Machine learning based algorithms for virtual early detection and screening of neurodegenerative and neurocognitive disorders: a systematic-review
Background and aimNeurodegenerative disorders (e.g., Alzheimer’s, Parkinson’s) lead to neuronal loss; neurocognitive disorders (e.g., delirium, dementia) show cognitive decline. Early detection is crucial for effective management. Machine learning aids in more precise disease identification, potenti...
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Frontiers Media S.A.
2024-12-01
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| Series: | Frontiers in Neurology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fneur.2024.1413071/full |
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| author | Milad Yousefi Matin Akhbari Zhina Mohamadi Shaghayegh Karami Hediyeh Dasoomi Alireza Atabi Seyed Amirali Sarkeshikian Mahdi Abdoullahi Dehaki Hesam Bayati Negin Mashayekhi Shirin Varmazyar Zahra Rahimian Mahsa Asadi Anar Daniel Shafiei Alireza Mohebbi |
| author_facet | Milad Yousefi Matin Akhbari Zhina Mohamadi Shaghayegh Karami Hediyeh Dasoomi Alireza Atabi Seyed Amirali Sarkeshikian Mahdi Abdoullahi Dehaki Hesam Bayati Negin Mashayekhi Shirin Varmazyar Zahra Rahimian Mahsa Asadi Anar Daniel Shafiei Alireza Mohebbi |
| author_sort | Milad Yousefi |
| collection | DOAJ |
| description | Background and aimNeurodegenerative disorders (e.g., Alzheimer’s, Parkinson’s) lead to neuronal loss; neurocognitive disorders (e.g., delirium, dementia) show cognitive decline. Early detection is crucial for effective management. Machine learning aids in more precise disease identification, potentially transforming healthcare. This comprehensive systematic review discusses how machine learning (ML), can enhance early detection of these disorders, surpassing traditional diagnostics’ constraints.MethodsIn this review, databases were examined up to August 15th, 2023, for ML data on neurodegenerative and neurocognitive diseases using PubMed, Scopus, Google Scholar, and Web of Science. Two investigators used the RAYYAN intelligence tool for systematic reviews to conduct the screening. Six blinded reviewers reviewed titles/abstracts. Cochrane risk of bias tool was used for quality assessment.ResultsOur search found 7,069 research studies, of which 1,365 items were duplicates and thus removed. Four thousand three hundred and thirty four studies were screened, and 108 articles met the criteria for inclusion after preprocessing. Twelve ML algorithms were observed for dementia, showing promise in early detection. Eighteen ML algorithms were identified for Parkinson’s, each effective in detection and diagnosis. Studies emphasized that ML algorithms are necessary for Alzheimer’s to be successful. Fourteen ML algorithms were discovered for mild cognitive impairment, with LASSO logistic regression being the only one with unpromising results.ConclusionThis review emphasizes the pressing necessity of integrating verified digital health resources into conventional medical practice. This integration may signify a new era in the early detection of neurodegenerative and neurocognitive illnesses, potentially changing the course of these conditions for millions globally. This study showcases specific and statistically significant findings to illustrate the progress in the area and the prospective influence of these advancements on the global management of neurocognitive and neurodegenerative illnesses. |
| format | Article |
| id | doaj-art-9cc2446beb8140d08d91d5642e2cf7a4 |
| institution | Kabale University |
| issn | 1664-2295 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Neurology |
| spelling | doaj-art-9cc2446beb8140d08d91d5642e2cf7a42024-12-09T06:29:08ZengFrontiers Media S.A.Frontiers in Neurology1664-22952024-12-011510.3389/fneur.2024.14130711413071Machine learning based algorithms for virtual early detection and screening of neurodegenerative and neurocognitive disorders: a systematic-reviewMilad Yousefi0Matin Akhbari1Zhina Mohamadi2Shaghayegh Karami3Hediyeh Dasoomi4Alireza Atabi5Seyed Amirali Sarkeshikian6Mahdi Abdoullahi Dehaki7Hesam Bayati8Negin Mashayekhi9Shirin Varmazyar10Zahra Rahimian11Mahsa Asadi Anar12Daniel Shafiei13Alireza Mohebbi14Institute for Cognitive and Brain Sciences, Shahid Beheshti University, Tehran, IranFaculty of Medicine, Istanbul Yeni Yuzyil University, Istanbul, TürkiyeSchool of Medicine, Kermanshah University of Medical Sciences, Kermanshah, IranSchool of Medicine, Tehran University of Medical Sciences, Tehran, IranStudent Research Committee, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, IranSchool of Medicine, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, IranSchool of Medicine, Shahid Beheshti University of Medical Science, Tehran, IranMaster’s of AI Engineering, Islamic Azad University Tehran Science and Research Branch, Tehran, IranDepartment of Radiology, Shahid Beheshti University of Medical Sciences, Tehran, Iran0Department of Neuroscience, Bahçeşehir University, Istanbul, Türkiye1School of Medicine, Shahroud University of Medical Sciences, Shahrud, Iran2School of Medicine, Shiraz University of Medical Sciences, Shiraz, Iran3Student Research Committee, Shahid Beheshti University of Medical Sciences, Tehran, Iran4School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran5Students Research Committee, Ardabil University of Medical Sciences, Ardabil, IranBackground and aimNeurodegenerative disorders (e.g., Alzheimer’s, Parkinson’s) lead to neuronal loss; neurocognitive disorders (e.g., delirium, dementia) show cognitive decline. Early detection is crucial for effective management. Machine learning aids in more precise disease identification, potentially transforming healthcare. This comprehensive systematic review discusses how machine learning (ML), can enhance early detection of these disorders, surpassing traditional diagnostics’ constraints.MethodsIn this review, databases were examined up to August 15th, 2023, for ML data on neurodegenerative and neurocognitive diseases using PubMed, Scopus, Google Scholar, and Web of Science. Two investigators used the RAYYAN intelligence tool for systematic reviews to conduct the screening. Six blinded reviewers reviewed titles/abstracts. Cochrane risk of bias tool was used for quality assessment.ResultsOur search found 7,069 research studies, of which 1,365 items were duplicates and thus removed. Four thousand three hundred and thirty four studies were screened, and 108 articles met the criteria for inclusion after preprocessing. Twelve ML algorithms were observed for dementia, showing promise in early detection. Eighteen ML algorithms were identified for Parkinson’s, each effective in detection and diagnosis. Studies emphasized that ML algorithms are necessary for Alzheimer’s to be successful. Fourteen ML algorithms were discovered for mild cognitive impairment, with LASSO logistic regression being the only one with unpromising results.ConclusionThis review emphasizes the pressing necessity of integrating verified digital health resources into conventional medical practice. This integration may signify a new era in the early detection of neurodegenerative and neurocognitive illnesses, potentially changing the course of these conditions for millions globally. This study showcases specific and statistically significant findings to illustrate the progress in the area and the prospective influence of these advancements on the global management of neurocognitive and neurodegenerative illnesses.https://www.frontiersin.org/articles/10.3389/fneur.2024.1413071/fullneurodegenerative disorderneurocognitive disordermachine learningearly detectionAI |
| spellingShingle | Milad Yousefi Matin Akhbari Zhina Mohamadi Shaghayegh Karami Hediyeh Dasoomi Alireza Atabi Seyed Amirali Sarkeshikian Mahdi Abdoullahi Dehaki Hesam Bayati Negin Mashayekhi Shirin Varmazyar Zahra Rahimian Mahsa Asadi Anar Daniel Shafiei Alireza Mohebbi Machine learning based algorithms for virtual early detection and screening of neurodegenerative and neurocognitive disorders: a systematic-review Frontiers in Neurology neurodegenerative disorder neurocognitive disorder machine learning early detection AI |
| title | Machine learning based algorithms for virtual early detection and screening of neurodegenerative and neurocognitive disorders: a systematic-review |
| title_full | Machine learning based algorithms for virtual early detection and screening of neurodegenerative and neurocognitive disorders: a systematic-review |
| title_fullStr | Machine learning based algorithms for virtual early detection and screening of neurodegenerative and neurocognitive disorders: a systematic-review |
| title_full_unstemmed | Machine learning based algorithms for virtual early detection and screening of neurodegenerative and neurocognitive disorders: a systematic-review |
| title_short | Machine learning based algorithms for virtual early detection and screening of neurodegenerative and neurocognitive disorders: a systematic-review |
| title_sort | machine learning based algorithms for virtual early detection and screening of neurodegenerative and neurocognitive disorders a systematic review |
| topic | neurodegenerative disorder neurocognitive disorder machine learning early detection AI |
| url | https://www.frontiersin.org/articles/10.3389/fneur.2024.1413071/full |
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