Machine learning outperforms clinical experts in classification of hip fractures.

Murphy, E A; Ehrhardt, B; Gregson, C L; von Arx, O A; Hartley, A; Whitehouse, M R; Thomas, M S; Stenhouse, G; Chesser, T J S; Budd, C J; Gill, H S

Murphy, E A; Ehrhardt, B; Gregson, C L; von Arx, O A; Hartley, A; Whitehouse, M R; Thomas, M S; Stenhouse, G; Chesser, T J S; Budd, C J; Gill, H S.

Affiliation

Murphy EA; Institute for Mathematical Innovation, University of Bath, Bath, UK.
Ehrhardt B; Institute for Mathematical Innovation, University of Bath, Bath, UK.
Gregson CL; Musculoskeletal Research Unit, Bristol Medical School, University of Bristol, Bristol, UK.
von Arx OA; MRC Integrative Epidemiology Unit, Bristol Medical School, University of Bristol, Bristol, UK.
Hartley A; Royal United Hospital NHS Foundation Trust, Bath, UK.
Whitehouse MR; Musculoskeletal Research Unit, Bristol Medical School, University of Bristol, Bristol, UK.
Thomas MS; MRC Integrative Epidemiology Unit, Bristol Medical School, University of Bristol, Bristol, UK.
Stenhouse G; Musculoskeletal Research Unit, Bristol Medical School, University of Bristol, Bristol, UK.
Chesser TJS; Department of Trauma and Orthopaedics, North Bristol NHS Trust, Bristol, UK.
Budd CJ; Royal United Hospital NHS Foundation Trust, Bath, UK.
Gill HS; Royal United Hospital NHS Foundation Trust, Bath, UK.

Sci Rep ; 12(1): 2058, 2022 02 08.

Article in En | MEDLINE | ID: mdl-35136091

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Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Machine Learning / Hip Fractures / Hip Joint Type of study: Diagnostic_studies / Prognostic_studies Limits: Humans Language: En Journal: Sci Rep Year: 2022 Document type: Article Affiliation country: Reino Unido Country of publication: Reino Unido

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