Machine learning based algorithms for virtual early detection and screening of neurodegenerative and neurocognitive disorders: a systematic-review

被引:11
作者
Yousefi, Milad [1 ]
Akhbari, Matin [2 ]
Mohamadi, Zhina [3 ]
Karami, Shaghayegh [4 ]
Dasoomi, Hediyeh [5 ]
Atabi, Alireza [6 ]
Sarkeshikian, Seyed Amirali [7 ]
Dehaki, Mahdi Abdoullahi [8 ]
Bayati, Hesam [9 ]
Mashayekhi, Negin [10 ]
Varmazyar, Shirin [11 ]
Rahimian, Zahra [12 ]
Asadi Anar, Mahsa [13 ]
Shafiei, Daniel [14 ]
Mohebbi, Alireza [15 ]
机构
[1] Shahid Beheshti Univ, Inst Cognit & Brain Sci, Tehran, Iran
[2] Istanbul Yeni Yuzyil Univ, Fac Med, Istanbul, Turkiye
[3] Kermanshah Univ Med Sci, Sch Med, Kermanshah, Iran
[4] Univ Tehran Med Sci, Sch Med, Tehran, Iran
[5] Ahvaz Jundishapur Univ Med Sci, Student Res Comm, Ahvaz, Iran
[6] Ahvaz Jundishapur Univ Med Sci, Sch Med, Ahvaz, Iran
[7] Shahid Beheshti Univ Med Sci, Sch Med, Tehran, Iran
[8] Islamic Azad Univ Tehran Sci, Res Branch, Tehran, Iran
[9] Shahid Beheshti Univ Med Sci, Dept Radiol, Tehran, Iran
[10] Bahcesehir Univ, Dept Pharmacol, Istanbul, Turkiye
[11] Shahroud Univ Med Sci, Sch Med, Shahrud, Iran
[12] Shiraz Univ Med Sci, Sch Med, Shiraz, Iran
[13] Shahid Beheshti Univ Med Sci, Student Res Comm, Tehran, Iran
[14] Shahid Beheshti Univ Med Sci, Sch Med, Tehran, Iran
[15] Ardabil Univ Med Sci, Students Res Comm, Ardebil, Iran
来源
FRONTIERS IN NEUROLOGY | 2024年 / 15卷
关键词
neurodegenerative disorder; neurocognitive disorder; machine learning; early detection; AI; ALZHEIMERS-DISEASE; EARLY-DIAGNOSIS; PARKINSONS-DISEASE; MULTIPLE-SCLEROSIS; NETWORK; PREDICTION; FEATURES; CONVERSION; IMAGES; MCI;
D O I
10.3389/fneur.2024.1413071
中图分类号
R74 [神经病学与精神病学];
学科分类号
摘要
Background and aim Neurodegenerative 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. Methods In 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. Results Our 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. Conclusion This 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.
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