Matches in SemOpenAlex for { <https://semopenalex.org/work/W4308922317> ?p ?o ?g. }
Showing items 1 to 86 of
86
with 100 items per page.
- W4308922317 endingPage "78" @default.
- W4308922317 startingPage "74" @default.
- W4308922317 abstract "Demand for total shoulder arthroplasty (TSA) has risen significantly and is projected to continue growing. From 2012 to 2017, the incidence of reverse total shoulder arthroplasty (rTSA) rose from 7.3 cases per 100,000 to 19.3 per 100,000. Anatomical TSA saw a growth from 9.5 cases per 100,000 to 12.5 per 100,000. Failure to identify implants in a timely manner can increase operative time, cost and risk of complications. Several machine learning models have been developed to perform medical image analysis. However, they have not been widely applied in shoulder surgery. The authors developed a machine learning model to identify shoulder implant manufacturers and type from anterior-posterior X-ray images. The model deployed was a convolutional neural network (CNN), which has been widely used in computer vision tasks. 696 radiographs were obtained from a single institution. 70% were used to train the model, while evaluation was done on 30%. On the evaluation set, the model performed with an overall accuracy of 93.9% with positive predictive value, sensitivity and F-1 scores of 94% across 10 different implant types (4 reverse, 6 anatomical). Average identification time was 0.110 s per implant. This proof of concept study demonstrates that machine learning can assist with preoperative planning and improve cost-efficiency in shoulder surgery." @default.
- W4308922317 created "2022-11-19" @default.
- W4308922317 creator A5026950052 @default.
- W4308922317 creator A5053371139 @default.
- W4308922317 creator A5054327139 @default.
- W4308922317 creator A5064743642 @default.
- W4308922317 creator A5073810462 @default.
- W4308922317 creator A5074023456 @default.
- W4308922317 creator A5075518033 @default.
- W4308922317 creator A5076778479 @default.
- W4308922317 date "2023-01-01" @default.
- W4308922317 modified "2023-10-18" @default.
- W4308922317 title "Development of a machine learning algorithm to identify total and reverse shoulder arthroplasty implants from X-ray images" @default.
- W4308922317 cites W1931855978 @default.
- W4308922317 cites W1967884169 @default.
- W4308922317 cites W2158022639 @default.
- W4308922317 cites W2162664051 @default.
- W4308922317 cites W2799391653 @default.
- W4308922317 cites W2922159377 @default.
- W4308922317 cites W2963521553 @default.
- W4308922317 cites W2981421798 @default.
- W4308922317 cites W3010513543 @default.
- W4308922317 cites W3013902712 @default.
- W4308922317 cites W3017372248 @default.
- W4308922317 cites W3025075066 @default.
- W4308922317 cites W3054439862 @default.
- W4308922317 cites W3081267181 @default.
- W4308922317 cites W3192094182 @default.
- W4308922317 cites W344616531 @default.
- W4308922317 cites W608212301 @default.
- W4308922317 doi "https://doi.org/10.1016/j.jor.2022.11.004" @default.
- W4308922317 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/36411845" @default.
- W4308922317 hasPublicationYear "2023" @default.
- W4308922317 type Work @default.
- W4308922317 citedByCount "4" @default.
- W4308922317 countsByYear W43089223172023 @default.
- W4308922317 crossrefType "journal-article" @default.
- W4308922317 hasAuthorship W4308922317A5026950052 @default.
- W4308922317 hasAuthorship W4308922317A5053371139 @default.
- W4308922317 hasAuthorship W4308922317A5054327139 @default.
- W4308922317 hasAuthorship W4308922317A5064743642 @default.
- W4308922317 hasAuthorship W4308922317A5073810462 @default.
- W4308922317 hasAuthorship W4308922317A5074023456 @default.
- W4308922317 hasAuthorship W4308922317A5075518033 @default.
- W4308922317 hasAuthorship W4308922317A5076778479 @default.
- W4308922317 hasBestOaLocation W43089223171 @default.
- W4308922317 hasConcept C11413529 @default.
- W4308922317 hasConcept C119857082 @default.
- W4308922317 hasConcept C141071460 @default.
- W4308922317 hasConcept C154945302 @default.
- W4308922317 hasConcept C2778336525 @default.
- W4308922317 hasConcept C2781411149 @default.
- W4308922317 hasConcept C36454342 @default.
- W4308922317 hasConcept C41008148 @default.
- W4308922317 hasConcept C71924100 @default.
- W4308922317 hasConcept C81363708 @default.
- W4308922317 hasConceptScore W4308922317C11413529 @default.
- W4308922317 hasConceptScore W4308922317C119857082 @default.
- W4308922317 hasConceptScore W4308922317C141071460 @default.
- W4308922317 hasConceptScore W4308922317C154945302 @default.
- W4308922317 hasConceptScore W4308922317C2778336525 @default.
- W4308922317 hasConceptScore W4308922317C2781411149 @default.
- W4308922317 hasConceptScore W4308922317C36454342 @default.
- W4308922317 hasConceptScore W4308922317C41008148 @default.
- W4308922317 hasConceptScore W4308922317C71924100 @default.
- W4308922317 hasConceptScore W4308922317C81363708 @default.
- W4308922317 hasLocation W43089223171 @default.
- W4308922317 hasLocation W43089223172 @default.
- W4308922317 hasOpenAccess W4308922317 @default.
- W4308922317 hasPrimaryLocation W43089223171 @default.
- W4308922317 hasRelatedWork W2003938723 @default.
- W4308922317 hasRelatedWork W2047967234 @default.
- W4308922317 hasRelatedWork W2118496982 @default.
- W4308922317 hasRelatedWork W2364998975 @default.
- W4308922317 hasRelatedWork W2439875401 @default.
- W4308922317 hasRelatedWork W3021430260 @default.
- W4308922317 hasRelatedWork W3027997911 @default.
- W4308922317 hasRelatedWork W4238867864 @default.
- W4308922317 hasRelatedWork W4287776258 @default.
- W4308922317 hasRelatedWork W2525756941 @default.
- W4308922317 hasVolume "35" @default.
- W4308922317 isParatext "false" @default.
- W4308922317 isRetracted "false" @default.
- W4308922317 workType "article" @default.