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Bone Fracture Detection Using Deep Supervised Learning from Radiological Images: A Paradigm Shift
Bone diseases are common and can result in various musculoskeletal conditions (MC). An estimated 1.71 billion patients suffer from musculoskeletal problems worldwide. Apart from musculoskeletal fractures, femoral neck injuries, knee osteoarthritis, and fractures are very common bone diseases, and th...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9600559/ https://www.ncbi.nlm.nih.gov/pubmed/36292109 http://dx.doi.org/10.3390/diagnostics12102420 |
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author | Meena, Tanushree Roy, Sudipta |
author_facet | Meena, Tanushree Roy, Sudipta |
author_sort | Meena, Tanushree |
collection | PubMed |
description | Bone diseases are common and can result in various musculoskeletal conditions (MC). An estimated 1.71 billion patients suffer from musculoskeletal problems worldwide. Apart from musculoskeletal fractures, femoral neck injuries, knee osteoarthritis, and fractures are very common bone diseases, and the rate is expected to double in the next 30 years. Therefore, proper and timely diagnosis and treatment of a fractured patient are crucial. Contrastingly, missed fractures are a common prognosis failure in accidents and emergencies. This causes complications and delays in patients’ treatment and care. These days, artificial intelligence (AI) and, more specifically, deep learning (DL) are receiving significant attention to assist radiologists in bone fracture detection. DL can be widely used in medical image analysis. Some studies in traumatology and orthopaedics have shown the use and potential of DL in diagnosing fractures and diseases from radiographs. In this systematic review, we provide an overview of the use of DL in bone imaging to help radiologists to detect various abnormalities, particularly fractures. We have also discussed the challenges and problems faced in the DL-based method, and the future of DL in bone imaging. |
format | Online Article Text |
id | pubmed-9600559 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-96005592022-10-27 Bone Fracture Detection Using Deep Supervised Learning from Radiological Images: A Paradigm Shift Meena, Tanushree Roy, Sudipta Diagnostics (Basel) Review Bone diseases are common and can result in various musculoskeletal conditions (MC). An estimated 1.71 billion patients suffer from musculoskeletal problems worldwide. Apart from musculoskeletal fractures, femoral neck injuries, knee osteoarthritis, and fractures are very common bone diseases, and the rate is expected to double in the next 30 years. Therefore, proper and timely diagnosis and treatment of a fractured patient are crucial. Contrastingly, missed fractures are a common prognosis failure in accidents and emergencies. This causes complications and delays in patients’ treatment and care. These days, artificial intelligence (AI) and, more specifically, deep learning (DL) are receiving significant attention to assist radiologists in bone fracture detection. DL can be widely used in medical image analysis. Some studies in traumatology and orthopaedics have shown the use and potential of DL in diagnosing fractures and diseases from radiographs. In this systematic review, we provide an overview of the use of DL in bone imaging to help radiologists to detect various abnormalities, particularly fractures. We have also discussed the challenges and problems faced in the DL-based method, and the future of DL in bone imaging. MDPI 2022-10-07 /pmc/articles/PMC9600559/ /pubmed/36292109 http://dx.doi.org/10.3390/diagnostics12102420 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Review Meena, Tanushree Roy, Sudipta Bone Fracture Detection Using Deep Supervised Learning from Radiological Images: A Paradigm Shift |
title | Bone Fracture Detection Using Deep Supervised Learning from Radiological Images: A Paradigm Shift |
title_full | Bone Fracture Detection Using Deep Supervised Learning from Radiological Images: A Paradigm Shift |
title_fullStr | Bone Fracture Detection Using Deep Supervised Learning from Radiological Images: A Paradigm Shift |
title_full_unstemmed | Bone Fracture Detection Using Deep Supervised Learning from Radiological Images: A Paradigm Shift |
title_short | Bone Fracture Detection Using Deep Supervised Learning from Radiological Images: A Paradigm Shift |
title_sort | bone fracture detection using deep supervised learning from radiological images: a paradigm shift |
topic | Review |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9600559/ https://www.ncbi.nlm.nih.gov/pubmed/36292109 http://dx.doi.org/10.3390/diagnostics12102420 |
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