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Kale M, Wankhede N, Pawar R, Ballal S, Kumawat R, Goswami M, Khalid M, Taksande B, Upaganlawar A, Umekar M, Kopalli SR, Koppula S. AI-driven innovations in Alzheimer's disease: Integrating early diagnosis, personalized treatment, and prognostic modelling. Ageing Res Rev 2024; 101:102497. [PMID: 39293530 DOI: 10.1016/j.arr.2024.102497] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2024] [Revised: 08/14/2024] [Accepted: 09/04/2024] [Indexed: 09/20/2024]
Abstract
Alzheimer's disease (AD) presents a significant challenge in neurodegenerative research and clinical practice due to its complex etiology and progressive nature. The integration of artificial intelligence (AI) into the diagnosis, treatment, and prognostic modelling of AD holds promising potential to transform the landscape of dementia care. This review explores recent advancements in AI applications across various stages of AD management. In early diagnosis, AI-enhanced neuroimaging techniques, including MRI, PET, and CT scans, enable precise detection of AD biomarkers. Machine learning models analyze these images to identify patterns indicative of early cognitive decline. Additionally, AI algorithms are employed to detect genetic and proteomic biomarkers, facilitating early intervention. Cognitive and behavioral assessments have also benefited from AI, with tools that enhance the accuracy of neuropsychological tests and analyze speech and language patterns for early signs of dementia. Personalized treatment strategies have been revolutionized by AI-driven approaches. In drug discovery, virtual screening and drug repurposing, guided by predictive modelling, accelerate the identification of effective treatments. AI also aids in tailoring therapeutic interventions by predicting individual responses to treatments and monitoring patient progress, allowing for dynamic adjustment of care plans. Prognostic modelling, another critical area, utilizes AI to predict disease progression through longitudinal data analysis and risk prediction models. The integration of multi-modal data, combining clinical, genetic, and imaging information, enhances the accuracy of these predictions. Deep learning techniques are particularly effective in fusing diverse data types to uncover new insights into disease mechanisms and progression. Despite these advancements, challenges remain, including ethical considerations, data privacy, and the need for seamless integration of AI tools into clinical workflows. This review underscores the transformative potential of AI in AD management while highlighting areas for future research and development. By leveraging AI, the healthcare community can improve early diagnosis, personalize treatments, and predict disease outcomes more accurately, ultimately enhancing the quality of life for individuals with AD.
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Affiliation(s)
- Mayur Kale
- Smt. Kishoritai Bhoyar College of Pharmacy, Kamptee, Nagpur, Maharashtra 441002, India.
| | - Nitu Wankhede
- Smt. Kishoritai Bhoyar College of Pharmacy, Kamptee, Nagpur, Maharashtra 441002, India.
| | - Rupali Pawar
- Smt. Kishoritai Bhoyar College of Pharmacy, Kamptee, Nagpur, Maharashtra 441002, India.
| | - Suhas Ballal
- Department of Chemistry and Biochemistry, School of Sciences, JAIN (Deemed to be University), Bangalore, Karnataka, India.
| | - Rohit Kumawat
- Department of Neurology, National Institute of Medical Sciences, NIMS University, Jaipur, Rajasthan, India.
| | - Manish Goswami
- Chandigarh Pharmacy College, Chandigarh Group of Colleges, Jhanjeri, Mohali, Punjab 140307, India.
| | - Mohammad Khalid
- Department of pharmacognosy, College of Pharmacy, Prince Sattam Bin Abdulaziz University Alkharj, Saudi Arabia.
| | - Brijesh Taksande
- Smt. Kishoritai Bhoyar College of Pharmacy, Kamptee, Nagpur, Maharashtra 441002, India.
| | - Aman Upaganlawar
- SNJB's Shriman Sureshdada Jain College of Pharmacy, Neminagar, Chandwad, Nashik, Maharashtra, India.
| | - Milind Umekar
- Smt. Kishoritai Bhoyar College of Pharmacy, Kamptee, Nagpur, Maharashtra 441002, India.
| | - Spandana Rajendra Kopalli
- Department of Bioscience and Biotechnology, Sejong University, Gwangjin-gu, Seoul 05006, Republic of Korea.
| | - Sushruta Koppula
- College of Biomedical and Health Sciences, Konkuk University, Chungju-Si, Chungju-Si, Chungcheongbuk Do 27478, Republic of Korea.
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Theodorakis N, Feretzakis G, Tzelves L, Paxinou E, Hitas C, Vamvakou G, Verykios VS, Nikolaou M. Integrating Machine Learning with Multi-Omics Technologies in Geroscience: Towards Personalized Medicine. J Pers Med 2024; 14:931. [PMID: 39338186 PMCID: PMC11433587 DOI: 10.3390/jpm14090931] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2024] [Revised: 08/27/2024] [Accepted: 08/30/2024] [Indexed: 09/30/2024] Open
Abstract
Aging is a fundamental biological process characterized by a progressive decline in physiological functions and an increased susceptibility to diseases. Understanding aging at the molecular level is crucial for developing interventions that could delay or reverse its effects. This review explores the integration of machine learning (ML) with multi-omics technologies-including genomics, transcriptomics, epigenomics, proteomics, and metabolomics-in studying the molecular hallmarks of aging to develop personalized medicine interventions. These hallmarks include genomic instability, telomere attrition, epigenetic alterations, loss of proteostasis, disabled macroautophagy, deregulated nutrient sensing, mitochondrial dysfunction, cellular senescence, stem cell exhaustion, altered intercellular communication, chronic inflammation, and dysbiosis. Using ML to analyze big and complex datasets helps uncover detailed molecular interactions and pathways that play a role in aging. The advances of ML can facilitate the discovery of biomarkers and therapeutic targets, offering insights into personalized anti-aging strategies. With these developments, the future points toward a better understanding of the aging process, aiming ultimately to promote healthy aging and extend life expectancy.
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Affiliation(s)
- Nikolaos Theodorakis
- Department of Cardiology & 65+ Clinic, Amalia Fleming General Hospital, 14, 25th Martiou Str., 15127 Melissia, Greece
- School of Medicine, National and Kapodistrian University of Athens, 75 Mikras Asias, 11527 Athens, Greece
| | - Georgios Feretzakis
- School of Science and Technology, Hellenic Open University, 18 Aristotelous Str., 26335 Patras, Greece
| | - Lazaros Tzelves
- 2nd Department of Urology, Sismanoglio General Hospital, Sismanogliou 37, National and Kapodistrian University of Athens, 15126 Athens, Greece
| | - Evgenia Paxinou
- School of Science and Technology, Hellenic Open University, 18 Aristotelous Str., 26335 Patras, Greece
| | - Christos Hitas
- Department of Cardiology & 65+ Clinic, Amalia Fleming General Hospital, 14, 25th Martiou Str., 15127 Melissia, Greece
| | - Georgia Vamvakou
- Department of Cardiology & 65+ Clinic, Amalia Fleming General Hospital, 14, 25th Martiou Str., 15127 Melissia, Greece
| | - Vassilios S Verykios
- School of Science and Technology, Hellenic Open University, 18 Aristotelous Str., 26335 Patras, Greece
| | - Maria Nikolaou
- Department of Cardiology & 65+ Clinic, Amalia Fleming General Hospital, 14, 25th Martiou Str., 15127 Melissia, Greece
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Veneziani I, Grimaldi A, Marra A, Morini E, Culicetto L, Marino S, Quartarone A, Maresca G. Towards a Deeper Understanding: Utilizing Machine Learning to Investigate the Association between Obesity and Cognitive Decline-A Systematic Review. J Clin Med 2024; 13:2307. [PMID: 38673581 PMCID: PMC11051247 DOI: 10.3390/jcm13082307] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2024] [Revised: 04/09/2024] [Accepted: 04/13/2024] [Indexed: 04/28/2024] Open
Abstract
Background/Objectives: Several studies have shown a relation between obesity and cognitive decline, highlighting a significant global health challenge. In recent years, artificial intelligence (AI) and machine learning (ML) have been integrated into clinical practice for analyzing datasets to identify new risk factors, build predictive models, and develop personalized interventions, thereby providing useful information to healthcare professionals. This systematic review aims to evaluate the potential of AI and ML techniques in addressing the relationship between obesity, its associated health consequences, and cognitive decline. Methods: Systematic searches were performed in PubMed, Cochrane, Web of Science, Scopus, Embase, and PsycInfo databases, which yielded eight studies. After reading the full text of the selected studies and applying predefined inclusion criteria, eight studies were included based on pertinence and relevance to the topic. Results: The findings underscore the utility of AI and ML in assessing risk and predicting cognitive decline in obese patients. Furthermore, these new technology models identified key risk factors and predictive biomarkers, paving the way for tailored prevention strategies and treatment plans. Conclusions: The early detection, prevention, and personalized interventions facilitated by these technologies can significantly reduce costs and time. Future research should assess ethical considerations, data privacy, and equitable access for all.
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Affiliation(s)
- Isabella Veneziani
- Department of Nervous System and Behavioural Sciences, Psychology Section, University of Pavia, Piazza Botta, 11, 27100 Pavia, Italy (A.G.)
| | - Alessandro Grimaldi
- Department of Nervous System and Behavioural Sciences, Psychology Section, University of Pavia, Piazza Botta, 11, 27100 Pavia, Italy (A.G.)
| | - Angela Marra
- IRCCS Centro Neurolesi “Bonino-Pulejo”, S.S. 113 Via Palermo C. da Casazza, 98124 Messina, Italy; (A.M.); (E.M.); (S.M.); (A.Q.); (G.M.)
| | - Elisabetta Morini
- IRCCS Centro Neurolesi “Bonino-Pulejo”, S.S. 113 Via Palermo C. da Casazza, 98124 Messina, Italy; (A.M.); (E.M.); (S.M.); (A.Q.); (G.M.)
| | - Laura Culicetto
- IRCCS Centro Neurolesi “Bonino-Pulejo”, S.S. 113 Via Palermo C. da Casazza, 98124 Messina, Italy; (A.M.); (E.M.); (S.M.); (A.Q.); (G.M.)
| | - Silvia Marino
- IRCCS Centro Neurolesi “Bonino-Pulejo”, S.S. 113 Via Palermo C. da Casazza, 98124 Messina, Italy; (A.M.); (E.M.); (S.M.); (A.Q.); (G.M.)
| | - Angelo Quartarone
- IRCCS Centro Neurolesi “Bonino-Pulejo”, S.S. 113 Via Palermo C. da Casazza, 98124 Messina, Italy; (A.M.); (E.M.); (S.M.); (A.Q.); (G.M.)
| | - Giuseppa Maresca
- IRCCS Centro Neurolesi “Bonino-Pulejo”, S.S. 113 Via Palermo C. da Casazza, 98124 Messina, Italy; (A.M.); (E.M.); (S.M.); (A.Q.); (G.M.)
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Veneziani I, Marra A, Formica C, Grimaldi A, Marino S, Quartarone A, Maresca G. Applications of Artificial Intelligence in the Neuropsychological Assessment of Dementia: A Systematic Review. J Pers Med 2024; 14:113. [PMID: 38276235 PMCID: PMC10820741 DOI: 10.3390/jpm14010113] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2023] [Revised: 01/09/2024] [Accepted: 01/16/2024] [Indexed: 01/27/2024] Open
Abstract
In the context of advancing healthcare, the diagnosis and treatment of cognitive disorders, particularly Mild Cognitive Impairment (MCI) and Alzheimer's Disease (AD), pose significant challenges. This review explores Artificial Intelligence (AI) and Machine Learning (ML) in neuropsychological assessment for the early detection and personalized treatment of MCI and AD. The review includes 37 articles that demonstrate that AI could be an useful instrument for optimizing diagnostic procedures, predicting cognitive decline, and outperforming traditional tests. Three main categories of applications are identified: (1) combining neuropsychological assessment with clinical data, (2) optimizing existing test batteries using ML techniques, and (3) employing virtual reality and games to overcome the limitations of traditional tests. Despite advancements, the review highlights a gap in developing tools that simplify the clinician's workflow and underscores the need for explainable AI in healthcare decision making. Future studies should bridge the gap between technical performance measures and practical clinical utility to yield accurate results and facilitate clinicians' roles. The successful integration of AI/ML in predicting dementia onset could reduce global healthcare costs and benefit aging societies.
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Affiliation(s)
- Isabella Veneziani
- Department of Nervous System and Behavioural Sciences, Psychology Section, University of Pavia, Piazza Botta, 11, 27100 Pavia, Italy;
| | - Angela Marra
- IRCCS Centro Neurolesi “Bonino-Pulejo”, S.S. 113 Via Palermo C. da Casazza, 98124 Messina, Italy; (A.M.); (A.G.); (S.M.); (A.Q.); (G.M.)
| | - Caterina Formica
- IRCCS Centro Neurolesi “Bonino-Pulejo”, S.S. 113 Via Palermo C. da Casazza, 98124 Messina, Italy; (A.M.); (A.G.); (S.M.); (A.Q.); (G.M.)
| | - Alessandro Grimaldi
- IRCCS Centro Neurolesi “Bonino-Pulejo”, S.S. 113 Via Palermo C. da Casazza, 98124 Messina, Italy; (A.M.); (A.G.); (S.M.); (A.Q.); (G.M.)
| | - Silvia Marino
- IRCCS Centro Neurolesi “Bonino-Pulejo”, S.S. 113 Via Palermo C. da Casazza, 98124 Messina, Italy; (A.M.); (A.G.); (S.M.); (A.Q.); (G.M.)
| | - Angelo Quartarone
- IRCCS Centro Neurolesi “Bonino-Pulejo”, S.S. 113 Via Palermo C. da Casazza, 98124 Messina, Italy; (A.M.); (A.G.); (S.M.); (A.Q.); (G.M.)
| | - Giuseppa Maresca
- IRCCS Centro Neurolesi “Bonino-Pulejo”, S.S. 113 Via Palermo C. da Casazza, 98124 Messina, Italy; (A.M.); (A.G.); (S.M.); (A.Q.); (G.M.)
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Fiorente N, Mojdehdehbaher S, Calabrò RS. Artificial Intelligence and Neurorehabilitation: Fact vs. Fiction. INNOVATIONS IN CLINICAL NEUROSCIENCE 2024; 21:10-12. [PMID: 38495602 PMCID: PMC10941864] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 03/19/2024]
Abstract
Artificial intelligence (AI) encompasses computer systems that mimic human cognitive functions, performing tasks such as learning, reasoning, problem solving, and decision-making. Neurorehabilitation is a specialized healthcare field aiding individuals with neurological injuries, employing various therapies to restore motor skills and cognitive function, enhancing their quality of life. The integration of AI in neurorehabilitation holds great promise, but it is crucial to approach this technology with a clear understanding of its capabilities and limitations. AI can enhance assessment, diagnosis, and personalized treatment plans, but it should complement, rather than replace, human healthcare providers. Additionally, ethical considerations must be at the forefront of AI implementation in the field of neurorehabilitation to ensure that patient wellbeing is prioritized.
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Affiliation(s)
- Nicola Fiorente
- Dr. Fiorente is with Polimedica Fisio and Sport in Cittadella (PD), Italy
| | - Sepehr Mojdehdehbaher
- Dr. Mojdehdehbaher is with Engineering Department, University of Messina in Messina, Italy
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