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Erschienen in:
Few-Shot Image Recognition with Manifolds Kapitel lesen Erstes Kapitel lesen

2020 | OriginalPaper | Buchkapitel

DeepTKAClassifier: Brand Classification of Total Knee Arthroplasty Implants Using Explainable Deep Convolutional Neural Networks

verfasst von : Shi Yan, Taghi Ramazanian, Elham Sagheb, Sunyang Fu, Sunghwan Sohn, David G. Lewallen, Hongfang Liu, Walter K. Kremers, Vipin Chaudhary, Michael Taunton, Hilal Maradit Kremers, Ahmad P. Tafti

Erschienen in: Advances in Visual Computing

Verlag: Springer International Publishing

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Abstract

Total knee arthroplasty (TKA) is one of the most successful surgical procedures worldwide. It improves quality of life, mobility, and functionality for the vast majority of patients. However, a TKA surgery may fail over time for several reasons, thus it requires a revision arthroplasty surgery. Identifying TKA implants is a critical consideration in preoperative planning of revision surgery. This study aims to develop, train, and validate deep convolutional neural network models to precisely classify four widely-used TKA implants based on only plain knee radiographs. Using 9,052 computationally annotated knee radiographs, we achieved weighted average precision, recall, and F1-score of 0.97, 0.97, and 0.97, respectively, with Cohen Kappa of 0.96.

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Vorheriges Kapitel Multiscale Detection of Cancerous Tissue in High Resolution Slide Scans
Nächstes Kapitel Multi-modal Image Fusion Based on Weight Local Features and Novel Sum-Modified-Laplacian in Non-subsampled Shearlet Transform Domain
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Metadaten
Titel
DeepTKAClassifier: Brand Classification of Total Knee Arthroplasty Implants Using Explainable Deep Convolutional Neural Networks
verfasst von
Shi Yan
Taghi Ramazanian
Elham Sagheb
Sunyang Fu
Sunghwan Sohn
David G. Lewallen
Hongfang Liu
Walter K. Kremers
Vipin Chaudhary
Michael Taunton
Hilal Maradit Kremers
Ahmad P. Tafti
Copyright-Jahr
2020
Buch
Advances in Visual Computing

Print ISBN: 978-3-030-64558-8

Electronic ISBN: 978-3-030-64559-5

Copyright-Jahr: 2020

DOI
https://doi.org/10.1007/978-3-030-64559-5_12

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