1
|
Carrarini C, Nardulli C, Titti L, Iodice F, Miraglia F, Vecchio F, Rossini PM. Neuropsychological and electrophysiological measurements for diagnosis and prediction of dementia: a review on Machine Learning approach. Ageing Res Rev 2024; 100:102417. [PMID: 39002643 DOI: 10.1016/j.arr.2024.102417] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2023] [Revised: 04/29/2024] [Accepted: 07/07/2024] [Indexed: 07/15/2024]
Abstract
INTRODUCTION Emerging and advanced technologies in the field of Artificial Intelligence (AI) represent promising methods to predict and diagnose neurodegenerative diseases, such as dementia. By using multimodal approaches, Machine Learning (ML) seems to provide a better understanding of the pathological mechanisms underlying the onset of dementia. The purpose of this review was to discuss the current ML application in the field of neuropsychology and electrophysiology, exploring its results in both prediction and diagnosis for different forms of dementia, such as Alzheimer's disease (AD), Vascular Dementia (VaD), Dementia with Lewy bodies (DLB), and Frontotemporal Dementia (FTD). METHODS Main ML-based papers focusing on neuropsychological assessments and electroencephalogram (EEG) studies were analyzed for each type of dementia. RESULTS An accuracy ranging between 70 % and 90 % or even more was observed in all neurophysiological and electrophysiological results trained by ML. Among all forms of dementia, the most significant findings were observed for AD. Relevant results were mostly related to diagnosis rather than prediction, because of the lack of longitudinal studies with appropriate follow-up duration. However, it remains unclear which ML algorithm performs better in diagnosing or predicting dementia. CONCLUSIONS Neuropsychological and electrophysiological measurements, together with ML analysis, may be considered as reliable instruments for early detection of dementia.
Collapse
Affiliation(s)
- Claudia Carrarini
- Department of Neuroscience & Neurorehabilitation, IRCCS San Raffaele, via della Pisana 235, Rome 00163, Italy; Department of Neuroscience, Catholic University of Sacred Heart, Largo Agostino Gemelli 8, Rome 00168, Italy
| | - Cristina Nardulli
- Department of Neuroscience & Neurorehabilitation, IRCCS San Raffaele, via della Pisana 235, Rome 00163, Italy
| | - Laura Titti
- Department of Neuroscience & Neurorehabilitation, IRCCS San Raffaele, via della Pisana 235, Rome 00163, Italy
| | - Francesco Iodice
- Department of Neuroscience & Neurorehabilitation, IRCCS San Raffaele, via della Pisana 235, Rome 00163, Italy
| | - Francesca Miraglia
- Department of Neuroscience & Neurorehabilitation, IRCCS San Raffaele, via della Pisana 235, Rome 00163, Italy; Department of Theoretical and Applied Sciences, eCampus University, via Isimbardi 10, Novedrate 22060, Italy
| | - Fabrizio Vecchio
- Department of Neuroscience & Neurorehabilitation, IRCCS San Raffaele, via della Pisana 235, Rome 00163, Italy; Department of Theoretical and Applied Sciences, eCampus University, via Isimbardi 10, Novedrate 22060, Italy
| | - Paolo Maria Rossini
- Department of Neuroscience & Neurorehabilitation, IRCCS San Raffaele, via della Pisana 235, Rome 00163, Italy.
| |
Collapse
|
2
|
Rasheed Z, Ma YK, Ullah I, Al-Khasawneh M, Almutairi SS, Abohashrh M. Integrating Convolutional Neural Networks with Attention Mechanisms for Magnetic Resonance Imaging-Based Classification of Brain Tumors. Bioengineering (Basel) 2024; 11:701. [PMID: 39061782 PMCID: PMC11273980 DOI: 10.3390/bioengineering11070701] [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: 06/17/2024] [Revised: 07/05/2024] [Accepted: 07/07/2024] [Indexed: 07/28/2024] Open
Abstract
The application of magnetic resonance imaging (MRI) in the classification of brain tumors is constrained by the complex and time-consuming characteristics of traditional diagnostics procedures, mainly because of the need for a thorough assessment across several regions. Nevertheless, advancements in deep learning (DL) have facilitated the development of an automated system that improves the identification and assessment of medical images, effectively addressing these difficulties. Convolutional neural networks (CNNs) have emerged as steadfast tools for image classification and visual perception. This study introduces an innovative approach that combines CNNs with a hybrid attention mechanism to classify primary brain tumors, including glioma, meningioma, pituitary, and no-tumor cases. The proposed algorithm was rigorously tested with benchmark data from well-documented sources in the literature. It was evaluated alongside established pre-trained models such as Xception, ResNet50V2, Densenet201, ResNet101V2, and DenseNet169. The performance metrics of the proposed method were remarkable, demonstrating classification accuracy of 98.33%, precision and recall of 98.30%, and F1-score of 98.20%. The experimental finding highlights the superior performance of the new approach in identifying the most frequent types of brain tumors. Furthermore, the method shows excellent generalization capabilities, making it an invaluable tool for healthcare in diagnosing brain conditions accurately and efficiently.
Collapse
Affiliation(s)
- Zahid Rasheed
- School of Electronics and Information Engineering, Harbin Institute of Technology, Harbin 150001, China; (Z.R.); (Y.-K.M.)
| | - Yong-Kui Ma
- School of Electronics and Information Engineering, Harbin Institute of Technology, Harbin 150001, China; (Z.R.); (Y.-K.M.)
| | - Inam Ullah
- Department of Computer Engineering, Gachon University, Sujeong-gu, Seongman 13120, Republic of Korea
| | - Mahmoud Al-Khasawneh
- School of Computing, Skyline University College, University City Sharjah, Sharjah 1797, United Arab Emirates;
- Applied Science Research Center, Applied Science Private University, Amman 11931, Jordan
- Jadara University Research Center, Jadara University, Irbid 21110, Jordan
| | - Sulaiman Sulmi Almutairi
- Department of Health Informatics, College of Public Health and Health Informatics, Qassim University, Qassim 51452, Saudi Arabia;
| | - Mohammed Abohashrh
- Department of Basic Medical Sciences, College of Applied Medical Sciences, King Khalid University, Abha 61421, Saudi Arabia;
| |
Collapse
|
3
|
AlMansoori ME, Jemimah S, Abuhantash F, AlShehhi A. Predicting early Alzheimer's with blood biomarkers and clinical features. Sci Rep 2024; 14:6039. [PMID: 38472245 PMCID: PMC10933308 DOI: 10.1038/s41598-024-56489-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2023] [Accepted: 03/07/2024] [Indexed: 03/14/2024] Open
Abstract
Alzheimer's disease (AD) is an incurable neurodegenerative disorder that leads to dementia. This study employs explainable machine learning models to detect dementia cases using blood gene expression, single nucleotide polymorphisms (SNPs), and clinical data from Alzheimer's Disease Neuroimaging Initiative (ADNI). Analyzing 623 ADNI participants, we found that the Support Vector Machine classifier with Mutual Information (MI) feature selection, trained on all three data modalities, achieved exceptional performance (accuracy = 0.95, AUC = 0.94). When using gene expression and SNP data separately, we achieved very good performance (AUC = 0.65, AUC = 0.63, respectively). Using SHapley Additive exPlanations (SHAP), we identified significant features, potentially serving as AD biomarkers. Notably, genetic-based biomarkers linked to axon myelination and synaptic vesicle membrane formation could aid early AD detection. In summary, this genetic-based biomarker approach, integrating machine learning and SHAP, shows promise for precise AD diagnosis, biomarker discovery, and offers novel insights for understanding and treating the disease. This approach addresses the challenges of accurate AD diagnosis, which is crucial given the complexities associated with the disease and the need for non-invasive diagnostic methods.
Collapse
Affiliation(s)
- Muaath Ebrahim AlMansoori
- Department of Biomedical Engineering, Khalifa University, P.O. Box: 127788, Abu Dhabi, United Arab Emirates
| | - Sherlyn Jemimah
- Department of Biomedical Engineering, Khalifa University, P.O. Box: 127788, Abu Dhabi, United Arab Emirates
| | - Ferial Abuhantash
- Department of Biomedical Engineering, Khalifa University, P.O. Box: 127788, Abu Dhabi, United Arab Emirates
| | - Aamna AlShehhi
- Department of Biomedical Engineering, Khalifa University, P.O. Box: 127788, Abu Dhabi, United Arab Emirates.
- Healthcare Engineering Innovation Center (HEIC), Khalifa University, P.O. Box: 127788, Abu Dhabi, United Arab Emirates.
| |
Collapse
|
4
|
Ávila-Jiménez JL, Cantón-Habas V, Carrera-González MDP, Rich-Ruiz M, Ventura S. A deep learning model for Alzheimer's disease diagnosis based on patient clinical records. Comput Biol Med 2024; 169:107814. [PMID: 38113682 DOI: 10.1016/j.compbiomed.2023.107814] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2023] [Revised: 11/19/2023] [Accepted: 12/03/2023] [Indexed: 12/21/2023]
Abstract
BACKGROUND Dementia, with Alzheimer's disease (AD) being the most common type of this neurodegenerative disease, is an under-diagnosed health problem in older people. The creation of classification models based on AD risk factors using Deep Learning is a promising tool to minimize the impact of under-diagnosis. OBJECTIVE To develop a Deep Learning model that uses clinical data from patients with dementia to classify whether they have AD. METHODS A Deep Learning model to identify AD in clinical records is proposed. In addition, several rebalancing methods have been used to preprocess the dataset and several studies have been carried out to tune up the model. RESULTS Model has been tested against other well-established machine learning techniques, having better results than these in terms of AUC with alpha less than 0.05. CONCLUSIONS The developed Neural Network Model has a good performance and can be an accurate assisting tool for AD diagnosis.
Collapse
Affiliation(s)
- J L Ávila-Jiménez
- Departament of Electronic and Computer Engineering. Andalusian Research Institute in Data Science and Computational Intelligence (DaSCI), University of Cordoba, Córdoba, Spain
| | - Vanesa Cantón-Habas
- Maimonides Institute for Biomedical Research, Reina Sofia University Hospital, University of Córdoba, Spain.
| | - María Del Pilar Carrera-González
- Maimonides Institute for Biomedical Research, Reina Sofia University Hospital, University of Córdoba, Spain; Experimental and Clinical Physiopathology Research Group CTS-1039; Department of Health Sciences, Faculty of Health Sciences; University of Jaén, Campus Universitario Las Lagunillas, Jaén, Spain
| | - Manuel Rich-Ruiz
- Maimonides Institute for Biomedical Research, Reina Sofia University Hospital, University of Córdoba, Spain; CIBER on Fragility and Healthy Aging (CIBERFES), Madrid, Spain; Instituto de Salud Carlos III, Nursing and Healthcare Research Unit (Investén-isciii), Madrid, Spain
| | - Sebastián Ventura
- Department of Computer Science and Numerical Analysis, Andalusian Research Institute in Data Science and Computational Intelligence (DaSCI), University of Cordoba, Córdoba, Spain
| |
Collapse
|
5
|
Cabrera-León Y, Báez PG, Fernández-López P, Suárez-Araujo CP. Neural Computation-Based Methods for the Early Diagnosis and Prognosis of Alzheimer's Disease Not Using Neuroimaging Biomarkers: A Systematic Review. J Alzheimers Dis 2024; 98:793-823. [PMID: 38489188 PMCID: PMC11091566 DOI: 10.3233/jad-231271] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 02/03/2024] [Indexed: 03/17/2024]
Abstract
Background The growing number of older adults in recent decades has led to more prevalent geriatric diseases, such as strokes and dementia. Therefore, Alzheimer's disease (AD), as the most common type of dementia, has become more frequent too. Background Objective: The goals of this work are to present state-of-the-art studies focused on the automatic diagnosis and prognosis of AD and its early stages, mainly mild cognitive impairment, and predicting how the research on this topic may change in the future. Methods Articles found in the existing literature needed to fulfill several selection criteria. Among others, their classification methods were based on artificial neural networks (ANNs), including deep learning, and data not from brain signals or neuroimaging techniques were used. Considering our selection criteria, 42 articles published in the last decade were finally selected. Results The most medically significant results are shown. Similar quantities of articles based on shallow and deep ANNs were found. Recurrent neural networks and transformers were common with speech or in longitudinal studies. Convolutional neural networks (CNNs) were popular with gait or combined with others in modular approaches. Above one third of the cross-sectional studies utilized multimodal data. Non-public datasets were frequently used in cross-sectional studies, whereas the opposite in longitudinal ones. The most popular databases were indicated, which will be helpful for future researchers in this field. Conclusions The introduction of CNNs in the last decade and their superb results with neuroimaging data did not negatively affect the usage of other modalities. In fact, new ones emerged.
Collapse
Affiliation(s)
- Ylermi Cabrera-León
- Instituto Universitario de Cibernética, Empresa y Sociedad, Universidad de Las Palmas de Gran Canaria, Las Palmas de Gran Canaria, Canary Islands, Spain
| | - Patricio García Báez
- Departamento de Ingeniería Informática y de Sistemas, Escuela Superior de Ingeniería y Tecnología, Universidad de La Laguna, San Cristóbal de La Laguna, Canary Islands, Spain
| | - Pablo Fernández-López
- Instituto Universitario de Cibernética, Empresa y Sociedad, Universidad de Las Palmas de Gran Canaria, Las Palmas de Gran Canaria, Canary Islands, Spain
| | - Carmen Paz Suárez-Araujo
- Instituto Universitario de Cibernética, Empresa y Sociedad, Universidad de Las Palmas de Gran Canaria, Las Palmas de Gran Canaria, Canary Islands, Spain
| |
Collapse
|
6
|
Cunha ILDOM. [Assistive technologies for elderly patients with dementia: perspectives from the bioethics of health care.]. Salud Colect 2023; 19:e4488. [PMID: 37992283 DOI: 10.18294/sc.2023.4488] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2023] [Accepted: 09/01/2023] [Indexed: 11/24/2023] Open
Abstract
Dementia is currently one of the most common diseases affecting elderly people, ranking seventh among leading causes of death. As it causes memory loss, difficulties in reasoning, and resulting difficulties in making and executing decisions, assistive technologies and cognitive stimulation are valuable resources in the care process. Based on a theoretical inquiry into the bioethics of health care, and drawing on the work of Aline Albuquerque and Victor Montori, this article focuses on three main issues: first, the concept of bioethics in health care, patient-centered care, and the idea of clinical empathy. Second, it looks at the use of assistive technologies in caring for elderly patients with dementia. Lastly, we offer a discussion of whether the care process could be considered a health technology.
Collapse
|
7
|
Mohamed AA, Marques O. Diagnostic Efficacy and Clinical Relevance of Artificial Intelligence in Detecting Cognitive Decline. Cureus 2023; 15:e47004. [PMID: 37965412 PMCID: PMC10641267 DOI: 10.7759/cureus.47004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/13/2023] [Indexed: 11/16/2023] Open
Abstract
Cognitive impairment is an age-associated disorder of increasing prevalence as the aging population continues to grow. Classified based on the level of cognitive decline, memory, function, and capacity to conduct activities of daily living, cognitive impairment ranges from mild cognitive impairment to dementia. When considering the insidious nature of the etiologies responsible for varying degrees of cognitive impairment, early diagnosis may provide a clinical benefit through the facilitation of early treatment. Typical diagnosis relies heavily on evaluation in a primary care setting. However, there is evidence that other diagnostic tools may aid in an earlier diagnosis of the different underlying pathologies responsible for cognitive impairment. Artificial intelligence represents a new intersecting field with healthcare that may aid in the early detection of neurodegenerative disorders. When assessing the role of AI in detecting cognitive decline, it is important to consider both the diagnostic efficacy of AI algorithms and the clinical relevance and impact of early interventions as a result of early detection. Thus, this review highlights promising investigations and developments in the space of artificial intelligence and healthcare and their potential to impact patient outcomes.
Collapse
Affiliation(s)
- Ali A Mohamed
- Neurological Surgery, Charles E. Schmidt College of Medicine, Florida Atlantic University, Boca Raton, USA
- Electrical Engineering and Computer Science, Florida Atlantic University, Boca Raton, USA
| | - Oge Marques
- Biomedical Sciences, Charles E. Schmidt College of Medicine, Florida Atlantic University, Boca Raton, USA
- Electrical Engineering and Computer Science, Florida Atlantic University, Boca Raton, USA
| |
Collapse
|
8
|
Kwak MG, Su Y, Chen K, Weidman D, Wu T, Lure F, Li J. Self-Supervised Contrastive Learning to Predict the Progression of Alzheimer's Disease with 3D Amyloid-PET. Bioengineering (Basel) 2023; 10:1141. [PMID: 37892871 PMCID: PMC10604381 DOI: 10.3390/bioengineering10101141] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2023] [Revised: 09/25/2023] [Accepted: 09/27/2023] [Indexed: 10/29/2023] Open
Abstract
Early diagnosis of Alzheimer's disease (AD) is an important task that facilitates the development of treatment and prevention strategies, and may potentially improve patient outcomes. Neuroimaging has shown great promise, including the amyloid-PET, which measures the accumulation of amyloid plaques in the brain-a hallmark of AD. It is desirable to train end-to-end deep learning models to predict the progression of AD for individuals at early stages based on 3D amyloid-PET. However, commonly used models are trained in a fully supervised learning manner, and they are inevitably biased toward the given label information. To this end, we propose a selfsupervised contrastive learning method to accurately predict the conversion to AD for individuals with mild cognitive impairment (MCI) with 3D amyloid-PET. The proposed method, SMoCo, uses both labeled and unlabeled data to capture general semantic representations underlying the images. As the downstream task is given as classification of converters vs. non-converters, unlike the general self-supervised learning problem that aims to generate task-agnostic representations, SMoCo additionally utilizes the label information in the pre-training. To demonstrate the performance of our method, we conducted experiments on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset. The results confirmed that the proposed method is capable of providing appropriate data representations, resulting in accurate classification. SMoCo showed the best classification performance over the existing methods, with AUROC = 85.17%, accuracy = 81.09%, sensitivity = 77.39%, and specificity = 82.17%. While SSL has demonstrated great success in other application domains of computer vision, this study provided the initial investigation of using a proposed self-supervised contrastive learning model, SMoCo, to effectively predict MCI conversion to AD based on 3D amyloid-PET.
Collapse
Affiliation(s)
- Min Gu Kwak
- H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA;
| | - Yi Su
- Banner Alzheimer’s Institute, Phoenix, AZ 85006, USA; (Y.S.); (K.C.); (D.W.)
| | - Kewei Chen
- Banner Alzheimer’s Institute, Phoenix, AZ 85006, USA; (Y.S.); (K.C.); (D.W.)
| | - David Weidman
- Banner Alzheimer’s Institute, Phoenix, AZ 85006, USA; (Y.S.); (K.C.); (D.W.)
| | - Teresa Wu
- School of Computing and Augmented Intelligence, Arizona State University, Tempe, AZ 85281, USA;
| | - Fleming Lure
- MS Technologies Corporation, Rockville, MD 20850, USA;
| | - Jing Li
- H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA;
| | | |
Collapse
|
9
|
Rodrigues PM, Madeiro JP, Marques JAL. Enhancing Health and Public Health through Machine Learning: Decision Support for Smarter Choices. Bioengineering (Basel) 2023; 10:792. [PMID: 37508819 PMCID: PMC10376309 DOI: 10.3390/bioengineering10070792] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2023] [Accepted: 06/29/2023] [Indexed: 07/30/2023] Open
Abstract
In recent years, the integration of Machine Learning (ML) techniques in the field of healthcare and public health has emerged as a powerful tool for improving decision-making processes [...].
Collapse
Affiliation(s)
- Pedro Miguel Rodrigues
- CBQF-Centro de Biotecnologia e Química Fina-Laboratório Associado, Escola Superior de Biotecnologia, Universidade Católica Portuguesa, Rua de Diogo Botelho 1327, 4169-005 Porto, Portugal
| | - João Paulo Madeiro
- Department of Computing, Federal University of Ceará, Fortaleza 60440-900, Ceará, Brazil
| | | |
Collapse
|
10
|
Wu CC, Su CH, Islam MM, Liao MH. Artificial Intelligence in Dementia: A Bibliometric Study. Diagnostics (Basel) 2023; 13:2109. [PMID: 37371004 PMCID: PMC10297057 DOI: 10.3390/diagnostics13122109] [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: 05/24/2023] [Revised: 06/10/2023] [Accepted: 06/16/2023] [Indexed: 06/29/2023] Open
Abstract
The applications of artificial intelligence (AI) in dementia research have garnered significant attention, prompting the planning of various research endeavors in current and future studies. The objective of this study is to provide a comprehensive overview of the research landscape regarding AI and dementia within scholarly publications and to suggest further studies for this emerging research field. A search was conducted in the Web of Science database to collect all relevant and highly cited articles on AI-related dementia research published in English until 16 May 2023. Utilizing bibliometric indicators, a search strategy was developed to assess the eligibility of titles, utilizing abstracts and full texts as necessary. The Bibliometrix tool, a statistical package in R, was used to produce and visualize networks depicting the co-occurrence of authors, research institutions, countries, citations, and keywords. We obtained a total of 1094 relevant articles published between 1997 and 2023. The number of annual publications demonstrated an increasing trend over the past 27 years. Journal of Alzheimer's Disease (39/1094, 3.56%), Frontiers in Aging Neuroscience (38/1094, 3.47%), and Scientific Reports (26/1094, 2.37%) were the most common journals for this domain. The United States (283/1094, 25.86%), China (222/1094, 20.29%), India (150/1094, 13.71%), and England (96/1094, 8.77%) were the most productive countries of origin. In terms of institutions, Boston University, Columbia University, and the University of Granada demonstrated the highest productivity. As for author contributions, Gorriz JM, Ramirez J, and Salas-Gonzalez D were the most active researchers. While the initial period saw a relatively low number of articles focusing on AI applications for dementia, there has been a noticeable upsurge in research within this domain in recent years (2018-2023). The present analysis sheds light on the key contributors in terms of researchers, institutions, countries, and trending topics that have propelled the advancement of AI in dementia research. These findings collectively underscore that the integration of AI with conventional treatment approaches enhances the effectiveness of dementia diagnosis, prediction, classification, and monitoring of treatment progress.
Collapse
Affiliation(s)
- Chieh-Chen Wu
- Department of Healthcare Information and Management, School of Health Technology, Ming Chuan University, Taipei 333, Taiwan;
- Department of Exercise and Health Promotion, College of Kinesiology and Health, Chinese Culture University, Taipei 111369, Taiwan;
| | - Chun-Hsien Su
- Department of Exercise and Health Promotion, College of Kinesiology and Health, Chinese Culture University, Taipei 111369, Taiwan;
- Graduate Institute of Sports Coaching Science, College of Kinesiology and Health, Chinese Culture University, Taipei 11114, Taiwan
| | | | - Mao-Hung Liao
- Superintendent Office, Yonghe Cardinal Tien Hospital, New Taipei City 23148, Taiwan
- Department of Healthcare Administration, Asia Eastern University of Science and Technology, Banciao District, New Taipei City 220303, Taiwan
| |
Collapse
|
11
|
Gallo C. Artificial Intelligence for Personalized Genetics and New Drug Development: Benefits and Cautions. Bioengineering (Basel) 2023; 10:bioengineering10050613. [PMID: 37237683 DOI: 10.3390/bioengineering10050613] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2023] [Accepted: 05/17/2023] [Indexed: 05/28/2023] Open
Abstract
As the global health care system grapples with steadily rising costs, increasing numbers of admissions, and the chronic defection of doctors and nurses from the profession, appropriate measures need to be put in place to reverse this course before it is too late [...].
Collapse
Affiliation(s)
- Crescenzio Gallo
- Department of Clinical and Experimental Medicine, University of Foggia, 71121 Foggia, Italy
| |
Collapse
|
12
|
Song S, Asken B, Armstrong MJ, Yang Y, Li Z. Predicting Progression to Clinical Alzheimer's Disease Dementia Using the Random Survival Forest. J Alzheimers Dis 2023; 95:535-548. [PMID: 37545237 PMCID: PMC10529100 DOI: 10.3233/jad-230208] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/08/2023]
Abstract
BACKGROUND Assessing the risk of developing clinical Alzheimer's disease (AD) dementia, by machine learning survival analysis approaches, among participants registered in Alzheimer's Disease Centers is important for AD dementia management. OBJECTIVE To construct a prediction model for the onset time of clinical AD dementia using the National Alzheimer Coordinating Center (NACC) and the Alzheimer's Disease Neuroimaging Initiative (ADNI) registered cohorts. METHODS A model was constructed using the Random Survival Forest (RSF) approach and internally and externally validated on the NACC cohort and the ADNI cohort. An R package and a Shiny app were provided for accessing the model. RESULTS We built a predictive model having the six predictors: delayed logical memory score (story recall), CDR® Dementia Staging Instrument - Sum of Boxes, general orientation in CDR®, ability to remember dates and ability to pay bills in the Functional Activities Questionnaire, and patient age. The C indices of the model were 90.82% (SE = 0.71%) and 86.51% (SE = 0.75%) in NACC and ADNI respectively. The time-dependent AUC and accuracy at 48 months were 92.48% (SE = 1.12%) and 88.66% (SE = 1.00%) respectively in NACC, and 90.16% (SE = 1.12%) and 85.00% (SE = 1.14%) respectively in ADNI. CONCLUSION The model showed good prediction performance and the six predictors were easy to obtain, cost-effective, and non-invasive. The model could be used to inform clinicians and patients on the probability of developing clinical AD dementia in 4 years with high accuracy.
Collapse
Affiliation(s)
- Shangchen Song
- Department of Biostatistics, University of Florida College of Public Health & Health Professions and College of Medicine, Gainesville, Florida, 32611, USA
| | - Breton Asken
- Department of Clinical and Health Psychology, University of Florida College of Public Health & Health Professions, Gainesville, FL, 32611, USA
- Norman Fixel Institute for Neurological Diseases, University of Florida, Gainesville, FL, 32608, USA
- University of Florida Center for Cognitive Aging and Memory, McKnight Brain Institute, Gainesville, FL, 32610, USA
| | - Melissa J. Armstrong
- Departments of Neurology and Health Outcomes & Biomedical Informatics, University of Florida College of Medicine, Gainesville, FL, 32611, USA
- Norman Fixel Institute for Neurological Diseases, University of Florida, Gainesville, FL, 32608, USA
| | - Yang Yang
- Department of Statistics, University of Georgia Franklin College of Arts and Sciences, Athens, GA, 30602, USA
| | - Zhigang Li
- Department of Biostatistics, University of Florida College of Public Health & Health Professions and College of Medicine, Gainesville, Florida, 32611, USA
| | | |
Collapse
|
13
|
Stecker M. A Perspective: Challenges in Dementia Research. MEDICINA (KAUNAS, LITHUANIA) 2022; 58:1368. [PMID: 36295529 PMCID: PMC9609997 DOI: 10.3390/medicina58101368] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 08/31/2022] [Revised: 09/21/2022] [Accepted: 09/27/2022] [Indexed: 11/17/2022]
Abstract
Although dementia is a common and devastating disease that has been studied intensely for more than 100 years, no effective disease modifying treatment has been found. At this impasse, new approaches are important. The purpose of this paper is to provide, in the context of current research, one clinician's perspective regarding important challenges in the field in the form of specific challenges. These challenges not only illustrate the scope of the problems inherent in finding treatments for dementia, but can also be specific targets to foster discussion, criticism and new research. One common theme is the need to transform research activities from small projects in individual laboratories/clinics to larger multinational projects, in which each clinician and researcher works as an integral part. This transformation will require collaboration between researchers, large corporations, regulatory/governmental authorities and the general population, as well as significant financial investments. However, the costs of transforming the approach are small in comparison with the cost of dementia.
Collapse
Affiliation(s)
- Mark Stecker
- Fresno Institute of Neuroscience, Fresno, CA 93720, USA
| |
Collapse
|