Fibromyalgia Detection and Diagnosis: A Systematic Review of Data-Driven Approaches and Clinical Implications
Fibromyalgia syndrome (FMS) is a long-lasting medical condition that poses significant challenges for diagnosis and management because of its complex and poorly understood nature. It affects millions of people around the globe, predominantly women, causing widespread pain, fatigue, cognitive impairm...
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IEEE
2025-01-01
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| Online Access: | https://ieeexplore.ieee.org/document/10872952/ |
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| author | Anirudh Atmakuru Subrata Chakraborty Massimo Salvi Oliver Faust Prabal Datta Barua Makiko Kobayashi Ru San Tan Filippo Molinari Abdul Hafeez-Baig U. Rajendra Acharya |
| author_facet | Anirudh Atmakuru Subrata Chakraborty Massimo Salvi Oliver Faust Prabal Datta Barua Makiko Kobayashi Ru San Tan Filippo Molinari Abdul Hafeez-Baig U. Rajendra Acharya |
| author_sort | Anirudh Atmakuru |
| collection | DOAJ |
| description | Fibromyalgia syndrome (FMS) is a long-lasting medical condition that poses significant challenges for diagnosis and management because of its complex and poorly understood nature. It affects millions of people around the globe, predominantly women, causing widespread pain, fatigue, cognitive impairments, and mood disturbances. The lack of objective measures to address FMS complicates its assessment, often leading to delayed or misdiagnosed cases. By hindering daily activities and productivity, FMS negatively impacts the quality of the patient’s life. Innovative approaches that use medical data, such as bio-signals and bioimaging, combined with machine learning techniques, hold the promise of deepening our knowledge of FMS, which might in turn lead to systems that offer efficient, precise, and personalized physician support. Furthermore, artificial intelligence-driven identification of biomarkers and patient subgroups could improve FMS management. In this systematic review, we explore the role of artificial intelligence in understanding FMS pathophysiology, discuss the present limitations, and shed light on future research avenues, aiming to translate findings into improved clinical outcomes. |
| format | Article |
| id | doaj-art-e6376bb74a924bc78bc7ec4e1add004a |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-e6376bb74a924bc78bc7ec4e1add004a2025-02-12T00:02:17ZengIEEEIEEE Access2169-35362025-01-0113250262504410.1109/ACCESS.2025.353919610872952Fibromyalgia Detection and Diagnosis: A Systematic Review of Data-Driven Approaches and Clinical ImplicationsAnirudh Atmakuru0https://orcid.org/0000-0003-1448-7334Subrata Chakraborty1https://orcid.org/0000-0002-0102-5424Massimo Salvi2https://orcid.org/0000-0001-7225-7401Oliver Faust3Prabal Datta Barua4Makiko Kobayashi5https://orcid.org/0000-0003-4711-530XRu San Tan6https://orcid.org/0000-0003-2086-6517Filippo Molinari7https://orcid.org/0000-0003-1150-2244Abdul Hafeez-Baig8U. Rajendra Acharya9https://orcid.org/0000-0003-2689-8552Manning College of Information and Computer Sciences, University of Massachusetts at Amherst, Amherst, MA, USASchool of Science and Technology, Faculty of Science, Agriculture, Business and Law, University of New England, Armidale, NSW, AustraliaDepartment of Electronics and Telecommunications, Politecnico di Torino, Turin, ItalySchool of Computing and Information Science, Anglia Ruskin University Cambridge Campus, Cambridge, U.K.School of Business (Information System), University of Southern Queensland, Toowoomba, QLD, AustraliaDepartment of Computer Science and Electrical Engineering, Kumamoto University, Kumamoto, JapanDepartment of Cardiology, National Heart Centre Singapore, Cluny Road, SingaporeDepartment of Electronics and Telecommunications, Politecnico di Torino, Turin, ItalySchool of Business (Information System), University of Southern Queensland, Toowoomba, QLD, AustraliaInternational Research Organization for Advanced Science and Technology (IROAST), Kumamoto University, Kumamoto, JapanFibromyalgia syndrome (FMS) is a long-lasting medical condition that poses significant challenges for diagnosis and management because of its complex and poorly understood nature. It affects millions of people around the globe, predominantly women, causing widespread pain, fatigue, cognitive impairments, and mood disturbances. The lack of objective measures to address FMS complicates its assessment, often leading to delayed or misdiagnosed cases. By hindering daily activities and productivity, FMS negatively impacts the quality of the patient’s life. Innovative approaches that use medical data, such as bio-signals and bioimaging, combined with machine learning techniques, hold the promise of deepening our knowledge of FMS, which might in turn lead to systems that offer efficient, precise, and personalized physician support. Furthermore, artificial intelligence-driven identification of biomarkers and patient subgroups could improve FMS management. In this systematic review, we explore the role of artificial intelligence in understanding FMS pathophysiology, discuss the present limitations, and shed light on future research avenues, aiming to translate findings into improved clinical outcomes.https://ieeexplore.ieee.org/document/10872952/Artificial intelligencefibromyalgiaFMS detectionmachine learningpain assessment |
| spellingShingle | Anirudh Atmakuru Subrata Chakraborty Massimo Salvi Oliver Faust Prabal Datta Barua Makiko Kobayashi Ru San Tan Filippo Molinari Abdul Hafeez-Baig U. Rajendra Acharya Fibromyalgia Detection and Diagnosis: A Systematic Review of Data-Driven Approaches and Clinical Implications IEEE Access Artificial intelligence fibromyalgia FMS detection machine learning pain assessment |
| title | Fibromyalgia Detection and Diagnosis: A Systematic Review of Data-Driven Approaches and Clinical Implications |
| title_full | Fibromyalgia Detection and Diagnosis: A Systematic Review of Data-Driven Approaches and Clinical Implications |
| title_fullStr | Fibromyalgia Detection and Diagnosis: A Systematic Review of Data-Driven Approaches and Clinical Implications |
| title_full_unstemmed | Fibromyalgia Detection and Diagnosis: A Systematic Review of Data-Driven Approaches and Clinical Implications |
| title_short | Fibromyalgia Detection and Diagnosis: A Systematic Review of Data-Driven Approaches and Clinical Implications |
| title_sort | fibromyalgia detection and diagnosis a systematic review of data driven approaches and clinical implications |
| topic | Artificial intelligence fibromyalgia FMS detection machine learning pain assessment |
| url | https://ieeexplore.ieee.org/document/10872952/ |
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