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Filtrado por: Contributor Jio Institute CVMI-Computer Vision in Medical Imaging Project Palabra clave Fractures Tipo de recurso Article

1. Bone Fracture Detection Using Deep Supervised Learning from Radiological Images: A Paradigm Shift

Descripción:

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.

Palabra clave:

Deep Learning, Fractures, Bone Imaging, Artificial Intelligence, Radiology, and Computer Vision

Tema:

Data Science and Artificial Intelligence

Creador:

Roy, Sudipta and Meena, Tanushree

Contribuidor:

Jio Institute CVMI-Computer Vision in Medical Imaging Project

Owner:

n.sakthivel@jioinstitute.edu.in

Editor:

MDPI

Ubicación:

Switzerland

Idioma:

English

Fecha de Subida:

10-02-2023

Fecha Modificada:

16-02-2023

Fecha de Creacion:

01-10-2022

Rights Statement Tesim:

In Copyright

License Tesim:

All rights reserved

Resource Type:

Article

Identificador:

10.3390/diagnostics12102420

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