Dataset for "Machine learning outperforms clinical experts in classification of hip fractures"

1011 41 14883 3 46593 document 14883 data_files.zip application/zip c05b2293f6c3f94e816dc79ffc45018a MD5 500811 2021-04-27 10:25:34 https://researchdata.bath.ac.uk/1011/1/data_files.zip 1011 1 1 application/zip other Data for figures 2 and 4, as well as MATLAB scripts to generate figures en public cc_by

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documentation 14924 3 46753 document 14924 DMP_ARCHi.docx application/vnd.openxmlformats-officedocument.wordprocessingml.document f4757354d97abd6da4fd94637bacde73 MD5 17041 2021-04-28 14:05:39 https://researchdata.bath.ac.uk/1011/3/DMP_ARCHi.docx 1011 3 3 application/vnd.openxmlformats-officedocument.wordprocessingml.document other en staffonly

DMP_ARCHi.docx

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http://eprints.org/relation/isVersionOf https://researchdata.bath.ac.uk/id/document/14924 http://eprints.org/relation/isVolatileVersionOf https://researchdata.bath.ac.uk/id/document/14924 http://eprints.org/relation/isIndexCodesVersionOf https://researchdata.bath.ac.uk/id/document/14924 14927 3 46767 document 14927 caffe_bkup.zip application/zip 2264244dda8e4d712c580c61cfcdd571 MD5 395466511 2021-04-29 10:27:39 https://researchdata.bath.ac.uk/1011/6/caffe_bkup.zip 1011 6 6 application/zip other Contains caffe final trained models for segmentation and classification, as well as MATLAB scripts to extract and analyse the machine learning data. en invisible all_rights_reserved

caffe_bkup.zip

code 14928 2 46771 document 14928 RUH RandD ARCHi approval letter (061117).pdf application/pdf a21f9d549da91ba0e8c408e7be992832 MD5 36006 2021-04-29 10:36:24 https://researchdata.bath.ac.uk/1011/7/RUH%20RandD%20ARCHi%20approval%20letter%20%28061117%29.pdf 1011 7 7 application/pdf other Approval letter from RUH R&D department. en invisible all_rights_reserved

RUH RandD ARCHi approval letter (061117).pdf

ethics 14929 2 46774 document 14929 lightbox.jpg image/png 6b5c1bd3d021a0ce6cbd8e6fe908b1d9 MD5 21982 2021-04-29 10:46:39 https://researchdata.bath.ac.uk/1011/8/lightbox.jpg 1011 8 8 image/png other Thumbnails conversion from other to thumbnail_lightbox en invisible

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http://eprints.org/relation/isVersionOf https://researchdata.bath.ac.uk/id/document/14928 http://eprints.org/relation/isVolatileVersionOf https://researchdata.bath.ac.uk/id/document/14928 http://eprints.org/relation/issmallThumbnailVersionOf https://researchdata.bath.ac.uk/id/document/14928 archive 3826 disk0/00/00/10/11 2022-02-09 15:22:06 2024-07-09 13:51:01 2022-02-09 15:22:06 data_collection show Gill Richie R.Gill@bath.ac.uk 0000-0002-7740-2062 University of Bath TRUE Ehrhardt Beate B.Ehrhardt@bath.ac.uk 0000-0002-4476-3611 University of Bath FALSE Murphy Ellen E.Murphy@bath.ac.uk University of Bath FALSE Dataset for "Machine learning outperforms clinical experts in classification of hip fractures" GJ0090 GS0080 KS0110 dept_mech_eng dept_math_sci The data is organised clearly into the source data for each figure. Hip fractures are a major cause of morbidity and mortality in the elderly, and incur high health and social care costs. Given projected population ageing, the number of incident hip fractures is predicted to increase globally. As fracture classification strongly determines the chosen surgical treatment, differences in fracture classification influence patient outcomes and treatment costs. We aimed to create a machine learning method for identifying and classifying hip fractures, and to compare its performance to experienced human observers. We used 3659 hip radiographs, classified by at least two expert clinicians. The machine learning method was able to classify hip fractures with 19% greater accuracy than humans, achieving overall accuracy of 92%. This data set contains the source data for figures 2 and 4, which are the main Results figures. Data are given in both csv and MAT file formats. The MATLAB scripts for generating the figures are also provided. 2022-02-08 University of Bath public RightsHolder University of Bath Arthroplasty for Arthritis Charity ARCHi: Automated Recognition and Classification of Hip Fracture bathcti Machine learning recognition of the hip joint in antero-posterior (AP) pelvis x-rays. Identification and classification of hip fractures using machine learning. Anonymised x-rays were obtained, with ethical approval, from North Bristol NHS Trust and the Royal United Hospital NHS Foundation Trust. The Caffe 1.0 machine learning system was used to generate the machine learning algorithms. MATLAB (version 2017) was used to process training and test data, and to analyse output. 2017-12-01 2019-03-29 UK, Southwest, Bath & Bristol en 1 10.15125/BATH-01011 https://doi.org/10.1038/s41598-022-06018-9 pub ENPC-0160 open request Dataset