Interpretable and parameter optimized ensemble model for knee osteoarthritis assessment using radiographs

Abstract Knee osteoarthritis (KOA) is an orthopedic disorder with a substantial impact on mobility and quality of life. An accurate assessment of the KOA levels is imperative in prioritizing meaningful patient care. Quantifying osteoarthritis features such as osteophytes and joint space narrowing (J...

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Autores principales:	Mohammed Bany Muhammad, Mohammed Yeasin
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/623b1906f80649a68c2659768a7aa29e
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spelling	oai:doaj.org-article:623b1906f80649a68c2659768a7aa29e2021-12-02T16:08:07ZInterpretable and parameter optimized ensemble model for knee osteoarthritis assessment using radiographs10.1038/s41598-021-93851-z2045-2322https://doaj.org/article/623b1906f80649a68c2659768a7aa29e2021-07-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-93851-zhttps://doaj.org/toc/2045-2322Abstract Knee osteoarthritis (KOA) is an orthopedic disorder with a substantial impact on mobility and quality of life. An accurate assessment of the KOA levels is imperative in prioritizing meaningful patient care. Quantifying osteoarthritis features such as osteophytes and joint space narrowing (JSN) from low-resolution images (i.e., X-ray images) are mostly subjective. We implement an objective assessment and quantification of KOA to aid practitioners. In particular, we developed an interpretable ensemble of convolutional neural network (CNN) models consisting of three modules. First, we developed a scale-invariant and aspect ratio preserving model to localize Knee joints. Second, we created multiple instances of "hyperparameter optimized" CNN models with diversity and build an ensemble scoring system to assess the severity of KOA according to the Kellgren–Lawrence grading (KL) scale. Third, we provided visual explanations of the predictions by the ensemble model. We tested our models using a collection of 37,996 Knee joints from the Osteoarthritis Initiative (OAI) dataset. Our results show a superior (13–27%) performance improvement compared to the state-of-the-art methods.Mohammed Bany MuhammadMohammed YeasinNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-11 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Mohammed Bany Muhammad Mohammed Yeasin Interpretable and parameter optimized ensemble model for knee osteoarthritis assessment using radiographs
description	Abstract Knee osteoarthritis (KOA) is an orthopedic disorder with a substantial impact on mobility and quality of life. An accurate assessment of the KOA levels is imperative in prioritizing meaningful patient care. Quantifying osteoarthritis features such as osteophytes and joint space narrowing (JSN) from low-resolution images (i.e., X-ray images) are mostly subjective. We implement an objective assessment and quantification of KOA to aid practitioners. In particular, we developed an interpretable ensemble of convolutional neural network (CNN) models consisting of three modules. First, we developed a scale-invariant and aspect ratio preserving model to localize Knee joints. Second, we created multiple instances of "hyperparameter optimized" CNN models with diversity and build an ensemble scoring system to assess the severity of KOA according to the Kellgren–Lawrence grading (KL) scale. Third, we provided visual explanations of the predictions by the ensemble model. We tested our models using a collection of 37,996 Knee joints from the Osteoarthritis Initiative (OAI) dataset. Our results show a superior (13–27%) performance improvement compared to the state-of-the-art methods.
format	article
author	Mohammed Bany Muhammad Mohammed Yeasin
author_facet	Mohammed Bany Muhammad Mohammed Yeasin
author_sort	Mohammed Bany Muhammad
title	Interpretable and parameter optimized ensemble model for knee osteoarthritis assessment using radiographs
title_short	Interpretable and parameter optimized ensemble model for knee osteoarthritis assessment using radiographs
title_full	Interpretable and parameter optimized ensemble model for knee osteoarthritis assessment using radiographs
title_fullStr	Interpretable and parameter optimized ensemble model for knee osteoarthritis assessment using radiographs
title_full_unstemmed	Interpretable and parameter optimized ensemble model for knee osteoarthritis assessment using radiographs
title_sort	interpretable and parameter optimized ensemble model for knee osteoarthritis assessment using radiographs
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/623b1906f80649a68c2659768a7aa29e
work_keys_str_mv	AT mohammedbanymuhammad interpretableandparameteroptimizedensemblemodelforkneeosteoarthritisassessmentusingradiographs AT mohammedyeasin interpretableandparameteroptimizedensemblemodelforkneeosteoarthritisassessmentusingradiographs
_version_	1718384599263870976

Interpretable and parameter optimized ensemble model for knee osteoarthritis assessment using radiographs

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