Cargando…
Magnetic resonance imaging assessments for knee segmentation and their use in combination with machine/deep learning as predictors of early osteoarthritis diagnosis and prognosis
Knee osteoarthritis (OA) is a prevalent and disabling disease that can develop over decades. This disease is heterogeneous and involves structural changes in the whole joint, encompassing multiple tissue types. Detecting OA before the onset of irreversible changes is crucial for early management, an...
Autores principales: | , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
SAGE Publications
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10155034/ https://www.ncbi.nlm.nih.gov/pubmed/37151912 http://dx.doi.org/10.1177/1759720X231165560 |
_version_ | 1785036250959314944 |
---|---|
author | Martel-Pelletier, Johanne Paiement, Patrice Pelletier, Jean-Pierre |
author_facet | Martel-Pelletier, Johanne Paiement, Patrice Pelletier, Jean-Pierre |
author_sort | Martel-Pelletier, Johanne |
collection | PubMed |
description | Knee osteoarthritis (OA) is a prevalent and disabling disease that can develop over decades. This disease is heterogeneous and involves structural changes in the whole joint, encompassing multiple tissue types. Detecting OA before the onset of irreversible changes is crucial for early management, and this could be achieved by allowing knee tissue visualization and quantifying their changes over time. Although some imaging modalities are available for knee structure assessment, magnetic resonance imaging (MRI) is preferred. This narrative review looks at existing literature, first on MRI-developed approaches for evaluating knee articular tissues, and second on prediction using machine/deep-learning-based methodologies and MRI as input or outcome for early OA diagnosis and prognosis. A substantial number of MRI methodologies have been developed to assess several knee tissues in a semi-quantitative and quantitative fashion using manual, semi-automated and fully automated systems. This dynamic field has grown substantially since the advent of machine/deep learning. Another active area is predictive modelling using machine/deep-learning methodologies enabling robust early OA diagnosis/prognosis. Moreover, incorporating MRI markers as input/outcome in such predictive models is important for a more accurate OA structural diagnosis/prognosis. The main limitation of their usage is the ability to move them in rheumatology practice. In conclusion, MRI knee tissue determination and quantification provide early indicators for individuals at high risk of developing this disease or for patient prognosis. Such assessment of knee tissues, combined with the development of models/tools from machine/deep learning using, in addition to other parameters, MRI markers for early diagnosis/prognosis, will maximize opportunities for individualized risk assessment for use in clinical practice permitting precision medicine. Future efforts should be made to integrate such prediction models into open access, allowing early disease management to prevent or delay the OA outcome. |
format | Online Article Text |
id | pubmed-10155034 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | SAGE Publications |
record_format | MEDLINE/PubMed |
spelling | pubmed-101550342023-05-04 Magnetic resonance imaging assessments for knee segmentation and their use in combination with machine/deep learning as predictors of early osteoarthritis diagnosis and prognosis Martel-Pelletier, Johanne Paiement, Patrice Pelletier, Jean-Pierre Ther Adv Musculoskelet Dis Addressing the Challenges Associated with the Development, Testing and Approval of Novel Therapeutics for Osteoarthritis Knee osteoarthritis (OA) is a prevalent and disabling disease that can develop over decades. This disease is heterogeneous and involves structural changes in the whole joint, encompassing multiple tissue types. Detecting OA before the onset of irreversible changes is crucial for early management, and this could be achieved by allowing knee tissue visualization and quantifying their changes over time. Although some imaging modalities are available for knee structure assessment, magnetic resonance imaging (MRI) is preferred. This narrative review looks at existing literature, first on MRI-developed approaches for evaluating knee articular tissues, and second on prediction using machine/deep-learning-based methodologies and MRI as input or outcome for early OA diagnosis and prognosis. A substantial number of MRI methodologies have been developed to assess several knee tissues in a semi-quantitative and quantitative fashion using manual, semi-automated and fully automated systems. This dynamic field has grown substantially since the advent of machine/deep learning. Another active area is predictive modelling using machine/deep-learning methodologies enabling robust early OA diagnosis/prognosis. Moreover, incorporating MRI markers as input/outcome in such predictive models is important for a more accurate OA structural diagnosis/prognosis. The main limitation of their usage is the ability to move them in rheumatology practice. In conclusion, MRI knee tissue determination and quantification provide early indicators for individuals at high risk of developing this disease or for patient prognosis. Such assessment of knee tissues, combined with the development of models/tools from machine/deep learning using, in addition to other parameters, MRI markers for early diagnosis/prognosis, will maximize opportunities for individualized risk assessment for use in clinical practice permitting precision medicine. Future efforts should be made to integrate such prediction models into open access, allowing early disease management to prevent or delay the OA outcome. SAGE Publications 2023-04-28 /pmc/articles/PMC10155034/ /pubmed/37151912 http://dx.doi.org/10.1177/1759720X231165560 Text en © The Author(s), 2023 https://creativecommons.org/licenses/by-nc/4.0/This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 License (https://creativecommons.org/licenses/by-nc/4.0/) which permits non-commercial use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access page (https://us.sagepub.com/en-us/nam/open-access-at-sage). |
spellingShingle | Addressing the Challenges Associated with the Development, Testing and Approval of Novel Therapeutics for Osteoarthritis Martel-Pelletier, Johanne Paiement, Patrice Pelletier, Jean-Pierre Magnetic resonance imaging assessments for knee segmentation and their use in combination with machine/deep learning as predictors of early osteoarthritis diagnosis and prognosis |
title | Magnetic resonance imaging assessments for knee segmentation and
their use in combination with machine/deep learning as predictors of early
osteoarthritis diagnosis and prognosis |
title_full | Magnetic resonance imaging assessments for knee segmentation and
their use in combination with machine/deep learning as predictors of early
osteoarthritis diagnosis and prognosis |
title_fullStr | Magnetic resonance imaging assessments for knee segmentation and
their use in combination with machine/deep learning as predictors of early
osteoarthritis diagnosis and prognosis |
title_full_unstemmed | Magnetic resonance imaging assessments for knee segmentation and
their use in combination with machine/deep learning as predictors of early
osteoarthritis diagnosis and prognosis |
title_short | Magnetic resonance imaging assessments for knee segmentation and
their use in combination with machine/deep learning as predictors of early
osteoarthritis diagnosis and prognosis |
title_sort | magnetic resonance imaging assessments for knee segmentation and
their use in combination with machine/deep learning as predictors of early
osteoarthritis diagnosis and prognosis |
topic | Addressing the Challenges Associated with the Development, Testing and Approval of Novel Therapeutics for Osteoarthritis |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10155034/ https://www.ncbi.nlm.nih.gov/pubmed/37151912 http://dx.doi.org/10.1177/1759720X231165560 |
work_keys_str_mv | AT martelpelletierjohanne magneticresonanceimagingassessmentsforkneesegmentationandtheiruseincombinationwithmachinedeeplearningaspredictorsofearlyosteoarthritisdiagnosisandprognosis AT paiementpatrice magneticresonanceimagingassessmentsforkneesegmentationandtheiruseincombinationwithmachinedeeplearningaspredictorsofearlyosteoarthritisdiagnosisandprognosis AT pelletierjeanpierre magneticresonanceimagingassessmentsforkneesegmentationandtheiruseincombinationwithmachinedeeplearningaspredictorsofearlyosteoarthritisdiagnosisandprognosis |