TY - JOUR
T1 - An Overview of Machine Learning in Orthopedic Surgery
T2 - An Educational Paper
AU - Padash, Sirwa
AU - Mickley, John P.
AU - Vera Garcia, Diana V.
AU - Nugen, Fred
AU - Khosravi, Bardia
AU - Erickson, Bradley J.
AU - Wyles, Cody C.
AU - Taunton, Michael J.
N1 - Publisher Copyright:
© 2023 Elsevier Inc.
PY - 2023/10
Y1 - 2023/10
N2 - The growth of artificial intelligence combined with the collection and storage of large amounts of data in the electronic medical record collection has created an opportunity for orthopedic research and translation into the clinical environment. Machine learning (ML) is a type of artificial intelligence tool well suited for processing the large amount of available data. Specific areas of ML frequently used by orthopedic surgeons performing total joint arthroplasty include tabular data analysis (spreadsheets), medical imaging processing, and natural language processing (extracting concepts from text). Previous studies have discussed models able to identify fractures in radiographs, identify implant type in radiographs, and determine the stage of osteoarthritis based on walking analysis. Despite the growing popularity of ML, there are limitations including its reliance on “good” data, potential for overfitting, long life cycle for creation, and ability to only perform one narrow task. This educational article will further discuss a general overview of ML, discussing these challenges and including examples of successfully published models.
AB - The growth of artificial intelligence combined with the collection and storage of large amounts of data in the electronic medical record collection has created an opportunity for orthopedic research and translation into the clinical environment. Machine learning (ML) is a type of artificial intelligence tool well suited for processing the large amount of available data. Specific areas of ML frequently used by orthopedic surgeons performing total joint arthroplasty include tabular data analysis (spreadsheets), medical imaging processing, and natural language processing (extracting concepts from text). Previous studies have discussed models able to identify fractures in radiographs, identify implant type in radiographs, and determine the stage of osteoarthritis based on walking analysis. Despite the growing popularity of ML, there are limitations including its reliance on “good” data, potential for overfitting, long life cycle for creation, and ability to only perform one narrow task. This educational article will further discuss a general overview of ML, discussing these challenges and including examples of successfully published models.
KW - computer vision
KW - data science
KW - deep learning
KW - machine learning
KW - total hip arthroplasty
KW - total knee arthroplasty
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U2 - 10.1016/j.arth.2023.08.043
DO - 10.1016/j.arth.2023.08.043
M3 - Review article
C2 - 37598786
AN - SCOPUS:85169569144
SN - 0883-5403
VL - 38
SP - 1938
EP - 1942
JO - Journal of Arthroplasty
JF - Journal of Arthroplasty
IS - 10
ER -