Deep learning for large scale MRI-based morphological phenotyping of osteoarthritis

Abstract Osteoarthritis (OA) develops through heterogenous pathophysiologic pathways. As a result, no regulatory agency approved disease modifying OA drugs are available to date. Stratifying knees into MRI-based morphological phenotypes may provide insight into predicting future OA incidence, leadin...

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Autores principales:	Nikan K. Namiri, Jinhee Lee, Bruno Astuto, Felix Liu, Rutwik Shah, Sharmila Majumdar, Valentina Pedoia
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/64ad8910af6d4df0b07397fb075826fa
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spelling	oai:doaj.org-article:64ad8910af6d4df0b07397fb075826fa2021-12-02T14:49:11ZDeep learning for large scale MRI-based morphological phenotyping of osteoarthritis10.1038/s41598-021-90292-62045-2322https://doaj.org/article/64ad8910af6d4df0b07397fb075826fa2021-05-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-90292-6https://doaj.org/toc/2045-2322Abstract Osteoarthritis (OA) develops through heterogenous pathophysiologic pathways. As a result, no regulatory agency approved disease modifying OA drugs are available to date. Stratifying knees into MRI-based morphological phenotypes may provide insight into predicting future OA incidence, leading to improved inclusion criteria and efficacy of therapeutics. We trained convolutional neural networks to classify bone, meniscus/cartilage, inflammatory, and hypertrophy phenotypes in knee MRIs from participants in the Osteoarthritis Initiative (n = 4791). We investigated cross-sectional association between baseline morphological phenotypes and baseline structural OA (Kellgren Lawrence grade > 1) and symptomatic OA. Among participants without baseline OA, we evaluated association of baseline phenotypes with 48-month incidence of structural OA and symptomatic OA. The area under the curve of bone, meniscus/cartilage, inflammatory, and hypertrophy phenotype neural network classifiers was 0.89 ± 0.01, 0.93 ± 0.03, 0.96 ± 0.02, and 0.93 ± 0.02, respectively (mean ± standard deviation). Among those with no baseline OA, bone phenotype (OR: 2.99 (95%CI: 1.59–5.62)) and hypertrophy phenotype (OR: 5.80 (95%CI: 1.82–18.5)) each respectively increased odds of developing incident structural OA and symptomatic OA at 48 months. All phenotypes except meniscus/cartilage increased odds of undergoing total knee replacement within 96 months. Artificial intelligence can rapidly stratify knees into structural phenotypes associated with incident OA and total knee replacement, which may aid in stratifying patients for clinical trials of targeted therapeutics.Nikan K. NamiriJinhee LeeBruno AstutoFelix LiuRutwik ShahSharmila MajumdarValentina PedoiaNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-10 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Nikan K. Namiri Jinhee Lee Bruno Astuto Felix Liu Rutwik Shah Sharmila Majumdar Valentina Pedoia Deep learning for large scale MRI-based morphological phenotyping of osteoarthritis
description	Abstract Osteoarthritis (OA) develops through heterogenous pathophysiologic pathways. As a result, no regulatory agency approved disease modifying OA drugs are available to date. Stratifying knees into MRI-based morphological phenotypes may provide insight into predicting future OA incidence, leading to improved inclusion criteria and efficacy of therapeutics. We trained convolutional neural networks to classify bone, meniscus/cartilage, inflammatory, and hypertrophy phenotypes in knee MRIs from participants in the Osteoarthritis Initiative (n = 4791). We investigated cross-sectional association between baseline morphological phenotypes and baseline structural OA (Kellgren Lawrence grade > 1) and symptomatic OA. Among participants without baseline OA, we evaluated association of baseline phenotypes with 48-month incidence of structural OA and symptomatic OA. The area under the curve of bone, meniscus/cartilage, inflammatory, and hypertrophy phenotype neural network classifiers was 0.89 ± 0.01, 0.93 ± 0.03, 0.96 ± 0.02, and 0.93 ± 0.02, respectively (mean ± standard deviation). Among those with no baseline OA, bone phenotype (OR: 2.99 (95%CI: 1.59–5.62)) and hypertrophy phenotype (OR: 5.80 (95%CI: 1.82–18.5)) each respectively increased odds of developing incident structural OA and symptomatic OA at 48 months. All phenotypes except meniscus/cartilage increased odds of undergoing total knee replacement within 96 months. Artificial intelligence can rapidly stratify knees into structural phenotypes associated with incident OA and total knee replacement, which may aid in stratifying patients for clinical trials of targeted therapeutics.
format	article
author	Nikan K. Namiri Jinhee Lee Bruno Astuto Felix Liu Rutwik Shah Sharmila Majumdar Valentina Pedoia
author_facet	Nikan K. Namiri Jinhee Lee Bruno Astuto Felix Liu Rutwik Shah Sharmila Majumdar Valentina Pedoia
author_sort	Nikan K. Namiri
title	Deep learning for large scale MRI-based morphological phenotyping of osteoarthritis
title_short	Deep learning for large scale MRI-based morphological phenotyping of osteoarthritis
title_full	Deep learning for large scale MRI-based morphological phenotyping of osteoarthritis
title_fullStr	Deep learning for large scale MRI-based morphological phenotyping of osteoarthritis
title_full_unstemmed	Deep learning for large scale MRI-based morphological phenotyping of osteoarthritis
title_sort	deep learning for large scale mri-based morphological phenotyping of osteoarthritis
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/64ad8910af6d4df0b07397fb075826fa
work_keys_str_mv	AT nikanknamiri deeplearningforlargescalemribasedmorphologicalphenotypingofosteoarthritis AT jinheelee deeplearningforlargescalemribasedmorphologicalphenotypingofosteoarthritis AT brunoastuto deeplearningforlargescalemribasedmorphologicalphenotypingofosteoarthritis AT felixliu deeplearningforlargescalemribasedmorphologicalphenotypingofosteoarthritis AT rutwikshah deeplearningforlargescalemribasedmorphologicalphenotypingofosteoarthritis AT sharmilamajumdar deeplearningforlargescalemribasedmorphologicalphenotypingofosteoarthritis AT valentinapedoia deeplearningforlargescalemribasedmorphologicalphenotypingofosteoarthritis
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Deep learning for large scale MRI-based morphological phenotyping of osteoarthritis

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