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SYSTEMATIC REVIEW article

Front. Neurol.
Sec. Artificial Intelligence in Neurology
Volume 15 - 2024 | doi: 10.3389/fneur.2024.1413071
This article is part of the Research Topic Exploring the Future of Neurology: How AI is Revolutionizing Diagnoses, Treatments, and Beyond View all 9 articles

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

Provisionally accepted
Mahsa Asadi Anar Mahsa Asadi Anar 1*Milad Yousefi Milad Yousefi 2Matin Akhbari Matin Akhbari 3Zhina Mohamadi Zhina Mohamadi 4Shaghayegh Karami Shaghayegh Karami 5Hediyeh Dasoomi Hediyeh Dasoomi 6Alireza Atabi Alireza Atabi 6Seyed Amirali Sarkeshikian Seyed Amirali Sarkeshikian 1Mahdi Abdoullahi Dehaki Mahdi Abdoullahi Dehaki 7Hesam Bayati Hesam Bayati 8Negin Mashayekhi Negin Mashayekhi 9Shirin Varmazyar Shirin Varmazyar 10Zahra Rahimian Zahra Rahimian 11
  • 1 Faculty of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran
  • 2 Shahid Beheshti University, Tehran, Tehran, Iran
  • 3 Istanbul Yeni Yüzyıl University, Istanbul, Türkiye
  • 4 Kermanshah University of Medical Sciences, Kermanshah, Kerman, Iran
  • 5 Tehran University of Medical Sciences, Tehran, Tehran, Iran
  • 6 Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Khuzestan, Iran
  • 7 Science and Research Branch, Islamic Azad University, Tehran, Tehran, Iran
  • 8 Shahid Beheshti University of Medical Sciences, Tehran, Tehran, Iran
  • 9 Bahçeşehir University, Istanbul, Türkiye
  • 10 Islamic Azad University, Shahrood, Shahrood, Semnan, Iran
  • 11 Shiraz University of Medical Sciences, Shiraz, Fars, Iran

The final, formatted version of the article will be published soon.

    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 7069 research studies, of which 1365 items were duplicates and thus removed. 4334 studies were screened, and 108 articles met the criteria for inclusion after preprocessing. 12 ML algorithms were observed for dementia, showing promise in early detection. 18 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. 14 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.

    Keywords: SBUMS, Arabi Ave, Daneshjoo Blvd, Velenjak, Tehran, Iran 19839-63113 neurodegenerative disorder, Neurocognitive disorder, machine learning, Early detection, AI

    Received: 06 Apr 2024; Accepted: 05 Nov 2024.

    Copyright: © 2024 Asadi Anar, Yousefi, Akhbari, Mohamadi, Karami, Dasoomi, Atabi, Sarkeshikian, Abdoullahi Dehaki, Bayati, Mashayekhi, Varmazyar and Rahimian. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Mahsa Asadi Anar, Faculty of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.