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Machine learning-based identification of hip arthroplasty designs

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dc.contributor.authorKang, YJ-
dc.contributor.authorYoo, JI-
dc.contributor.authorCha, YH-
dc.contributor.authorPark, CH-
dc.contributor.authorKim, JT-
dc.description.abstractBACKGROUND: The purposes of this study were to develop a machine learning-based implant recognition program and to verify its accuracy.

METHODS: Postoperative anteroposterior (AP) X-rays (>/=300 dpi) were collected of patients who underwent total hip arthroplasty. X-rays with a wire or plate added and those without a true anteroposterior view were excluded. A total of 170 X-ray images of hip implants from 29 brands were collected from five hospitals and a Google image search. These collected images were manually reorganised to ensure appropriate labelling. Collected images were preprocessed to have grey-scaled pixels with histogram equalisation for efficient training. Images varied by +10/-10 degrees , and 3606 unique images derived from the original 170 images were created for training. Discussion of the validation set being derived 25% of training set. The recognition model structure consisted of two steps: object detection and clustering. Model training was performed with Keras deep learning platform.

RESULTS: The 170 X-ray images of hip implants were used to build a stem detection model using YOLOv3. Manually labelled images were successfully trained into the stem detection model. Evaluation of 58 newly labelled X-ray images showed highly accurate stem detection (mean average precision > 0.99). Fully connected layers generated 29 class outputs. After training, a receiver operating characteristic curve was generated with a test set containing 25% of all stem-cropped images, yielding an area under the curve of 0.99.

CONCLUSION: Femoral stem identification in patients with total hip arthroplasty was very accurate. This technology could be used to collect large-scale implant information. The translational potential of this article: This program has the following clinical relevance. First, we can prepare the implants needed for revision surgery by identifying the old types of implants. Second, it can be used to diagnose peripheral osteolysis or periprosthetic fracture by further developing the ability to sensitise implant detection. Third, an automated implant detection system will help organise imaging data systematically and easily for arthroplasty registry construction.
dc.titleMachine learning-based identification of hip arthroplasty designs-
dc.subject.keywordHip arthroplasty-
dc.subject.keywordMachine learning-
dc.contributor.affiliatedAuthor김, 정택-
dc.type.localJournal Papers-
dc.citation.titleJournal of orthopaedic translation-
dc.identifier.bibliographicCitationJournal of orthopaedic translation, 21. : 13-17, 2019-
Appears in Collections:
Journal Papers > School of Medicine / Graduate School of Medicine > Orthopedic Surgery
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