{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T03:35:27Z","timestamp":1793504127263,"version":"4.5.0"},"reference-count":59,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2023,11,21]],"date-time":"2023-11-21T00:00:00Z","timestamp":1700524800000},"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:p>Ultrasonography (US) has become a valuable imaging tool for the examination of the musculoskeletal system. It provides important diagnostic information and it can also be very useful in the assessment of disease activity and treatment response. US has gained widespread use in rheumatology practice because it provides real time and dynamic assessment, although it is dependent on the examiner\u2019s experience. The implementation of artificial intelligence (AI) techniques in the process of image recognition and interpretation has the potential to overcome certain limitations related to physician-dependent assessment, such as the variability in image acquisition. Multiple studies in the field of AI have explored how integrated machine learning algorithms could automate specific tissue recognition, diagnosis of joint and muscle pathology, and even grading of synovitis which is essential for monitoring disease activity. AI-based techniques applied in musculoskeletal US imaging focus on automated segmentation, image enhancement, detection and classification. AI-based US imaging can thus improve accuracy, time efficiency and offer a framework for standardization between different examinations. This paper will offer an overview of current research in the field of AI-based ultrasonography of the musculoskeletal system with focus on the applications of machine learning techniques in the examination of joints, muscles and peripheral nerves, which could potentially improve the performance of everyday clinical practice.<\/jats:p>","DOI":"10.3389\/fmed.2023.1286085","type":"journal-article","created":{"date-parts":[[2023,11,23]],"date-time":"2023-11-23T16:05:37Z","timestamp":1700755537000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":30,"title":["Applications of artificial intelligence in musculoskeletal ultrasound: narrative review"],"prefix":"10.3389","volume":"10","author":[{"given":"Stefan Cristian","family":"Dinescu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Doru","family":"Stoica","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Cristina Elena","family":"Bita","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Andreea-Iulia","family":"Nicoara","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mihaela","family":"Cirstei","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Maria-Alexandra","family":"Staiculesc","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Florentin","family":"Vreju","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2023,11,21]]},"reference":[{"key":"ref1","doi-asserted-by":"publisher","first-page":"20190031","DOI":"10.1259\/bjro.20190031","article-title":"The role of artificial intelligence in medical imaging research","volume":"2","author":"Tang","year":"2019","journal-title":"BJR Open"},{"key":"ref2","doi-asserted-by":"publisher","first-page":"570","DOI":"10.3348\/kjr.2017.18.4.570","article-title":"Deep learning in medical imaging: general overview","volume":"18","author":"Lee","year":"2017","journal-title":"Korean J Radiol"},{"key":"ref3","doi-asserted-by":"publisher","first-page":"26731","DOI":"10.1007\/s11042-022-14305-w","article-title":"Machine learning and deep learning approach for medical image analysis: diagnosis to detection","volume":"82","author":"Rana","year":"2023","journal-title":"Multimed Tools Appl"},{"key":"ref4","doi-asserted-by":"publisher","first-page":"196","DOI":"10.4103\/jmu.jmu_182_21","article-title":"Deep learning for detecting supraspinatus calcific tendinopathy on ultrasound images","volume":"30","author":"Chiu","year":"2022","journal-title":"J Med Ultrasound"},{"key":"ref5","doi-asserted-by":"publisher","first-page":"313","DOI":"10.14366\/usg.21031","article-title":"Artificial intelligence in medical ultrasonography: driving on an unpaved road","volume":"40","author":"Kim","year":"2021","journal-title":"Ultrasonography"},{"key":"ref6","doi-asserted-by":"publisher","first-page":"3693","DOI":"10.3390\/app13063693","article-title":"Recent advances in artificial intelligence-assisted ultrasound scanning","volume":"13","author":"Tenajas","year":"2023","journal-title":"Appl Sci"},{"key":"ref7","doi-asserted-by":"publisher","first-page":"591","DOI":"10.1007\/s00540-021-02947-3","article-title":"A real-time anatomy \u0131dentification via tool based on artificial \u0131ntelligence for ultrasound-guided peripheral nerve block procedures: an accuracy study","volume":"35","author":"Gungor","year":"2021","journal-title":"J Anesth"},{"key":"ref8","doi-asserted-by":"publisher","first-page":"802","DOI":"10.1002\/ca.23742","article-title":"Identifying anatomical structures on ultrasound: assistive artificial intelligence in ultrasound-guided regional anesthesia","volume":"34","author":"Bowness","year":"2021","journal-title":"Clin Anat"},{"key":"ref9","doi-asserted-by":"publisher","first-page":"1056055","DOI":"10.3389\/fcvm.2023.1056055","article-title":"Assisted probe guidance in cardiac ultrasound: a review","volume":"10","author":"Ferraz","year":"2023","journal-title":"Front Cardiovasc Med"},{"key":"ref10","doi-asserted-by":"publisher","first-page":"1197447","DOI":"10.3389\/fonc.2023.1197447","article-title":"Artificial intelligence - 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