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An Algorithmic Approach to Understanding Osteoarthritic Knee Pain
BACKGROUND: Osteoarthritic knee pain is a complex phenomenon, and multiple factors, both within the knee and external to it, can contribute to how the patient perceives pain. We sought to determine how well a deep neural network could predict osteoarthritic knee pain and other symptoms solely from a...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Journal of Bone and Joint Surgery, Inc.
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10545400/ https://www.ncbi.nlm.nih.gov/pubmed/37790197 http://dx.doi.org/10.2106/JBJS.OA.23.00039 |
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author | Hill, Brandon G. Byrum, Travis Zhou, Anthony Schilling, Peter L. |
author_facet | Hill, Brandon G. Byrum, Travis Zhou, Anthony Schilling, Peter L. |
author_sort | Hill, Brandon G. |
collection | PubMed |
description | BACKGROUND: Osteoarthritic knee pain is a complex phenomenon, and multiple factors, both within the knee and external to it, can contribute to how the patient perceives pain. We sought to determine how well a deep neural network could predict osteoarthritic knee pain and other symptoms solely from a single radiograph view. METHODS: We used data from the Osteoarthritis Initiative, a 10-year observational study of patients with knee osteoarthritis. We paired >50,000 weight-bearing, posteroanterior knee radiographs with corresponding Knee Injury and Osteoarthritis Outcome Score (KOOS) pain, symptoms, and activities of daily living subscores and used them to train a series of deep learning models to predict those scores solely from raw radiographic input. We created regression models for specific score predictions and classification models to predict whether the modeled KOOS subscore exceeded a range of thresholds. RESULTS: The root-mean-square errors were 15.7 for KOOS pain, 13.1 for KOOS symptoms, and 14.2 for KOOS activities of daily living. Modeling was performed to predict whether pain was above or below given pain thresholds, and was able to predict extreme pain (KOOS pain < 40) with an area under the curve (AUC) of 0.78. Notably, the system was also able to correctly predict numerous cases where the Kellgren-Lawrence (KL) grade assigned by the radiologist was 0 but patient pain was high, and cases where the KL grade was 4 but patient pain was low. CONCLUSIONS: A deep neural network can be trained to predict the osteoarthritic knee pain that a patient experienced and other symptoms with reasonable accuracy from a single posteroanterior view of the knee, even using low-resolution images. The system can predict pain and dysfunction that the traditional KL grade does not capture. Deep learning applied to raw imaging inputs holds promise for disentangling sources of pain within the knee from aggravating factors external to the knee. LEVEL OF EVIDENCE: Diagnostic Level III. See Instructions for Authors for a complete description of levels of evidence. |
format | Online Article Text |
id | pubmed-10545400 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Journal of Bone and Joint Surgery, Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-105454002023-10-03 An Algorithmic Approach to Understanding Osteoarthritic Knee Pain Hill, Brandon G. Byrum, Travis Zhou, Anthony Schilling, Peter L. JB JS Open Access Scientific Articles BACKGROUND: Osteoarthritic knee pain is a complex phenomenon, and multiple factors, both within the knee and external to it, can contribute to how the patient perceives pain. We sought to determine how well a deep neural network could predict osteoarthritic knee pain and other symptoms solely from a single radiograph view. METHODS: We used data from the Osteoarthritis Initiative, a 10-year observational study of patients with knee osteoarthritis. We paired >50,000 weight-bearing, posteroanterior knee radiographs with corresponding Knee Injury and Osteoarthritis Outcome Score (KOOS) pain, symptoms, and activities of daily living subscores and used them to train a series of deep learning models to predict those scores solely from raw radiographic input. We created regression models for specific score predictions and classification models to predict whether the modeled KOOS subscore exceeded a range of thresholds. RESULTS: The root-mean-square errors were 15.7 for KOOS pain, 13.1 for KOOS symptoms, and 14.2 for KOOS activities of daily living. Modeling was performed to predict whether pain was above or below given pain thresholds, and was able to predict extreme pain (KOOS pain < 40) with an area under the curve (AUC) of 0.78. Notably, the system was also able to correctly predict numerous cases where the Kellgren-Lawrence (KL) grade assigned by the radiologist was 0 but patient pain was high, and cases where the KL grade was 4 but patient pain was low. CONCLUSIONS: A deep neural network can be trained to predict the osteoarthritic knee pain that a patient experienced and other symptoms with reasonable accuracy from a single posteroanterior view of the knee, even using low-resolution images. The system can predict pain and dysfunction that the traditional KL grade does not capture. Deep learning applied to raw imaging inputs holds promise for disentangling sources of pain within the knee from aggravating factors external to the knee. LEVEL OF EVIDENCE: Diagnostic Level III. See Instructions for Authors for a complete description of levels of evidence. Journal of Bone and Joint Surgery, Inc. 2023-10-03 /pmc/articles/PMC10545400/ /pubmed/37790197 http://dx.doi.org/10.2106/JBJS.OA.23.00039 Text en Copyright © 2023 The Authors. Published by The Journal of Bone and Joint Surgery, Incorporated. All rights reserved. https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution-Non Commercial-No Derivatives License 4.0 (https://creativecommons.org/licenses/by-nc-nd/4.0/) (CCBY-NC-ND), where it is permissible to download and share the work provided it is properly cited. The work cannot be changed in any way or used commercially without permission from the journal. |
spellingShingle | Scientific Articles Hill, Brandon G. Byrum, Travis Zhou, Anthony Schilling, Peter L. An Algorithmic Approach to Understanding Osteoarthritic Knee Pain |
title | An Algorithmic Approach to Understanding Osteoarthritic Knee Pain |
title_full | An Algorithmic Approach to Understanding Osteoarthritic Knee Pain |
title_fullStr | An Algorithmic Approach to Understanding Osteoarthritic Knee Pain |
title_full_unstemmed | An Algorithmic Approach to Understanding Osteoarthritic Knee Pain |
title_short | An Algorithmic Approach to Understanding Osteoarthritic Knee Pain |
title_sort | algorithmic approach to understanding osteoarthritic knee pain |
topic | Scientific Articles |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10545400/ https://www.ncbi.nlm.nih.gov/pubmed/37790197 http://dx.doi.org/10.2106/JBJS.OA.23.00039 |
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