Please use this identifier to cite or link to this item:
https://hdl.handle.net/11499/58213
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DC Field | Value | Language |
---|---|---|
dc.contributor.author | Isaac, Amanda | - |
dc.contributor.author | Akdogan, Asli Irmak | - |
dc.contributor.author | Dalili, Danoob | - |
dc.contributor.author | Saber, Nuran | - |
dc.contributor.author | Drobny, David | - |
dc.contributor.author | Guglielmi, Giuseppe | - |
dc.contributor.author | Modat, Marc | - |
dc.date.accessioned | 2024-11-20T18:03:31Z | - |
dc.date.available | 2024-11-20T18:03:31Z | - |
dc.date.issued | 2024 | - |
dc.identifier.issn | 1089-7860 | - |
dc.identifier.issn | 1098-898X | - |
dc.identifier.uri | https://doi.org/10.1055/s-0044-1789218 | - |
dc.identifier.uri | https://hdl.handle.net/11499/58213 | - |
dc.description.abstract | Artificial intelligence (AI) has significantly impacted the field of medical imaging, particularly in diagnosing and managing metabolic bone diseases (MBDs) such as osteoporosis and osteopenia, Paget's disease, osteomalacia, and rickets, as well as rare conditions such as osteitis fibrosa cystica and osteogenesis imperfecta. This article provides an in-depth analysis of AI techniques used in imaging these conditions, recent advancements, and their clinical applications. It also explores ethical considerations and future perspectives. Through comprehensive examination and case studies, we highlight the transformative potential of AI in enhancing diagnostic accuracy, improving patient outcomes, and contributing to personalized medicine. By integrating AI with existing imaging techniques, we can significantly enhance the capabilities of medical imaging in diagnosing, monitoring, and treating MBDs. We also provide a comprehensive overview of the current state, challenges, and future prospects of AI applications in this crucial area of health care. | en_US |
dc.language.iso | en | en_US |
dc.publisher | Thieme Medical Publ Inc | en_US |
dc.relation.ispartof | Seminars in Musculoskeletal Radiology | en_US |
dc.rights | info:eu-repo/semantics/closedAccess | en_US |
dc.subject | artificial intelligence | en_US |
dc.subject | deep learning | en_US |
dc.subject | machine learning | en_US |
dc.subject | natural language processing | en_US |
dc.subject | computer vision | en_US |
dc.subject | Age Assessment | en_US |
dc.subject | Spine | en_US |
dc.subject | Prediction | en_US |
dc.subject | Models | en_US |
dc.subject | Interventions | en_US |
dc.subject | Population | en_US |
dc.subject | Validation | en_US |
dc.subject | Fractures | en_US |
dc.subject | Medicine | en_US |
dc.subject | Impact | en_US |
dc.title | Artificial Intelligence Applications for Imaging Metabolic Bone Diseases | en_US |
dc.type | Article | en_US |
dc.identifier.volume | 28 | en_US |
dc.identifier.issue | 5 | en_US |
dc.identifier.startpage | 610 | en_US |
dc.identifier.endpage | 619 | en_US |
dc.department | Pamukkale University | en_US |
dc.authorid | Isaac, Amanda/0000-0001-5827-0562 | - |
dc.identifier.doi | 10.1055/s-0044-1789218 | - |
dc.relation.publicationcategory | Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı | en_US |
dc.authorscopusid | 57192649326 | - |
dc.authorscopusid | 57211281448 | - |
dc.authorscopusid | 57203802559 | - |
dc.authorscopusid | 59369447800 | - |
dc.authorscopusid | 57204489643 | - |
dc.authorscopusid | 7005143760 | - |
dc.authorscopusid | 24376919100 | - |
dc.authorwosid | Isaac, Amanda/AAF-2563-2020 | - |
dc.identifier.pmid | 39406223 | en_US |
dc.identifier.scopus | 2-s2.0-85206551043 | en_US |
dc.identifier.wos | WOS:001339263400005 | en_US |
dc.institutionauthor | … | - |
item.languageiso639-1 | en | - |
item.fulltext | No Fulltext | - |
item.grantfulltext | none | - |
item.openairetype | Article | - |
item.cerifentitytype | Publications | - |
item.openairecristype | http://purl.org/coar/resource_type/c_18cf | - |
Appears in Collections: | PubMed İndeksli Yayınlar Koleksiyonu / PubMed Indexed Publications Collection Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection |
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