal","family":"Greene","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Carlos","family":"Jorquera","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Felipe","family":"Feijoo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2022,9,23]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"1400","DOI":"10.1016\/j.mehy.2006.05.048","article-title":"\u201cLateral elbow tendinopathy\u201d is the most appropriate diagnostic term for the condition commonly referred-to as lateral epicondylitis.","volume":"67","author":"Stasinopoulos","year":"2006","journal-title":"Med Hypotheses."},{"key":"B2","doi-asserted-by":"publisher","first-page":"787","DOI":"10.1002\/9781444345100.ch92","article-title":"Tennis elbow.","author":"Struijs","year":"2012","journal-title":"Evidence-Based Orthopedics."},{"key":"B3","doi-asserted-by":"publisher","first-page":"1065","DOI":"10.1093\/aje\/kwj325","article-title":"Prevalence and determinants of lateral and medial epicondylitis: a population study.","volume":"164","author":"Shiri","year":"2006","journal-title":"Am J Epidemiol."},{"key":"B4","doi-asserted-by":"publisher","first-page":"1955","DOI":"10.2106\/JBJS.F.00727","article-title":"Anatomic factors related to the cause of tennis elbow.","volume":"89","author":"Bunata","year":"2007","journal-title":"J Bone Joint Surg Am."},{"key":"B5","doi-asserted-by":"publisher","first-page":"30","DOI":"10.1097\/AJP.0000000000000078","article-title":"Cold hyperalgesia associated with poorer prognosis in lateral epicondylalgia: a 1-year prognostic study of physical and PS.","volume":"31","author":"Coombes","year":"2015","journal-title":"Clin J Pain."},{"key":"B6","doi-asserted-by":"publisher","first-page":"E38","DOI":"10.1055\/s-0035-1569455","article-title":"Ultrasonographic Differentiation of Lateral Elbow Pain.","volume":"2","author":"Obuchowicz","year":"2016","journal-title":"Ultrasound Int Open."},{"key":"B7","doi-asserted-by":"publisher","first-page":"1066","DOI":"10.1177\/0363546514568087","article-title":"The epidemiology and health care burden of tennis elbow: a population-based study.","volume":"43","author":"Sanders","year":"2015","journal-title":"Am J Sports Med."},{"key":"B8","doi-asserted-by":"publisher","first-page":"765","DOI":"10.1002\/art.22222","article-title":"Epidemiologic surveillance of upper-extremity musculoskeletal disorders in the working population.","volume":"55","author":"Roquelaure","year":"2006","journal-title":"Arthritis Care Res."},{"key":"B9","doi-asserted-by":"publisher","first-page":"234","DOI":"10.1177\/036354657900700405","article-title":"An epidemiologic study of tennis elbow. Incidence, recurrence, and effectiveness of prevention strategies.","volume":"7","author":"Gruchow","year":"1979","journal-title":"Am J Sports Med."},{"key":"B10","doi-asserted-by":"publisher","first-page":"369","DOI":"10.1016\/j.jbspin.2003.05.002","article-title":"Treatment of lateral epicondylitis: where is the evidence?.","volume":"71","author":"Hong","year":"2004","journal-title":"Joint Bone Spine."},{"key":"B11","doi-asserted-by":"publisher","first-page":"174","DOI":"10.1016\/j.jphys.2015.07.015","article-title":"Physiotherapy management of lateral epicondylalgia.","volume":"61","author":"Bisset","year":"2015","journal-title":"J Physiother."},{"key":"B12","doi-asserted-by":"publisher","first-page":"1253","DOI":"10.1136\/bjsports-2016-096712","article-title":"Physical examination of the elbow, what is the evidence? A systematic literature review.","volume":"52","author":"Zwerus","year":"2018","journal-title":"Br J Sports Med."},{"key":"B13","doi-asserted-by":"publisher","first-page":"671","DOI":"10.1016\/j.ejrad.2014.12.007","article-title":"Ultrasound of the elbow with emphasis on detailed assessment of ligaments, tendons, and nerves.","volume":"84","author":"De Maeseneer","year":"2015","journal-title":"Eur J Radiol."},{"key":"B14","doi-asserted-by":"publisher","first-page":"76","DOI":"10.1016\/j.jus.2007.04.005","article-title":"Ultrasound of the elbow: examination techniques and US appearance of the normal and pathologic joint.","volume":"10","author":"Draghi","year":"2007","journal-title":"J Ultrasound."},{"key":"B15","first-page":"141","article-title":"Ultrasound assessment of the elbow.","volume":"14","author":"Radunovic","year":"2012","journal-title":"Med Ultrasonogr."},{"key":"B16","doi-asserted-by":"publisher","DOI":"10.1007\/s40134-016-0182-8","article-title":"Elbow Ultrasound.","volume":"4","author":"Pierce","year":"2016","journal-title":"Curr Radiol Rep."},{"key":"B17","doi-asserted-by":"publisher","first-page":"793","DOI":"10.2214\/ajr.157.4.1892039","article-title":"Sonography of the normal elbow.","volume":"157","author":"Barr","year":"1991","journal-title":"Am J Roentgenol."},{"key":"B18","doi-asserted-by":"publisher","first-page":"196","DOI":"10.1002\/jcu.20676","article-title":"Measurement issues in the sonographic assessment of tennis elbow.","volume":"38","author":"Poltawski","year":"2010","journal-title":"J Clin Ultrasound."},{"key":"B19","doi-asserted-by":"publisher","first-page":"872","DOI":"10.1136\/bjsm.2007.043901","article-title":"Diagnostic accuracy of power Doppler ultrasound in patients with chronic tennis elbow.","volume":"42","author":"Du Toit","year":"2008","journal-title":"Br J Sports Med."},{"key":"B20","doi-asserted-by":"publisher","first-page":"151","DOI":"10.1136\/bjsm.24.3.151","article-title":"Tennis elbow: an ultrasonographic study in tennis players.","volume":"24","author":"Maffulli","year":"1990","journal-title":"Br J Sports Med."},{"key":"B21","doi-asserted-by":"publisher","first-page":"1209","DOI":"10.1177\/0363546509359066","article-title":"Lateral elbow tendinopathy: correlation of ultrasound findings with pain and functional disability.","volume":"38","author":"Clarke","year":"2010","journal-title":"Am J Sports Med."},{"key":"B22","first-page":"115","article-title":"Elbow tendinopathy.","volume":"2","author":"Longo","year":"2012","journal-title":"Muscles Ligaments Tendons J."},{"key":"B23","doi-asserted-by":"publisher","first-page":"2070","DOI":"10.1249\/MSS.0000000000000345","article-title":"Diagnostic ultrasound imaging for lateral epicondylalgia: a case-control study.","volume":"46","author":"Heales","year":"2014","journal-title":"Med Sci Sports Exerc."},{"key":"B24","doi-asserted-by":"publisher","first-page":"281","DOI":"10.1016\/j.otsr.2014.01.006","article-title":"The diagnostic test accuracy of ultrasound for the detection of lateral epicondylitis: a systematic review and meta-analysis.","volume":"100","author":"Latham","year":"2014","journal-title":"Orthop Traumatol Surg Res."},{"key":"B25","doi-asserted-by":"publisher","DOI":"10.1186\/1471-2342-14-10","article-title":"The diagnostic validity of musculoskeletal ultrasound in lateral epicondylalgia: a systematic review.","volume":"4","author":"Dones","year":"2014","journal-title":"BMC Med Imaging."},{"key":"B26","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0181828","article-title":"Ultrasonography versus magnetic resonance imaging in detecting and grading common extensor tendon tear in chronic lateral epicondylitis.","volume":"12","author":"Bachta","year":"2017","journal-title":"PLoS One."},{"key":"B27","doi-asserted-by":"publisher","first-page":"2059","DOI":"10.1016\/j.ultrasmedbio.2018.05.022","article-title":"Classification of tendon matrix change using ultrasound imaging: a systematic review and meta-analysis.","volume":"44","author":"Matthews","year":"2018","journal-title":"Ultrasound Med Biol."},{"key":"B28","doi-asserted-by":"publisher","first-page":"1122","DOI":"10.1016\/j.cell.2018.02.010","article-title":"Identifying medical diagnoses and treatable diseases by image-based deep learning.","volume":"172","author":"Kermany","year":"2018","journal-title":"Cell."},{"key":"B29","doi-asserted-by":"publisher","first-page":"500","DOI":"10.1038\/s41568-018-0016-5","article-title":"Artificial intelligence in radiology.","volume":"18","author":"Hosny","year":"2018","journal-title":"Nat Rev Cancer."},{"key":"B30","doi-asserted-by":"publisher","first-page":"781","DOI":"10.1148\/radiol.2019190613","article-title":"A roadmap for foundational research on artificial intelligence in medical imaging: from the 2018 NIH\/RSNA\/ACR\/The Academy workshop.","volume":"291","author":"Langlotz","year":"2019","journal-title":"Radiology."},{"key":"B31","doi-asserted-by":"publisher","first-page":"152","DOI":"10.1016\/j.ejrad.2018.03.019","article-title":"The future of radiology augmented with artificial intelligence: a strategy for success.","volume":"102","author":"Liew","year":"2018","journal-title":"Eur J Radiol."},{"key":"B32","doi-asserted-by":"publisher","first-page":"198","DOI":"10.2174\/1567205013666160620122926","article-title":"Role of Artificial Intelligence Techniques (Automatic Classifiers) in Molecular Imaging Modalities in Neurodegenerative Diseases.","volume":"14","author":"Cascianelli","year":"2017","journal-title":"Curr Alzheimer Res."},{"key":"B33","doi-asserted-by":"publisher","first-page":"56","DOI":"10.1016\/j.trsl.2018.01.001","article-title":"Use of multimodality imaging and artificial intelligence for diagnosis and prognosis of early stages of Alzheimer\u2019s disease.","volume":"194","author":"Liu","year":"2018","journal-title":"Transl Res."},{"key":"B34","doi-asserted-by":"publisher","first-page":"1966","DOI":"10.1609\/aaai.v29i1.9473","article-title":"Sparse Bayesian multiview learning for simultaneous association discovery and diagnosis of Alzheimer\u2019s disease.","author":"Zhe","year":"2015","journal-title":"Proceedings of the National Conference on Artificial Intelligence."},{"key":"B35","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pmed.1002686","article-title":"Deep learning for chest radiograph diagnosis: a retrospective comparison of the CheXNeXt algorithm to practicing radiologists.","volume":"15","author":"Rajpurkar","year":"2018","journal-title":"PLoS Med."},{"key":"B36","doi-asserted-by":"publisher","DOI":"10.1001\/jamanetworkopen.2019.1095","article-title":"Development and Validation of a Deep Learning-Based Automated Detection Algorithm for Major Thoracic Diseases on Chest Radiographs.","volume":"2","author":"Hwang","year":"2019","journal-title":"JAMA Netw Open."},{"key":"B37","doi-asserted-by":"publisher","first-page":"218","DOI":"10.1148\/radiol.2018180237","article-title":"Development and validation of deep learning-based automatic detection algorithm for malignant pulmonary nodules on chest radiographs.","volume":"290","author":"Nam","year":"2019","journal-title":"Radiology."},{"key":"B38","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-019-51779-5","article-title":"Machine friendly machine learning: interpretation of computed tomography without image reconstruction.","volume":"9","author":"Lee","year":"2019","journal-title":"Sci Rep."},{"key":"B39","doi-asserted-by":"publisher","first-page":"42","DOI":"10.1152\/japplphysiol.00752.2015","article-title":"A machine-learning approach for computation of fractional flow reserve from coronary computed tomography.","volume":"121","author":"Itu","year":"2016","journal-title":"J Appl Physiol."},{"key":"B40","doi-asserted-by":"publisher","first-page":"274","DOI":"10.1016\/j.jcct.2019.04.007","article-title":"Advanced atherosclerosis imaging by CT: radiomics, machine learning and deep learning.","volume":"13","author":"Kolossv\u00e1ry","year":"2019","journal-title":"J Cardiovasc Comput Tomogr."},{"key":"B41","doi-asserted-by":"publisher","first-page":"1975","DOI":"10.1093\/eurheartj\/ehy404","article-title":"Clinical applications of machine learning in cardiovascular disease and its relevance to cardiac imaging.","volume":"40","author":"Al\u2019Aref","year":"2019","journal-title":"Eur Heart J."},{"key":"B42","doi-asserted-by":"publisher","DOI":"10.1038\/s41746-017-0015-z","article-title":"Advanced machine learning in action: identification of intracranial hemorrhage on computed tomography scans of the head with clinical workflow integration.","volume":"1","author":"Arbabshirani","year":"2018","journal-title":"NPJ Digit Med."},{"key":"B43","doi-asserted-by":"publisher","first-page":"59","DOI":"10.1177\/117693510600200030","article-title":"Applications of machine learning in cancer prediction and prognosis.","volume":"2","author":"Cruz","year":"2006","journal-title":"Cancer Inform."},{"key":"B44","doi-asserted-by":"publisher","first-page":"175","DOI":"10.1016\/j.neucom.2013.05.053","article-title":"Breast tumor detection in digital mammography based on extreme learning machine.","volume":"128","author":"Wang","year":"2014","journal-title":"Neurocomputing."},{"key":"B45","doi-asserted-by":"publisher","first-page":"2259","DOI":"10.1007\/s10916-011-9693-2","article-title":"Discovering mammography-based machine learning classifiers for breast cancer diagnosis.","volume":"36","author":"Ramos-Poll\u00e1n","year":"2012","journal-title":"J Med Syst."},{"key":"B46","doi-asserted-by":"publisher","first-page":"930","DOI":"10.1016\/j.neucom.2015.08.048","article-title":"Breast mass classification in digital mammography based on extreme learning machine.","volume":"173","author":"Xie","year":"2016","journal-title":"Neurocomputing."},{"key":"B47","doi-asserted-by":"publisher","first-page":"45","DOI":"10.1016\/j.media.2018.03.006","article-title":"Deep learning in mammography and breast histology, an overview and future trends.","volume":"47","author":"Hamidinekoo","year":"2018","journal-title":"Med Image Anal."},{"key":"B48","article-title":"Structure-leveraged methods in breast cancer risk prediction.","volume":"17","author":"Fan","year":"2016","journal-title":"J Mach Learn Res."},{"key":"B49","doi-asserted-by":"publisher","first-page":"248","DOI":"10.1016\/j.cmpb.2015.12.014","article-title":"Representation learning for mammography mass lesion classification with convolutional neural networks.","volume":"127","author":"Arevalo","year":"2016","journal-title":"Comput Methods Programs Biomed."},{"key":"B50","doi-asserted-by":"publisher","DOI":"10.1109\/DeSE.2016.8","article-title":"Breast cancer detection using K-nearest neighbor machine learning algorithm.","author":"Al-Hadidi","year":"2016","journal-title":"Proceedings 2016 9th International Conference on Developments in eSystems Engineering, DeSE."},{"key":"B51","doi-asserted-by":"publisher","DOI":"10.1038\/srep27327","article-title":"Discrimination of breast cancer with microcalcifications on mammography by deep learning.","volume":"6","author":"Wang","year":"2016","journal-title":"Sci Rep."},{"key":"B52","doi-asserted-by":"publisher","DOI":"10.2196\/10010","article-title":"Artificial intelligence versus clinicians in disease diagnosis: systematic review.","volume":"7","author":"Shen","year":"2019","journal-title":"J Med Internet Res."},{"key":"B53","doi-asserted-by":"publisher","first-page":"506","DOI":"10.2214\/AJR.19.21117","article-title":"Artificial intelligence in musculoskeletal imaging: current status and future directions.","volume":"213","author":"Gyftopoulos","year":"2019","journal-title":"Am J Roentgenol."},{"key":"B54","doi-asserted-by":"publisher","first-page":"183","DOI":"10.1007\/s00256-019-03284-z","article-title":"Current applications and future directions of deep learning in musculoskeletal radiology.","volume":"49","author":"Chea","year":"2020","journal-title":"Skeletal Radiol."},{"key":"B55","doi-asserted-by":"publisher","first-page":"8","DOI":"10.1016\/j.compbiomed.2018.05.011","article-title":"Deep neural networks for automatic detection of osteoporotic vertebral fractures on CT scans.","volume":"98","author":"Tomita","year":"2018","journal-title":"Comput Biol Med."},{"key":"B56","doi-asserted-by":"publisher","DOI":"10.1117\/12.2217146","article-title":"Deep convolutional networks for automated detection of posterior-element fractures on spine CT.","author":"Roth","year":"2016","journal-title":"Medical Imaging 2016: Computer-Aided Diagnosis."},{"key":"B57","doi-asserted-by":"publisher","first-page":"27","DOI":"10.1016\/j.cmpb.2019.02.006","article-title":"Deep learning and SURF for automated classification and detection of calcaneus fractures in CT images.","volume":"171","author":"Pranata","year":"2019","journal-title":"Comput Methods Programs Biomed."},{"key":"B58","doi-asserted-by":"publisher","first-page":"235","DOI":"10.1016\/j.diii.2019.03.002","article-title":"Automatic knee meniscus tear detection and orientation classification with Mask-RCNN.","volume":"100","author":"Couteaux","year":"2019","journal-title":"Diagn Interv Imaging."},{"key":"B59","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pmed.1002699","article-title":"Deep-learning-assisted diagnosis for knee magnetic resonance imaging: development and retrospective validation of MRNet.","volume":"15","author":"Bien","year":"2018","journal-title":"PLoS Med."},{"key":"B60","doi-asserted-by":"publisher","first-page":"243","DOI":"10.1016\/j.diii.2019.02.007","article-title":"Artificial intelligence to diagnose meniscus tears on MRI.","volume":"100","author":"Roblot","year":"2019","journal-title":"Diagn Interv Imaging."},{"key":"B61","doi-asserted-by":"publisher","first-page":"160","DOI":"10.1148\/radiol.2018172986","article-title":"Deep learning approach for evaluating knee MR images: achieving high diagnostic performance for cartilage lesion detection.","volume":"289","author":"Liu","year":"2018","journal-title":"Radiology."},{"key":"B62","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0207496","article-title":"Ossification area localization in pediatric hand radiographs using deep neural networks for object detection.","volume":"13","author":"Koitka","year":"2018","journal-title":"PLoS One."},{"key":"B63","doi-asserted-by":"publisher","first-page":"313","DOI":"10.1148\/radiol.2017170236","article-title":"Performance of a deep-learning neural network model in assessing skeletal maturity on pediatric hand radiographs.","volume":"287","author":"Larson","year":"2018","journal-title":"Radiology."},{"key":"B64","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-018-20132-7","article-title":"Automatic knee osteoarthritis diagnosis from plain radiographs: a deep learning-based approach.","volume":"8","author":"Tiulpin","year":"2018","journal-title":"Sci Rep."},{"key":"B65","doi-asserted-by":"publisher","first-page":"786","DOI":"10.1007\/s00261-018-1517-0","article-title":"Machine learning for medical ultrasound: status, methods, and future opportunities.","volume":"43","author":"Brattain","year":"2018","journal-title":"Abdom Radiol."},{"key":"B66","doi-asserted-by":"publisher","DOI":"10.1177\/1742271X14568074","article-title":"Special issue on education and training in ultrasound.","volume":"23","author":"Martin","year":"2015","journal-title":"Ultrasound."},{"key":"B67","doi-asserted-by":"publisher","first-page":"11","DOI":"10.1109\/JPROC.2019.2932116","article-title":"Deep learning in ultrasound imaging.","volume":"108","author":"van Sloun","year":"2019","journal-title":"Proc IEEE."},{"key":"B68","doi-asserted-by":"publisher","first-page":"55","DOI":"10.4103\/0973-6042.76960","article-title":"Ultrasound: basic understanding and learning the language.","volume":"4","author":"Ihnatsenka","year":"2010","journal-title":"Int J Shoulder Surg."},{"key":"B69","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3109761.3158381","article-title":"Automatic ROI detection and classification of the achilles tendon ultrasound images.","author":"Benrabha","year":"2017","journal-title":"Proceedings of the 1st International Conference on Internet of Things and Machine Learning"},{"key":"B70","doi-asserted-by":"publisher","first-page":"2426","DOI":"10.1016\/j.ultrasmedbio.2017.04.022","article-title":"Random Forest-Based Bone Segmentation in Ultrasound.","volume":"43","author":"Baka","year":"2017","journal-title":"Ultrasound Med Biol."},{"key":"B71","doi-asserted-by":"publisher","first-page":"201","DOI":"10.1016\/j.compbiomed.2016.03.018","article-title":"Segmentation of the spinous process and its acoustic shadow in vertebral ultrasound images.","volume":"72","author":"Berton","year":"2016","journal-title":"Comput Biol Med."},{"key":"B72","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-00934-2_105","article-title":"Estimating achilles tendon healing progress with convolutional neural networks.","author":"Kapinski","year":"2018","journal-title":"Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)."},{"key":"B73","doi-asserted-by":"publisher","first-page":"1500","DOI":"10.1016\/j.ijsu.2014.07.014","article-title":"Strengthening the Reporting of Observational Studies in Epidemiology (STROBE): explanation and elaboration.","volume":"12","author":"Vandenbroucke","year":"2014","journal-title":"Int J Surg."},{"key":"B74","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0205171","article-title":"Relationship between ultrasound detected tendon abnormalities, and sensory and clinical characteristics in people with chronic lateral epicondylalgia.","volume":"13","author":"Palaniswamy","year":"2018","journal-title":"PLoS One."},{"key":"B75","doi-asserted-by":"publisher","DOI":"10.12688\/f1000research.73441.1","article-title":"Ultrasound findings in lateral elbow tendinopathy: a retrospective analysis of radiological tendon features [version 1; peer review: awaiting peer review].","volume":"11","author":"Droppelmann","year":"2022","journal-title":"F1000Res."},{"key":"B76","doi-asserted-by":"publisher","first-page":"777","DOI":"10.2214\/ajr.176.3.1760777","article-title":"Sonographic examination of lateral epicondylitis.","volume":"176","author":"Connell","year":"2001","journal-title":"Am J Roentgenol."},{"key":"B77","doi-asserted-by":"publisher","first-page":"230","DOI":"10.1148\/radiol.2371040784","article-title":"Lateral epicondylitis of the elbow: US findings.","volume":"237","author":"Levin","year":"2005","journal-title":"Radiology."},{"key":"B78","doi-asserted-by":"crossref","DOI":"10.1007\/978-3-540-28163-4","author":"Bianchi","year":"2007","journal-title":"Ultrasound of the Musculoskeletal System"},{"key":"B79","doi-asserted-by":"publisher","first-page":"938","DOI":"10.2519\/jospt.2015.5841","article-title":"Management of lateral elbow tendinopathy: one size does not fit all.","volume":"45","author":"Coombes","year":"2015","journal-title":"J Orthop Sports Phys Ther."},{"key":"B80","doi-asserted-by":"publisher","first-page":"391","DOI":"10.1302\/2058-5241.1.000049","article-title":"Lateral epicondylitis of the elbow.","volume":"1","author":"Vaquero-Picado","year":"2016","journal-title":"EFORT Open Rev."},{"key":"B81","doi-asserted-by":"publisher","first-page":"5529","DOI":"10.1242\/jcs.123604","article-title":"Machine learning in cell biology-teaching computers to recognize phenotypes.","volume":"126","author":"Sommer","year":"2013","journal-title":"J Cell Sci."},{"key":"B82","doi-asserted-by":"publisher","first-page":"253","DOI":"10.1016\/j.jvcir.2019.05.016","article-title":"Image classification base on PCA of multi-view deep representation.","volume":"62","author":"Sun","year":"2019","journal-title":"J Vis Commun Image Represent."},{"key":"B83","doi-asserted-by":"publisher","first-page":"354","DOI":"10.1016\/j.compbiomed.2019.02.017","article-title":"Radiological images and machine learning: trends, perspectives, and prospects.","volume":"108","author":"Zhang","year":"2019","journal-title":"Comput Biol Med."},{"key":"B84","article-title":"Assay development guidelines for image-based high content screening, high content analysis and high content imaging.","author":"Buchser","year":"2014","journal-title":"Assay Guidance Manual."},{"key":"B85","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1109\/ACCT.2014.74","article-title":"A detailed review of feature extraction in image processing systems.","author":"Kumar","year":"2014","journal-title":"2014 Fourth International Conference on Advanced Computing &amp; Communication Technologies."},{"key":"B86","doi-asserted-by":"publisher","first-page":"23","DOI":"10.1109\/ICSEngT.2017.8123414","article-title":"Texture analysis and classification in ultrasound medical images for determining echo pattern characteristics.","author":"Nugroho","year":"2017","journal-title":"2017 IEEE International Conference on System Engineering and Technology."},{"key":"B87","first-page":"12","article-title":"Image analysis for microscopy screens.","volume":"6","author":"Sklyar","year":"2006","journal-title":"R News."},{"key":"B88","author":"Bosch","year":"2021","journal-title":"Machine Learning in R. Package \u2018mlr\u2019"},{"key":"B89","author":"Breiman","year":"2022","journal-title":"Breiman and Cutler\u2019s Random Forests for Classification and Regression. Package \u2018randomForest"},{"key":"B90","author":"Sorower","year":"2010","journal-title":"A Literature Survey on Algorithms for Multi-Label Learning."},{"key":"B91","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2019.105063","article-title":"Development of an automatic muscle atrophy measuring algorithm to calculate the ratio of supraspinatus in supraspinous fossa using deep learning.","volume":"182","author":"Kim","year":"2019","journal-title":"Comput Methods Programs Biomed."}],"container-title":["Frontiers in Medicine"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fmed.2022.945698\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,9,23]],"date-time":"2022-09-23T06:06:09Z","timestamp":1663913169000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fmed.2022.945698\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,9,23]]},"references-count":91,"alternative-id":["10.3389\/fmed.2022.945698"],"URL":"https:\/\/doi.org\/10.3389\/fmed.2022.945698","relation":{},"ISSN":["2296-858X"],"issn-type":[{"value":"2296-858X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,9,23]]},"article-number":"945698"}}