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Yang B, Earnest T, Kumar S, Kothapalli D, Benzinger T, Gordon B, Sotiras A. Evaluation of ComBat harmonization for reducing across-tracer biases in regional amyloid PET analyses. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.06.14.24308952. [PMID: 38947044 PMCID: PMC11213066 DOI: 10.1101/2024.06.14.24308952] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/02/2024]
Abstract
Background Differences in amyloid positron emission tomography (PET) radiotracer pharmacokinetics and binding properties lead to discrepancies in amyloid-β uptake estimates. Harmonization of tracer-specific biases is crucial for optimal performance of downstream tasks. Here, we investigated the efficacy of ComBat, a data-driven harmonization model, for reducing tracer-specific biases in regional amyloid PET measurements from [18F]-florbetapir (FBP) and [11C]-Pittsburgh Compound-B (PiB). Methods One-hundred-thirteen head-to-head FBP-PiB scan pairs, scanned from the same subject within ninety days, were selected from the Open Access Series of Imaging Studies 3 (OASIS-3) dataset. The Centiloid scale, ComBat with no covariates, ComBat with biological covariates, and GAM-ComBat with biological covariates were used to harmonize both global and regional amyloid standardized uptake value ratios (SUVR). Intraclass correlation coefficient (ICC) and mean standardized absolute error (MsAE) were computed to measure the absolute agreement between tracers. Additionally, longitudinal amyloid SUVRs from an anti-amyloid drug trial were simulated using linear mixed effects modeling. Differences in rates-of-change between simulated treatment and placebo groups were tested, and change in statistical power/Type-I error after harmonization was quantified. Results In the head-to-head tracer comparison, the best ICC and MsAE were achieved after harmonizing with ComBat with no covariates for the global summary SUVR. ComBat with no covariates also performed the best in harmonizing regional SUVRs. In the clinical trial simulation, harmonization with both Centiloid and ComBat increased statistical power of detecting true rate-of-change differences between groups and decreased false discovery rate in the absence of a treatment effect. The greatest benefit of harmonization was observed when groups exhibited differing FPB-to-PiB proportions. Conclusions ComBat outperformed the Centiloid scale in harmonizing both global and regional amyloid estimates. Additionally, ComBat improved the detection of rate-of-change differences between clinical trial groups. Our findings suggest that ComBat is a viable alternative to Centiloid for harmonizing regional amyloid PET analyses.
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Affiliation(s)
- Braden Yang
- Mallinckrodt Institute of Radiology, Washington University School of Medicine in St. Louis, St. Louis, MO, USA 63110
| | - Tom Earnest
- Mallinckrodt Institute of Radiology, Washington University School of Medicine in St. Louis, St. Louis, MO, USA 63110
| | - Sayantan Kumar
- Mallinckrodt Institute of Radiology, Washington University School of Medicine in St. Louis, St. Louis, MO, USA 63110
| | - Deydeep Kothapalli
- Mallinckrodt Institute of Radiology, Washington University School of Medicine in St. Louis, St. Louis, MO, USA 63110
| | - Tammie Benzinger
- Mallinckrodt Institute of Radiology, Washington University School of Medicine in St. Louis, St. Louis, MO, USA 63110
| | - Brian Gordon
- Mallinckrodt Institute of Radiology, Washington University School of Medicine in St. Louis, St. Louis, MO, USA 63110
| | - Aristeidis Sotiras
- Mallinckrodt Institute of Radiology, Washington University School of Medicine in St. Louis, St. Louis, MO, USA 63110
- Institute for Informatics, Data Science and Biostatistics, Washington University School of Medicine in St. Louis, St. Louis, MO, USA 63110
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Odusami M, Maskeliūnas R, Damaševičius R, Misra S. Machine learning with multimodal neuroimaging data to classify stages of Alzheimer's disease: a systematic review and meta-analysis. Cogn Neurodyn 2024; 18:775-794. [PMID: 38826669 PMCID: PMC11143094 DOI: 10.1007/s11571-023-09993-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2023] [Revised: 06/23/2023] [Accepted: 07/17/2023] [Indexed: 06/04/2024] Open
Abstract
In recent years, Alzheimer's disease (AD) has been a serious threat to human health. Researchers and clinicians alike encounter a significant obstacle when trying to accurately identify and classify AD stages. Several studies have shown that multimodal neuroimaging input can assist in providing valuable insights into the structural and functional changes in the brain related to AD. Machine learning (ML) algorithms can accurately categorize AD phases by identifying patterns and linkages in multimodal neuroimaging data using powerful computational methods. This study aims to assess the contribution of ML methods to the accurate classification of the stages of AD using multimodal neuroimaging data. A systematic search is carried out in IEEE Xplore, Science Direct/Elsevier, ACM DigitalLibrary, and PubMed databases with forward snowballing performed on Google Scholar. The quantitative analysis used 47 studies. The explainable analysis was performed on the classification algorithm and fusion methods used in the selected studies. The pooled sensitivity and specificity, including diagnostic efficiency, were evaluated by conducting a meta-analysis based on a bivariate model with the hierarchical summary receiver operating characteristics (ROC) curve of multimodal neuroimaging data and ML methods in the classification of AD stages. Wilcoxon signed-rank test is further used to statistically compare the accuracy scores of the existing models. With a 95% confidence interval of 78.87-87.71%, the combined sensitivity for separating participants with mild cognitive impairment (MCI) from healthy control (NC) participants was 83.77%; for separating participants with AD from NC, it was 94.60% (90.76%, 96.89%); for separating participants with progressive MCI (pMCI) from stable MCI (sMCI), it was 80.41% (74.73%, 85.06%). With a 95% confidence interval (78.87%, 87.71%), the Pooled sensitivity for distinguishing mild cognitive impairment (MCI) from healthy control (NC) participants was 83.77%, with a 95% confidence interval (90.76%, 96.89%), the Pooled sensitivity for distinguishing AD from NC was 94.60%, likewise (MCI) from healthy control (NC) participants was 83.77% progressive MCI (pMCI) from stable MCI (sMCI) was 80.41% (74.73%, 85.06%), and early MCI (EMCI) from NC was 86.63% (82.43%, 89.95%). Pooled specificity for differentiating MCI from NC was 79.16% (70.97%, 87.71%), AD from NC was 93.49% (91.60%, 94.90%), pMCI from sMCI was 81.44% (76.32%, 85.66%), and EMCI from NC was 85.68% (81.62%, 88.96%). The Wilcoxon signed rank test showed a low P-value across all the classification tasks. Multimodal neuroimaging data with ML is a promising future in classifying the stages of AD but more research is required to increase the validity of its application in clinical practice.
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Affiliation(s)
- Modupe Odusami
- Department of Multimedia Engineering, Kaunas University of Technology, Kaunas, Lithuania
| | - Rytis Maskeliūnas
- Department of Multimedia Engineering, Kaunas University of Technology, Kaunas, Lithuania
| | | | - Sanjay Misra
- Department of Applied Data Science, Institute for Energy Technology, Halden, Norway
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De A, Mishra TK, Saraf S, Tripathy B, Reddy SS. A Review on the Use of Modern Computational Methods in Alzheimer's Disease-Detection and Prediction. Curr Alzheimer Res 2024; 20:845-861. [PMID: 38468529 DOI: 10.2174/0115672050301514240307071217] [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: 01/13/2024] [Revised: 02/07/2024] [Accepted: 02/20/2024] [Indexed: 03/13/2024]
Abstract
Discoveries in the field of medical sciences are blooming rapidly at the cost of voluminous efforts. Presently, multidisciplinary research activities have been especially contributing to catering cutting-edge solutions to critical problems in the domain of medical sciences. The modern age computing resources have proved to be a boon in this context. Effortless solutions have become a reality, and thus, the real beneficiary patients are able to enjoy improved lives. One of the most emerging problems in this context is Alzheimer's disease, an incurable neurological disorder. For this, early diagnosis is made possible with benchmark computing tools and schemes. These benchmark schemes are the results of novel research contributions being made intermittently in the timeline. In this review, an attempt is made to explore all such contributions in the past few decades. A systematic review is made by categorizing these contributions into three folds, namely, First, Second, and Third Generations. However, priority is given to the latest ones as a handful of literature reviews are already available for the classical ones. Key contributions are discussed vividly. The objectives set for this review are to bring forth the latest discoveries in computing methodologies, especially those dedicated to the diagnosis of Alzheimer's disease. A detailed timeline of the contributions is also made available. Performance plots for certain key contributions are also presented for better graphical understanding.
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Affiliation(s)
- Arka De
- School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India
| | - Tusar Kanti Mishra
- Department of Computer Science and Engineering, Manipal Institute of Technology Bengaluru, Manipal Academy of Higher Education, Manipal, Karnataka, 576104, India
| | - Sameeksha Saraf
- School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India
| | - Balakrushna Tripathy
- School of Information Technology, Vellore Institute of Technology, Vellore, Tamil Nadu, India
| | - Shiva Shankar Reddy
- Department of Computer Science and Engineering, Sagi Rama Krishnam Raju Engineering College, Bhimavaram, Andhra Pradesh, India
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Lü W, Zhang M, Yu W, Kuang W, Chen L, Zhang W, Yu J, Lü Y. Differentiating Alzheimer's disease from mild cognitive impairment: a quick screening tool based on machine learning. BMJ Open 2023; 13:e073011. [PMID: 38070931 PMCID: PMC10729043 DOI: 10.1136/bmjopen-2023-073011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/21/2023] [Accepted: 11/20/2023] [Indexed: 12/18/2023] Open
Abstract
BACKGROUND Alzheimer's disease (AD) is a neurodegenerative disorder characterised by cognitive decline, behavioural and psychological symptoms of dementia (BPSD) and impairment of activities of daily living (ADL). Early differentiation of AD from mild cognitive impairment (MCI) is necessary. METHODS A total of 458 patients newly diagnosed with AD and MCI were included. Eleven batteries were used to evaluate ADL, BPSD and cognitive function (ABC). Machine learning approaches including XGboost, classification and regression tree, Bayes, support vector machines and logical regression were used to build and verify the new tool. RESULTS The Alzheimer's Disease Assessment Scale (ADAS-cog) word recognition task showed the best importance in judging AD and MCI, followed by correct numbers of auditory verbal learning test delay recall and ADAS-cog orientation. We also provided a selected ABC-Scale that covered ADL, BPSD and cognitive function with an estimated completion time of 18 min. The sensitivity was improved in the four models. CONCLUSION The quick screen ABC-Scale covers three dimensions of ADL, BPSD and cognitive function with good efficiency in differentiating AD from MCI.
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Affiliation(s)
- Wenqi Lü
- Department of Geriatrics, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
- Department of Psychiatry, West China Hospital, Sichuan University, Chengdu, China
| | - Meiwei Zhang
- College of Electrical Engineering, Chongqing University, Chongqing, China
| | - Weihua Yu
- Institutes of Neuroscience, Chongqing Medical University, Chongqing, China
| | - Weihong Kuang
- Department of Psychiatry, West China Hospital, Sichuan University, Chengdu, China
| | - Lihua Chen
- Institutes of Neuroscience, Chongqing Medical University, Chongqing, China
| | - Wenbo Zhang
- Department of Geriatrics, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Juan Yu
- College of Electrical Engineering, Chongqing University, Chongqing, China
| | - Yang Lü
- Department of Geriatrics, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
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5
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Blanco K, Salcidua S, Orellana P, Sauma-Pérez T, León T, Steinmetz LCL, Ibañez A, Duran-Aniotz C, de la Cruz R. Systematic review: fluid biomarkers and machine learning methods to improve the diagnosis from mild cognitive impairment to Alzheimer's disease. Alzheimers Res Ther 2023; 15:176. [PMID: 37838690 PMCID: PMC10576366 DOI: 10.1186/s13195-023-01304-8] [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: 06/02/2023] [Accepted: 09/15/2023] [Indexed: 10/16/2023]
Abstract
Mild cognitive impairment (MCI) is often considered an early stage of dementia, with estimated rates of progression to dementia up to 80-90% after approximately 6 years from the initial diagnosis. Diagnosis of cognitive impairment in dementia is typically based on clinical evaluation, neuropsychological assessments, cerebrospinal fluid (CSF) biomarkers, and neuroimaging. The main goal of diagnosing MCI is to determine its cause, particularly whether it is due to Alzheimer's disease (AD). However, only a limited percentage of the population has access to etiological confirmation, which has led to the emergence of peripheral fluid biomarkers as a diagnostic tool for dementias, including MCI due to AD. Recent advances in biofluid assays have enabled the use of sophisticated statistical models and multimodal machine learning (ML) algorithms for the diagnosis of MCI based on fluid biomarkers from CSF, peripheral blood, and saliva, among others. This approach has shown promise for identifying specific causes of MCI, including AD. After a PRISMA analysis, 29 articles revealed a trend towards using multimodal algorithms that incorporate additional biomarkers such as neuroimaging, neuropsychological tests, and genetic information. Particularly, neuroimaging is commonly used in conjunction with fluid biomarkers for both cross-sectional and longitudinal studies. Our systematic review suggests that cost-effective longitudinal multimodal monitoring data, representative of diverse cultural populations and utilizing white-box ML algorithms, could be a valuable contribution to the development of diagnostic models for AD due to MCI. Clinical assessment and biomarkers, together with ML techniques, could prove pivotal in improving diagnostic tools for MCI due to AD.
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Affiliation(s)
- Kevin Blanco
- Center for Social and Cognitive Neuroscience (CSCN), School of Psychology, Universidad Adolfo Ibanez, Diagonal Las Torres 2640, Peñalolén, Santiago, Chile
| | - Stefanny Salcidua
- Latin American Institute for Brain Health (BrainLat), Universidad Adolfo Ibáñez, Santiago, Chile
- Faculty of Engineering and Sciences, Universidad Adolfo Ibáñez, Diagonal Las Torres 2700, Building D, Peñalolén, Santiago, Chile
| | - Paulina Orellana
- Center for Social and Cognitive Neuroscience (CSCN), School of Psychology, Universidad Adolfo Ibanez, Diagonal Las Torres 2640, Peñalolén, Santiago, Chile
- Latin American Institute for Brain Health (BrainLat), Universidad Adolfo Ibáñez, Santiago, Chile
| | - Tania Sauma-Pérez
- Latin American Institute for Brain Health (BrainLat), Universidad Adolfo Ibáñez, Santiago, Chile
| | - Tomás León
- Global Brain Health Institute, Trinity College, Dublin, Ireland
- Memory and Neuropsychiatric Center (CMYN) Neurology Department, Hospital del Salvador and Faculty of Medicine, University of Chile, Santiago, Chile
| | - Lorena Cecilia López Steinmetz
- Latin American Institute for Brain Health (BrainLat), Universidad Adolfo Ibáñez, Santiago, Chile
- Technische Universität Berlin, Berlin, Deutschland
- Instituto de Investigaciones Psicológicas (IIPsi), Universidad Nacional de Córdoba (UNC) y Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Córdoba, Argentina
| | - Agustín Ibañez
- Latin American Institute for Brain Health (BrainLat), Universidad Adolfo Ibáñez, Santiago, Chile
- Global Brain Health Institute, Trinity College, Dublin, Ireland
- Global Brain Health Institute, University of California San Francisco (UCSF), San Francisco, CA, USA
- Cognitive Neuroscience Center (CNC), Universidad de San Andrés, & National Scientific and Technical Research Council (CONICET), Buenos Aires, Argentina
| | - Claudia Duran-Aniotz
- Center for Social and Cognitive Neuroscience (CSCN), School of Psychology, Universidad Adolfo Ibanez, Diagonal Las Torres 2640, Peñalolén, Santiago, Chile.
- Latin American Institute for Brain Health (BrainLat), Universidad Adolfo Ibáñez, Santiago, Chile.
| | - Rolando de la Cruz
- Latin American Institute for Brain Health (BrainLat), Universidad Adolfo Ibáñez, Santiago, Chile.
- Faculty of Engineering and Sciences, Universidad Adolfo Ibáñez, Diagonal Las Torres 2700, Building D, Peñalolén, Santiago, Chile.
- Data Observatory Foundation, ANID Technology Center No. DO210001, Santiago, Chile.
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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.
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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
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7
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Garcia Valencia OA, Thongprayoon C, Jadlowiec CC, Mao SA, Miao J, Cheungpasitporn W. Enhancing Kidney Transplant Care through the Integration of Chatbot. Healthcare (Basel) 2023; 11:2518. [PMID: 37761715 PMCID: PMC10530762 DOI: 10.3390/healthcare11182518] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2023] [Revised: 09/03/2023] [Accepted: 09/09/2023] [Indexed: 09/29/2023] Open
Abstract
Kidney transplantation is a critical treatment option for end-stage kidney disease patients, offering improved quality of life and increased survival rates. However, the complexities of kidney transplant care necessitate continuous advancements in decision making, patient communication, and operational efficiency. This article explores the potential integration of a sophisticated chatbot, an AI-powered conversational agent, to enhance kidney transplant practice and potentially improve patient outcomes. Chatbots and generative AI have shown promising applications in various domains, including healthcare, by simulating human-like interactions and generating contextually appropriate responses. Noteworthy AI models like ChatGPT by OpenAI, BingChat by Microsoft, and Bard AI by Google exhibit significant potential in supporting evidence-based research and healthcare decision making. The integration of chatbots in kidney transplant care may offer transformative possibilities. As a clinical decision support tool, it could provide healthcare professionals with real-time access to medical literature and guidelines, potentially enabling informed decision making and improved knowledge dissemination. Additionally, the chatbot has the potential to facilitate patient education by offering personalized and understandable information, addressing queries, and providing guidance on post-transplant care. Furthermore, under clinician or transplant pharmacist supervision, it has the potential to support post-transplant care and medication management by analyzing patient data, which may lead to tailored recommendations on dosages, monitoring schedules, and potential drug interactions. However, to fully ascertain its effectiveness and safety in these roles, further studies and validation are required. Its integration with existing clinical decision support systems may enhance risk stratification and treatment planning, contributing to more informed and efficient decision making in kidney transplant care. Given the importance of ethical considerations and bias mitigation in AI integration, future studies may evaluate long-term patient outcomes, cost-effectiveness, user experience, and the generalizability of chatbot recommendations. By addressing these factors and potentially leveraging AI capabilities, the integration of chatbots in kidney transplant care holds promise for potentially improving patient outcomes, enhancing decision making, and fostering the equitable and responsible use of AI in healthcare.
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Affiliation(s)
- Oscar A. Garcia Valencia
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA; (O.A.G.V.); (C.T.)
| | - Charat Thongprayoon
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA; (O.A.G.V.); (C.T.)
| | - Caroline C. Jadlowiec
- Division of Transplant Surgery, Department of Surgery, Mayo Clinic, Phoenix, AZ 85054, USA;
| | - Shennen A. Mao
- Division of Transplant Surgery, Department of Transplantation, Mayo Clinic, Jacksonville, FL 32224, USA
| | - Jing Miao
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA; (O.A.G.V.); (C.T.)
| | - Wisit Cheungpasitporn
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA; (O.A.G.V.); (C.T.)
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8
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Ayubcha C, Singh SB, Patel KH, Rahmim A, Hasan J, Liu L, Werner T, Alavi A. Machine learning in the positron emission tomography imaging of Alzheimer's disease. Nucl Med Commun 2023; 44:751-766. [PMID: 37395538 DOI: 10.1097/mnm.0000000000001723] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/04/2023]
Abstract
The utilization of machine learning techniques in medicine has exponentially increased over the last decades due to innovations in computer processing, algorithm development, and access to big data. Applications of machine learning techniques to neuroimaging specifically have unveiled various hidden interactions, structures, and mechanisms related to various neurological disorders. One application of interest is the imaging of Alzheimer's disease, the most common cause of progressive dementia. The diagnoses of Alzheimer's disease, mild cognitive impairment, and preclinical Alzheimer's disease have been difficult. Molecular imaging, particularly via PET scans, holds tremendous value in the imaging of Alzheimer's disease. To date, many novel algorithms have been developed with great success that leverage machine learning in the context of Alzheimer's disease. This review article provides an overview of the diverse applications of machine learning to PET imaging of Alzheimer's disease.
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Affiliation(s)
- Cyrus Ayubcha
- Harvard Medical School
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts
| | - Shashi B Singh
- Department of Radiology, Stanford University School of Medicine, Stanford, California
| | - Krishna H Patel
- Icahn School of Medicine at Mount Sinai, New York City, New York, USA
| | - Arman Rahmim
- Departments of Radiology and Physics, University of British Columbia, Vancouver, British Columbia, Canada
| | - Jareed Hasan
- Department of Radiology, Stanford University School of Medicine, Stanford, California
| | - Litian Liu
- Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | - Thomas Werner
- Department of Radiology, Stanford University School of Medicine, Stanford, California
| | - Abass Alavi
- Department of Radiology, Stanford University School of Medicine, Stanford, California
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9
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Ahmadzadeh M, Christie GJ, Cosco TD, Arab A, Mansouri M, Wagner KR, DiPaola S, Moreno S. Neuroimaging and machine learning for studying the pathways from mild cognitive impairment to alzheimer's disease: a systematic review. BMC Neurol 2023; 23:309. [PMID: 37608251 PMCID: PMC10463866 DOI: 10.1186/s12883-023-03323-2] [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: 08/03/2022] [Accepted: 07/08/2023] [Indexed: 08/24/2023] Open
Abstract
BACKGROUND This systematic review synthesizes the most recent neuroimaging procedures and machine learning approaches for the prediction of conversion from mild cognitive impairment to Alzheimer's disease dementia. METHODS We systematically searched PubMed, SCOPUS, and Web of Science databases following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) systematic review guidelines. RESULTS Our search returned 2572 articles, 56 of which met the criteria for inclusion in the final selection. The multimodality framework and deep learning techniques showed potential for predicting the conversion of MCI to AD dementia. CONCLUSION Findings of this systematic review identified that the possibility of using neuroimaging data processed by advanced learning algorithms is promising for the prediction of AD progression. We also provided a detailed description of the challenges that researchers are faced along with future research directions. The protocol has been registered in the International Prospective Register of Systematic Reviews- CRD42019133402 and published in the Systematic Reviews journal.
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Affiliation(s)
- Maryam Ahmadzadeh
- School of Interactive Arts and Technology, Simon Fraser University, 250 - 13450 102 Ave, Surrey, BC, Canada
| | - Gregory J Christie
- School of Interactive Arts and Technology, Simon Fraser University, 250 - 13450 102 Ave, Surrey, BC, Canada
| | - Theodore D Cosco
- Gerontology Research Center, Simon Fraser University, Vancouver, BC, Canada
- Oxford Institute of Population Ageing, University of Oxford, Oxford, UK
| | - Ali Arab
- Department of Computing Science, Simon Fraser University, Burnaby, BC, Canada
| | - Mehrdad Mansouri
- Department of Computing Science, Simon Fraser University, Burnaby, BC, Canada
| | - Kevin R Wagner
- Gerontology Research Center, Simon Fraser University, Vancouver, BC, Canada
| | - Steve DiPaola
- School of Interactive Arts and Technology, Simon Fraser University, 250 - 13450 102 Ave, Surrey, BC, Canada.
| | - Sylvain Moreno
- School of Interactive Arts and Technology, Simon Fraser University, 250 - 13450 102 Ave, Surrey, BC, Canada
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10
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Luo M, He Z, Cui H, Chen YPP, Ward P. Class activation attention transfer neural networks for MCI conversion prediction. Comput Biol Med 2023; 156:106700. [PMID: 36871338 DOI: 10.1016/j.compbiomed.2023.106700] [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: 11/17/2021] [Revised: 08/24/2022] [Accepted: 12/09/2022] [Indexed: 02/23/2023]
Abstract
Accurate prediction of the trajectory of Alzheimer's disease (AD) from an early stage is of substantial value for treatment and planning to delay the onset of AD. We propose a novel attention transfer method to train a 3D convolutional neural network to predict which patients with mild cognitive impairment (MCI) will progress to AD within 3 years. A model is first trained on a separate but related source task (task we are transferring information from) to automatically learn regions of interest (ROI) from a given image. Next we train a model to simultaneously classify progressive MCI (pMCI) and stable MCI (sMCI) (the target task we want to solve) and the ROIs learned from the source task. The predicted ROIs are then used to focus the model's attention on certain areas of the brain when classifying pMCI versus sMCI. Thus, in contrast to traditional transfer learning, we transfer attention maps instead of transferring model weights from a source task to the target classification task. Our Method outperformed all methods tested including traditional transfer learning and methods that used expert knowledge to define ROI. Furthermore, the attention map transferred from the source task highlights known Alzheimer's pathology.
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Affiliation(s)
- Min Luo
- Department of Computer Science and Information Technology, La Trobe University, Melbourne Vic, 3086, Australia
| | - Zhen He
- Department of Computer Science and Information Technology, La Trobe University, Melbourne Vic, 3086, Australia.
| | - Hui Cui
- Department of Computer Science and Information Technology, La Trobe University, Melbourne Vic, 3086, Australia
| | - Yi-Ping Phoebe Chen
- Department of Computer Science and Information Technology, La Trobe University, Melbourne Vic, 3086, Australia
| | - Phillip Ward
- Monash Biomedical Imaging, Melbourne Vic, 3800, Australia; Turner Institute for Brain and Mental Health, Monash University, Melbourne, Vic, 3800, Australia; Australian Research Council Centre of Excellence for Integrative Brain Function, Melbourne 3800, Australia
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11
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Javeed A, Dallora AL, Berglund JS, Ali A, Ali L, Anderberg P. Machine Learning for Dementia Prediction: A Systematic Review and Future Research Directions. J Med Syst 2023; 47:17. [PMID: 36720727 PMCID: PMC9889464 DOI: 10.1007/s10916-023-01906-7] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2022] [Accepted: 01/03/2023] [Indexed: 02/02/2023]
Abstract
Nowadays, Artificial Intelligence (AI) and machine learning (ML) have successfully provided automated solutions to numerous real-world problems. Healthcare is one of the most important research areas for ML researchers, with the aim of developing automated disease prediction systems. One of the disease detection problems that AI and ML researchers have focused on is dementia detection using ML methods. Numerous automated diagnostic systems based on ML techniques for early prediction of dementia have been proposed in the literature. Few systematic literature reviews (SLR) have been conducted for dementia prediction based on ML techniques in the past. However, these SLR focused on a single type of data modality for the detection of dementia. Hence, the purpose of this study is to conduct a comprehensive evaluation of ML-based automated diagnostic systems considering different types of data modalities such as images, clinical-features, and voice data. We collected the research articles from 2011 to 2022 using the keywords dementia, machine learning, feature selection, data modalities, and automated diagnostic systems. The selected articles were critically analyzed and discussed. It was observed that image data driven ML models yields promising results in terms of dementia prediction compared to other data modalities, i.e., clinical feature-based data and voice data. Furthermore, this SLR highlighted the limitations of the previously proposed automated methods for dementia and presented future directions to overcome these limitations.
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Affiliation(s)
- Ashir Javeed
- Aging Research Center, Karolinska Institutet, Tomtebodavagen, Stockholm, 17165, Solna, Sweden
- Department of Health, Blekinge Institute of Technology, Valhallavägen 1, Karlskrona, 37141, Blekinge, Sweden
| | - Ana Luiza Dallora
- Department of Health, Blekinge Institute of Technology, Valhallavägen 1, Karlskrona, 37141, Blekinge, Sweden
| | - Johan Sanmartin Berglund
- Department of Health, Blekinge Institute of Technology, Valhallavägen 1, Karlskrona, 37141, Blekinge, Sweden.
| | - Arif Ali
- Department of Computer Science, University of Science and Technology Bannu, Township, Bannu, 28100, Khyber-Pakhtunkhwa, Pakistan
| | - Liaqata Ali
- Department of Electrical Engineering, University of Science and Technology Bannu, Township, Bannu, 28100, Khyber-Pakhtunkhwa, Pakistan
| | - Peter Anderberg
- Department of Health, Blekinge Institute of Technology, Valhallavägen 1, Karlskrona, 37141, Blekinge, Sweden
- School of Health Sciences, University of Skovde, Högskolevägen 1, Skövde, SE-541 28, Skövde, Sweden
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12
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Bhargava Y, Baths V. Technology for dementia care: benefits, opportunities and concerns. JOURNAL OF GLOBAL HEALTH REPORTS 2022. [DOI: 10.29392/001c.39606] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022] Open
Abstract
The rise in incidence and prevalence of dementia globally is driving the technological revolution to develop effective healthcare solutions for dementia patients and caretakers. These solutions promise enhanced mechanisms to manage lifestyle, monitor cognitive performance, ensure the security and safety of the elderly, and deliver rehabilitation services. In this viewpoint, we contextualize the role of technology in dementia care by elaborating on these solutions and discussing the associated benefits, opportunities, and concerns.
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Affiliation(s)
- Yesoda Bhargava
- Cognitive Neuroscience Lab, BITS Pilani K K Birla Goa Campus, Zuarinagar, Goa, India
| | - Veeky Baths
- Cognitive Neuroscience Lab, BITS Pilani K K Birla Goa Campus, Zuarinagar, Goa, India
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13
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Battineni G, Chintalapudi N, Hossain MA, Losco G, Ruocco C, Sagaro GG, Traini E, Nittari G, Amenta F. Artificial Intelligence Models in the Diagnosis of Adult-Onset Dementia Disorders: A Review. Bioengineering (Basel) 2022; 9:bioengineering9080370. [PMID: 36004895 PMCID: PMC9405227 DOI: 10.3390/bioengineering9080370] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2022] [Revised: 07/25/2022] [Accepted: 08/02/2022] [Indexed: 11/16/2022] Open
Abstract
Background: The progressive aging of populations, primarily in the industrialized western world, is accompanied by the increased incidence of several non-transmittable diseases, including neurodegenerative diseases and adult-onset dementia disorders. To stimulate adequate interventions, including treatment and preventive measures, an early, accurate diagnosis is necessary. Conventional magnetic resonance imaging (MRI) represents a technique quite common for the diagnosis of neurological disorders. Increasing evidence indicates that the association of artificial intelligence (AI) approaches with MRI is particularly useful for improving the diagnostic accuracy of different dementia types. Objectives: In this work, we have systematically reviewed the characteristics of AI algorithms in the early detection of adult-onset dementia disorders, and also discussed its performance metrics. Methods: A document search was conducted with three databases, namely PubMed (Medline), Web of Science, and Scopus. The search was limited to the articles published after 2006 and in English only. The screening of the articles was performed using quality criteria based on the Newcastle–Ottawa Scale (NOS) rating. Only papers with an NOS score 7 were considered for further review. Results: The document search produced a count of 1876 articles and, because of duplication, 1195 papers were not considered. Multiple screenings were performed to assess quality criteria, which yielded 29 studies. All the selected articles were further grouped based on different attributes, including study type, type of AI model used in the identification of dementia, performance metrics, and data type. Conclusions: The most common adult-onset dementia disorders occurring were Alzheimer’s disease and vascular dementia. AI techniques associated with MRI resulted in increased diagnostic accuracy ranging from 73.3% to 99%. These findings suggest that AI should be associated with conventional MRI techniques to obtain a precise and early diagnosis of dementia disorders occurring in old age.
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Affiliation(s)
- Gopi Battineni
- Clinical Research Centre, School of Medicinal and Health Products Sciences, University of Camerino, 62032 Camerino, Italy
- Correspondence: ; Tel.: +39-3331728206
| | - Nalini Chintalapudi
- Clinical Research Centre, School of Medicinal and Health Products Sciences, University of Camerino, 62032 Camerino, Italy
| | - Mohammad Amran Hossain
- Clinical Research Centre, School of Medicinal and Health Products Sciences, University of Camerino, 62032 Camerino, Italy
| | - Giuseppe Losco
- School of Architecture and Design, University of Camerino, 63100 Ascoli Piceno, Italy
| | - Ciro Ruocco
- Clinical Research Centre, School of Medicinal and Health Products Sciences, University of Camerino, 62032 Camerino, Italy
| | - Getu Gamo Sagaro
- Clinical Research Centre, School of Medicinal and Health Products Sciences, University of Camerino, 62032 Camerino, Italy
| | - Enea Traini
- Clinical Research Centre, School of Medicinal and Health Products Sciences, University of Camerino, 62032 Camerino, Italy
| | - Giulio Nittari
- Clinical Research Centre, School of Medicinal and Health Products Sciences, University of Camerino, 62032 Camerino, Italy
| | - Francesco Amenta
- Clinical Research Centre, School of Medicinal and Health Products Sciences, University of Camerino, 62032 Camerino, Italy
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14
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Shabha G, Edwards DJ, Gaines K, Laycok P. Toward an Integrated Context-Based Design Approach for Dementia Residential Care Homes: A Review of Key Operational Design Problems. HERD-HEALTH ENVIRONMENTS RESEARCH & DESIGN JOURNAL 2022; 15:323-342. [PMID: 35833917 DOI: 10.1177/19375867221100210] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
OBJECTIVES, PURPOSE, OR AIM This article seeks to develop a context-based management system focusing on assessing key operational and design problems and affecting wayfinding in dementia residential care homes. BACKGROUND Dementia is multifaceted neurocognitive impairments largely attributed to cognitive deterioration manifested in memory loss and visuospatial deficit which have wider practical implications to both environmental safety and wayfinding and navigation of dementia user. Two key questions were addressed in this context: (1) How can cognitively facilitating assistive technology (AT) be made more user-focused to mitigate the impacts of cognitive impairments on environmental safety and wayfinding? (2) How can design intervention and changes in design topology, colors and texture, and internal finishing aid wayfinding, navigation, and orientation in dementia residential care homes? METHOD A systematic literature review and analysis was undertaken to assess the efficacy of key cognitively-related AT to support activities of daily living and environmental safety of dementia sufferers in a care home and aid wayfinding, navigation, and orientation. RESULTS Several key design variables to facilitate wayfinding and spatial orientation were identified which include design topology, floor finishing, signposting, and use of color and texture strengthened by meaning, emotional connection to places and cognitively focused intervention via memory cueing and objects-centered recognition. CONCLUSIONS Key operational and design guidelines were proposed to assist built environment, care home developers, clinicians and healthcare professionals, and care services providers. There is a need to move toward a dementia-centered design to address the challenges facing people living with dementia in care homes. This should be based on the interrelated behavioral, cognitive, and communication factors.
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Affiliation(s)
- Ghasson Shabha
- School of Engineering and the Built Environment, Birmingham City University, Birmingham, United Kingdom
| | - David J Edwards
- School of Engineering and the Built Environment, Birmingham City University, Birmingham, United Kingdom
| | - Kristi Gaines
- Department of Design, Texas Tech University (TTU), Lubbock, TX, USA
| | - Paul Laycok
- School of Engineering and the Built Environment, Birmingham City University, Birmingham, United Kingdom
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15
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Li R, Wang X, Lawler K, Garg S, Bai Q, Alty J. Applications of Artificial Intelligence to aid detection of dementia: a scoping review on current capabilities and future directions. J Biomed Inform 2022; 127:104030. [DOI: 10.1016/j.jbi.2022.104030] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2021] [Revised: 01/21/2022] [Accepted: 02/12/2022] [Indexed: 12/17/2022]
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16
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Machine learning approaches for parsing comorbidity/heterogeneity in antisociality and substance use disorders: A primer. PERSONALITY NEUROSCIENCE 2021; 4:e6. [PMID: 34909565 PMCID: PMC8640675 DOI: 10.1017/pen.2021.2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/10/2019] [Revised: 03/30/2021] [Accepted: 04/12/2021] [Indexed: 12/13/2022]
Abstract
By some accounts, as many as 93% of individuals diagnosed with antisocial personality disorder (ASPD) or psychopathy also meet criteria for some form of substance use disorder (SUD). This high level of comorbidity, combined with an overlapping biopsychosocial profile, and potentially interacting features, has made it difficult to delineate the shared/unique characteristics of each disorder. Moreover, while rarely acknowledged, both SUD and antisociality exist as highly heterogeneous disorders in need of more targeted parcellation. While emerging data-driven nosology for psychiatric disorders (e.g., Research Domain Criteria (RDoC), Hierarchical Taxonomy of Psychopathology (HiTOP)) offers the opportunity for a more systematic delineation of the externalizing spectrum, the interrogation of large, complex neuroimaging-based datasets may require data-driven approaches that are not yet widely employed in psychiatric neuroscience. With this in mind, the proposed article sets out to provide an introduction into machine learning methods for neuroimaging that can help parse comorbid, heterogeneous externalizing samples. The modest machine learning work conducted to date within the externalizing domain demonstrates the potential utility of the approach but remains highly nascent. Within the paper, we make suggestions for how future work can make use of machine learning methods, in combination with emerging psychiatric nosology systems, to further diagnostic and etiological understandings of the externalizing spectrum. Finally, we briefly consider some challenges that will need to be overcome to encourage further progress in the field.
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17
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Purnomo G, Yeo SJ, Liow MHL. Artificial intelligence in arthroplasty. ARTHROPLASTY 2021; 3:37. [PMID: 35236494 PMCID: PMC8796516 DOI: 10.1186/s42836-021-00095-3] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2021] [Accepted: 08/31/2021] [Indexed: 01/10/2023] Open
Abstract
Artificial intelligence (AI) is altering the world of medicine. Given the rapid advances in technology, computers are now able to learn and improve, imitating humanoid cognitive function. AI applications currently exist in various medical specialties, some of which are already in clinical use. This review presents the potential uses and limitations of AI in arthroplasty to provide a better understanding of the existing technology and future direction of this field.Recent literature demonstrates that the utilization of AI in the field of arthroplasty has the potential to improve patient care through better diagnosis, screening, planning, monitoring, and prediction. The implementation of AI technology will enable arthroplasty surgeons to provide patient-specific management in clinical decision making, preoperative health optimization, resource allocation, decision support, and early intervention. While this technology presents a variety of exciting opportunities, it also has several limitations and challenges that need to be overcome to ensure its safety and effectiveness.
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Affiliation(s)
- Glen Purnomo
- St. Vincentius a Paulo Catholic Hospital, Surabaya, Indonesia.
- Adult Reconstruction Service, Department of Orthopaedic Surgery, Singapore General Hospital, Singapore, Singapore.
| | - Seng-Jin Yeo
- Adult Reconstruction Service, Department of Orthopaedic Surgery, Singapore General Hospital, Singapore, Singapore
- Duke-NUS Medical School, Singapore, Singapore
| | - Ming Han Lincoln Liow
- Adult Reconstruction Service, Department of Orthopaedic Surgery, Singapore General Hospital, Singapore, Singapore
- Duke-NUS Medical School, Singapore, Singapore
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18
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Ying Q, Xing X, Liu L, Lin AL, Jacobs N, Liang G. Multi-Modal Data Analysis for Alzheimer's Disease Diagnosis: An Ensemble Model Using Imagery and Genetic Features. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2021; 2021:3586-3591. [PMID: 34892014 DOI: 10.1109/embc46164.2021.9630174] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Alzheimer's disease (AD) is a devastating neurological disorder primarily affecting the elderly. An estimated 6.2 million Americans age 65 and older are suffering from Alzheimer's dementia today. Brain magnetic resonance imaging (MRI) is widely used for the clinical diagnosis of AD. In the meanwhile, medical researchers have identified 40 risk locus using single-nucleotide polymorphisms (SNPs) information from Genome-wide association study (GWAS) in the past decades. However, existing studies usually treat MRI and GWAS separately. For instance, convolutional neural networks are often trained using MRI for AD diagnosis. GWAS and SNPs are frequently used to identify genomic traits. In this study, we propose a multi-modal AD diagnosis neural network that uses both MRIs and SNPs. The proposed method demonstrates a novel way to use GWAS findings by directly including SNPs in predictive models. We test the proposed methods on the Alzheimer's Disease Neuroimaging Initiative dataset. The evaluation results show that the proposed method improves the model performance on AD diagnosis and achieves 93.5% AUC and 96.1% AP, respectively, when patients have both MRI and SNP data. We believe this work brings exciting new insights to GWAS applications and sheds light on future research directions.
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19
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Singhania U, Tripathy B, Hasan MK, Anumbe NC, Alboaneen D, Ahmed FRA, Ahmed TE, Nour MMM. A Predictive and Preventive Model for Onset of Alzheimer's Disease. Front Public Health 2021; 9:751536. [PMID: 34708019 PMCID: PMC8542726 DOI: 10.3389/fpubh.2021.751536] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2021] [Accepted: 09/08/2021] [Indexed: 12/02/2022] Open
Abstract
Alzheimer's Disease (AD) is a neurodegenerative irreversible brain disorder that gradually wipes out the memory, thinking skills and eventually the ability to carry out day-to-day tasks. The amount of AD patients is rapidly increasing due to several lifestyle changes that affect biological functions. Detection of AD at its early stages helps in the treatment of patients. In this paper, a predictive and preventive model that uses biomarkers such as the amyloid-beta protein is proposed to detect, predict, and prevent AD onset. A Convolution Neural Network (CNN) based model is developed to predict AD at its early stages. The results obtained proved that the proposed model outperforms the traditional Machine Learning (ML) algorithms such as Logistic Regression, Support Vector Machine, Decision Tree Classifier, and K Nearest Neighbor algorithms.
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Affiliation(s)
- Udit Singhania
- School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, India
| | - Balakrushna Tripathy
- School of Information Technology and Engineering, Vellore Institute of Technology, Vellore, India
| | - Mohammad Kamrul Hasan
- Center for Cyber Security, Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia (UKM), Bangi, Malaysia
| | - Noble C. Anumbe
- Department of Mechanical Engineering, University of South Carolina, Columbia, SC, United States
| | - Dabiah Alboaneen
- Computer Science Department, College of Science and Humanities, Imam Abdulrahman Bin Faisal University, Jubail, Saudi Arabia
| | - Fatima Rayan Awad Ahmed
- Computer Science Department, College of Computer Engineering and Science, Prince Sattam Bin Abdulaziz University, Al-Kharj, Saudi Arabia
| | - Thowiba E. Ahmed
- Computer Science Department, College of Science and Humanities, Imam Abdulrahman Bin Faisal University, Jubail, Saudi Arabia
| | - Manasik M. Mohamed Nour
- Department of Mathematics, College of Science and Humanity, Prince Sattam Bin Abdulaziz University, Al-Kharj, Saudi Arabia
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20
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Tagde P, Tagde S, Bhattacharya T, Tagde P, Chopra H, Akter R, Kaushik D, Rahman MH. Blockchain and artificial intelligence technology in e-Health. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2021; 28:52810-52831. [PMID: 34476701 PMCID: PMC8412875 DOI: 10.1007/s11356-021-16223-0] [Citation(s) in RCA: 30] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/27/2021] [Accepted: 08/24/2021] [Indexed: 05/21/2023]
Abstract
Blockchain and artificial intelligence technologies are novel innovations in healthcare sector. Data on healthcare indices are collected from data published on Web of Sciences and other Google survey from various governing bodies. In this review, we focused on various aspects of blockchain and artificial intelligence and also discussed about integrating both technologies for making a significant difference in healthcare by promoting the implementation of a generalizable analytical technology that can be integrated into a more comprehensive risk management approach. This article has shown the various possibilities of creating reliable artificial intelligence models in e-Health using blockchain, which is an open network for the sharing and authorization of information. Healthcare professionals will have access to the blockchain to display the medical records of the patient, and AI uses a variety of proposed algorithms and decision-making capability, as well as large quantities of data. Thus, by integrating the latest advances of these technologies, the medical system will have improved service efficiency, reduced costs, and democratized healthcare. Blockchain enables the storage of cryptographic records, which AI needs.
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Affiliation(s)
- Priti Tagde
- Bhabha Pharmacy Research Institute, Bhabha University Bhopal, Bhopal M.P, India.
- PRISAL Foundation (Pharmaceutical Royal International Society), New delhi, India.
| | - Sandeep Tagde
- PRISAL Foundation (Pharmaceutical Royal International Society), New delhi, India
| | - Tanima Bhattacharya
- School of Chemistry & Chemical Engineering, Hubei University, Wuhan, China
- Department of Science & Engineering, Novel Global Community Education Foundation, Hebersham, Australia
| | - Pooja Tagde
- Practice of Medicine Department, Govt. Homeopathy College, Bhopal, M.P, India
| | - Hitesh Chopra
- Chitkara College of Pharmacy, Rajpura, Punjab, 140401, India
| | - Rokeya Akter
- Department of Pharmacy, Jagannath University, Sadarghat, Dhaka, 1100, Bangladesh
| | - Deepak Kaushik
- Department of Pharmaceutical Sciences, Maharshi Dayanand University, Rohtak, Haryana, 124001, India
| | - Md Habibur Rahman
- Department of Pharmacy, Southeast University, Banani, Dhaka, 1213, Bangladesh.
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21
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Grueso S, Viejo-Sobera R. Machine learning methods for predicting progression from mild cognitive impairment to Alzheimer's disease dementia: a systematic review. Alzheimers Res Ther 2021; 13:162. [PMID: 34583745 PMCID: PMC8480074 DOI: 10.1186/s13195-021-00900-w] [Citation(s) in RCA: 60] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2021] [Accepted: 09/12/2021] [Indexed: 01/18/2023]
Abstract
BACKGROUND An increase in lifespan in our society is a double-edged sword that entails a growing number of patients with neurocognitive disorders, Alzheimer's disease being the most prevalent. Advances in medical imaging and computational power enable new methods for the early detection of neurocognitive disorders with the goal of preventing or reducing cognitive decline. Computer-aided image analysis and early detection of changes in cognition is a promising approach for patients with mild cognitive impairment, sometimes a prodromal stage of Alzheimer's disease dementia. METHODS We conducted a systematic review following PRISMA guidelines of studies where machine learning was applied to neuroimaging data in order to predict whether patients with mild cognitive impairment might develop Alzheimer's disease dementia or remain stable. After removing duplicates, we screened 452 studies and selected 116 for qualitative analysis. RESULTS Most studies used magnetic resonance image (MRI) and positron emission tomography (PET) data but also magnetoencephalography. The datasets were mainly extracted from the Alzheimer's disease neuroimaging initiative (ADNI) database with some exceptions. Regarding the algorithms used, the most common was support vector machine with a mean accuracy of 75.4%, but convolutional neural networks achieved a higher mean accuracy of 78.5%. Studies combining MRI and PET achieved overall better classification accuracy than studies that only used one neuroimaging technique. In general, the more complex models such as those based on deep learning, combined with multimodal and multidimensional data (neuroimaging, clinical, cognitive, genetic, and behavioral) achieved the best performance. CONCLUSIONS Although the performance of the different methods still has room for improvement, the results are promising and this methodology has a great potential as a support tool for clinicians and healthcare professionals.
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Affiliation(s)
- Sergio Grueso
- Cognitive NeuroLab, Faculty of Health Sciences, Universitat Oberta de Catalunya (UOC), Rambla del Poblenou 156, 08018, Barcelona, Spain.
| | - Raquel Viejo-Sobera
- Cognitive NeuroLab, Faculty of Health Sciences, Universitat Oberta de Catalunya (UOC), Rambla del Poblenou 156, 08018, Barcelona, Spain
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22
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Hariry RE, Barenji RV, Paradkar A. Towards Pharma 4.0 in clinical trials: A future-orientated perspective. Drug Discov Today 2021; 27:315-325. [PMID: 34537331 DOI: 10.1016/j.drudis.2021.09.002] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2020] [Revised: 06/14/2021] [Accepted: 09/08/2021] [Indexed: 12/12/2022]
Abstract
Pharma 4.0, a technology ecosystem in drug development analogous to Industry 4.0 in healthcare, is transforming the traditional approach to drug discovery and development, aligning product quality with less time to market, and creating intelligent stakeholder networks through effective collaborations. The wide range of potential Pharma 4.0 networks have produced several conceptualizations, which have led to a lack of clarity and definition. The main emphasis of this paper is on the clinical trial stage of drug development in the Pharma 4.0 era. It highlights the merged computerized technologies that are currently used in clinical research, and proposes a framework for integrating Pharma 4.0 technologies. The impact of and barriers to employing the proposed framework are discussed, highlighting its potential and some future research applications.
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Affiliation(s)
- Reza Ebrahimi Hariry
- Department of Pharmacology and Toxicology, Ankara University, Ankara, Turkey; Smart Engineering and Health Research Group, Hacettepe University, Ankara, Turkey
| | - Reza Vatankhah Barenji
- Smart Engineering and Health Research Group, Hacettepe University, Ankara, Turkey; Department of Industrial Engineering, Hacettepe University, Ankara, Turkey.
| | - Anant Paradkar
- Centre for Pharmaceutical Engineering Science, University of Bradford, Bradford, UK
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23
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Speiser JL, Callahan KE, Houston DK, Fanning J, Gill TM, Guralnik JM, Newman AB, Pahor M, Rejeski WJ, Miller ME. Machine Learning in Aging: An Example of Developing Prediction Models for Serious Fall Injury in Older Adults. J Gerontol A Biol Sci Med Sci 2021; 76:647-654. [PMID: 32498077 DOI: 10.1093/gerona/glaa138] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2020] [Indexed: 12/31/2022] Open
Abstract
BACKGROUND Advances in computational algorithms and the availability of large datasets with clinically relevant characteristics provide an opportunity to develop machine learning prediction models to aid in diagnosis, prognosis, and treatment of older adults. Some studies have employed machine learning methods for prediction modeling, but skepticism of these methods remains due to lack of reproducibility and difficulty in understanding the complex algorithms that underlie models. We aim to provide an overview of two common machine learning methods: decision tree and random forest. We focus on these methods because they provide a high degree of interpretability. METHOD We discuss the underlying algorithms of decision tree and random forest methods and present a tutorial for developing prediction models for serious fall injury using data from the Lifestyle Interventions and Independence for Elders (LIFE) study. RESULTS Decision tree is a machine learning method that produces a model resembling a flow chart. Random forest consists of a collection of many decision trees whose results are aggregated. In the tutorial example, we discuss evaluation metrics and interpretation for these models. Illustrated using data from the LIFE study, prediction models for serious fall injury were moderate at best (area under the receiver operating curve of 0.54 for decision tree and 0.66 for random forest). CONCLUSIONS Machine learning methods offer an alternative to traditional approaches for modeling outcomes in aging, but their use should be justified and output should be carefully described. Models should be assessed by clinical experts to ensure compatibility with clinical practice.
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Affiliation(s)
- Jaime Lynn Speiser
- Department of Biostatistics and Data Science, Wake Forest School of Medicine, Winston-Salem, North Carolina
| | - Kathryn E Callahan
- Department of Internal Medicine, Section on Gerontology and Geriatric Medicine, Wake Forest School of Medicine, Winston-Salem, North Carolina
| | - Denise K Houston
- Department of Internal Medicine, Section on Gerontology and Geriatric Medicine, Wake Forest School of Medicine, Winston-Salem, North Carolina
| | - Jason Fanning
- Department of Health and Exercise Science, Wake Forest University, Winston-Salem, North Carolina
| | - Thomas M Gill
- Department of Internal Medicine, Yale School of Medicine, New Haven, Connecticut
| | - Jack M Guralnik
- Department of Epidemiology and Public Health, University of Maryland School of Medicine, Baltimore
| | - Anne B Newman
- Department of Epidemiology, Graduate School of Public Health, University of Pittsburgh, Pennsylvania
| | - Marco Pahor
- Department of Aging and Geriatric Research, University of Florida, Gainesville
| | - W Jack Rejeski
- Department of Health and Exercise Science, Wake Forest University, Winston-Salem, North Carolina
| | - Michael E Miller
- Department of Biostatistics and Data Science, Wake Forest School of Medicine, Winston-Salem, North Carolina
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24
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Boyle AJ, Gaudet VC, Black SE, Vasdev N, Rosa-Neto P, Zukotynski KA. Artificial intelligence for molecular neuroimaging. ANNALS OF TRANSLATIONAL MEDICINE 2021; 9:822. [PMID: 34268435 PMCID: PMC8246223 DOI: 10.21037/atm-20-6220] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/01/2020] [Accepted: 01/08/2021] [Indexed: 11/25/2022]
Abstract
In recent years, artificial intelligence (AI) or the study of how computers and machines can gain intelligence, has been increasingly applied to problems in medical imaging, and in particular to molecular imaging of the central nervous system. Many AI innovations in medical imaging include improving image quality, segmentation, and automating classification of disease. These advances have led to an increased availability of supportive AI tools to assist physicians in interpreting images and making decisions affecting patient care. This review focuses on the role of AI in molecular neuroimaging, primarily applied to positron emission tomography (PET) and single photon emission computed tomography (SPECT). We emphasize technical innovations such as AI in computed tomography (CT) generation for the purposes of attenuation correction and disease localization, as well as applications in neuro-oncology and neurodegenerative diseases. Limitations and future prospects for AI in molecular brain imaging are also discussed. Just as new equipment such as SPECT and PET revolutionized the field of medical imaging a few decades ago, AI and its related technologies are now poised to bring on further disruptive changes. An understanding of these new technologies and how they work will help physicians adapt their practices and succeed with these new tools.
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Affiliation(s)
- Amanda J Boyle
- Azrieli Centre for Neuro-Radiochemistry, Brain Health Imaging Centre, Centre for Addiction and Mental Health, Toronto, Ontario, Canada
| | - Vincent C Gaudet
- Department of Electrical and Computer Engineering, University of Waterloo, Waterloo, Ontario, Canada
| | - Sandra E Black
- Department of Medicine (Neurology), Sunnybrook Health Sciences Centre, University of Toronto, Toronto, Ontario, Canada
| | - Neil Vasdev
- Azrieli Centre for Neuro-Radiochemistry, Brain Health Imaging Centre, Centre for Addiction and Mental Health, Toronto, Ontario, Canada.,Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada
| | - Pedro Rosa-Neto
- Translational Neuroimaging Laboratory, McGill University Research Centre for Studies in Aging, Douglas Research Institute, McGill University, Montréal, Québec, Canada
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25
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Martínez-Florez JF, Osorio JD, Cediel JC, Rivas JC, Granados-Sánchez AM, López-Peláez J, Jaramillo T, Cardona JF. Short-Term Memory Binding Distinguishing Amnestic Mild Cognitive Impairment from Healthy Aging: A Machine Learning Study. J Alzheimers Dis 2021; 81:729-742. [PMID: 33814438 DOI: 10.3233/jad-201447] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2023]
Abstract
BACKGROUND Amnestic mild cognitive impairment (aMCI) is the most common preclinical stage of Alzheimer's disease (AD). A strategy to reduce the impact of AD is the early aMCI diagnosis and clinical intervention. Neuroimaging, neurobiological, and genetic markers have proved to be sensitive and specific for the early diagnosis of AD. However, the high cost of these procedures is prohibitive in low-income and middle-income countries (LIMCs). The neuropsychological assessments currently aim to identify cognitive markers that could contribute to the early diagnosis of dementia. OBJECTIVE Compare machine learning (ML) architectures classifying and predicting aMCI and asset the contribution of cognitive measures including binding function in distinction and prediction of aMCI. METHODS We conducted a two-year follow-up assessment of a sample of 154 subjects with a comprehensive multidomain neuropsychological battery. Statistical analysis was proposed using complete ML architectures to compare subjects' performance to classify and predict aMCI. Additionally, permutation importance and Shapley additive explanations (SHAP) routines were implemented for feature importance selection. RESULTS AdaBoost, gradient boosting, and XGBoost had the highest performance with over 80%success classifying aMCI, and decision tree and random forest had the highest performance with over 70%success predictive routines. Feature importance points, the auditory verbal learning test, short-term memory binding tasks, and verbal and category fluency tasks were used as variables with the first grade of importance to distinguish healthy cognition and aMCI. CONCLUSION Although neuropsychological measures do not replace biomarkers' utility, it is a relatively sensitive and specific diagnostic tool for aMCI. Further studies with ML must identify cognitive performance that differentiates conversion from average MCI to the pathological MCI observed in AD.
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Affiliation(s)
| | - Juan D Osorio
- Instituto de Psicología, Universidad del Valle, Santiago de Cali, Colombia
| | - Judith C Cediel
- Instituto de Psicología, Universidad del Valle, Santiago de Cali, Colombia.,Departamento de Estudios Psicológicos, Facultad de Derecho y Ciencias Sociales, Universidad ICESI , Santiago de Cali, Colombia
| | - Juan C Rivas
- Departamento de Psiquiatría, Facultad de Salud, Universidad del Valle, Santiago de Cali, Colombia.,Hospital Departamental Psiquiátrico Universitario del Valle, Santiago de Cali, Colombia.,Departamento de Psiquiatría, Fundación Valle del Lili, Santiago de Cali, Colombia
| | - Ana M Granados-Sánchez
- Departamento de Imágenes Diagnósticas, Fundación Valle del Lili, Santiago de Cali, Colombia
| | | | - Tania Jaramillo
- Instituto de Psicología, Universidad del Valle, Santiago de Cali, Colombia
| | - Juan F Cardona
- Instituto de Psicología, Universidad del Valle, Santiago de Cali, Colombia
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26
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Larentzakis A, Lygeros N. Artificial intelligence (AI) in medicine as a strategic valuable tool. Pan Afr Med J 2021; 38:184. [PMID: 33995790 PMCID: PMC8106796 DOI: 10.11604/pamj.2021.38.184.28197] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2021] [Accepted: 02/05/2021] [Indexed: 12/25/2022] Open
Abstract
Humans' creativity led to machines that outperform human capabilities in terms of workload, effectiveness, precision, endurance, strength, and repetitiveness. It has always been a vision and a way to transcend the existence and to give more sense to life, which is precious. The common denominator of all these creations was that they were meant to replace, enhance or go beyond the mechanical capabilities of the human body. The story takes another bifurcation when Alan Turing introduced the concept of a machine that could think, in 1950. Artificial intelligence, presented as a term in 1956, describes the use of computers to imitate intelligence and critical thinking comparable to humans. However, the revolution began in 1943, when artificial neural networks was an attempt to exploit the architecture of the human brain to perform tasks that conventional algorithms had little success with. Artificial intelligence is becoming a research focus and a tool of strategic value. The same observations apply in the field of healthcare, too. In this manuscript, we try to address key questions regarding artificial intelligence in medicine, such as what artificial intelligence is and how it works, what is its value in terms of application in medicine, and what are the prospects?
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Affiliation(s)
- Andreas Larentzakis
- First Department of Propaedeutic Surgery, Athens Medical School, National and Kapodistrian University of Athens, Hippocration General Athens Hospital, Athens, Greece
| | - Nik Lygeros
- Laboratoire de Génie des Procédés Catalytiques, Centre National de la Recherche Scientifique/École Supérieure de Chimie Physique Électronique, Lyon, France
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27
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Tschuggnall M, Grote V, Pirchl M, Holzner B, Rumpold G, Fischer MJ. Machine learning approaches to predict rehabilitation success based on clinical and patient-reported outcome measures. INFORMATICS IN MEDICINE UNLOCKED 2021. [DOI: 10.1016/j.imu.2021.100598] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022] Open
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28
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Tsang G, Zhou SM, Xie X. Modeling Large Sparse Data for Feature Selection: Hospital Admission Predictions of the Dementia Patients Using Primary Care Electronic Health Records. IEEE JOURNAL OF TRANSLATIONAL ENGINEERING IN HEALTH AND MEDICINE 2020; 9:3000113. [PMID: 33354439 PMCID: PMC7737850 DOI: 10.1109/jtehm.2020.3040236] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/07/2020] [Revised: 08/05/2020] [Accepted: 09/03/2020] [Indexed: 11/18/2022]
Abstract
A growing elderly population suffering from incurable, chronic conditions such as dementia present a continual strain on medical services due to mental impairment paired with high comorbidity resulting in increased hospitalization risk. The identification of at risk individuals allows for preventative measures to alleviate said strain. Electronic health records provide opportunity for big data analysis to address such applications. Such data however, provides a challenging problem space for traditional statistics and machine learning due to high dimensionality and sparse data elements. This article proposes a novel machine learning methodology: entropy regularization with ensemble deep neural networks (ECNN), which simultaneously provides high predictive performance of hospitalization of patients with dementia whilst enabling an interpretable heuristic analysis of the model architecture, able to identify individual features of importance within a large feature domain space. Experimental results on health records containing 54,647 features were able to identify 10 event indicators within a patient timeline: a collection of diagnostic events, medication prescriptions and procedural events, the highest ranked being essential hypertension. The resulting subset was still able to provide a highly competitive hospitalization prediction (Accuracy: 0.759) as compared to the full feature domain (Accuracy: 0.755) or traditional feature selection techniques (Accuracy: 0.737), a significant reduction in feature size. The discovery and heuristic evidence of correlation provide evidence for further clinical study of said medical events as potential novel indicators. There also remains great potential for adaption of ECNN within other medical big data domains as a data mining tool for novel risk factor identification.
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Affiliation(s)
- Gavin Tsang
- Department of Computer ScienceSwansea UniversitySwanseaSA1 8ENU.K.
| | - Shang-Ming Zhou
- Institute of Life Science, Swansea UniversitySwanseaSA1 8ENU.K.
| | - Xianghua Xie
- Department of Computer ScienceSwansea UniversitySwanseaSA1 8ENU.K.
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29
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Keator DB, Doran E, Taylor L, Phelan MJ, Hom C, Tseung K, van Erp TGM, Potkin SG, Brickman AM, Rosas DH, Yassa MA, Silverman W, Lott IT. Brain amyloid and the transition to dementia in Down syndrome. ALZHEIMER'S & DEMENTIA (AMSTERDAM, NETHERLANDS) 2020; 12:e12126. [PMID: 33204814 PMCID: PMC7656170 DOI: 10.1002/dad2.12126] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 08/18/2020] [Revised: 09/30/2020] [Accepted: 10/01/2020] [Indexed: 11/15/2022]
Abstract
INTRODUCTION Down syndrome (DS) is associated with elevated risk for Alzheimer's disease (AD) due to amyloid beta (Aβ) lifelong accumulation. We hypothesized that the spatial distribution of brain Aβ predicts future dementia conversion in individuals with DS. METHODS We acquired 18F-florbetapir positron emission tomography scans from 19 nondemented individuals with DS at baseline and monitored them for 4 years, with five individuals transitioning to dementia. Machine learning classification using an independent test set determined features on 18F-florbetapir standardized uptake value ratio maps that predicted transition. RESULTS In addition to "AD signature" regions including the inferior parietal cortex, temporal lobes, and the cingulum, we found that Aβ cortical binding in the prefrontal and superior frontal cortices distinguished subjects who transitioned to dementia. Classification did well in predicting transitioners. DISCUSSION Our study suggests that specific regional profiles of brain amyloid in older adults with DS may predict cognitive decline and are informative in evaluating the risk for dementia.
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Affiliation(s)
- David B. Keator
- Department of Psychiatry and Human BehaviorUniversity of California, IrvineIrvineCaliforniaUSA
| | - Eric Doran
- Department of PediatricsIrvine Medical CenterUniversity of CaliforniaOrangeCaliforniaUSA
| | - Lisa Taylor
- Department of Psychiatry and Human BehaviorUniversity of California, IrvineIrvineCaliforniaUSA
| | - Michael J. Phelan
- Department of StatisticsUniversity of California, IrvineIrvineCaliforniaUSA
| | - Christy Hom
- Department of Psychiatry and Human BehaviorUniversity of California, IrvineIrvineCaliforniaUSA
| | | | - Theo G. M. van Erp
- Department of Psychiatry and Human BehaviorUniversity of California, IrvineIrvineCaliforniaUSA
- Center for the Neurobiology of Learning and MemoryUniversity of California, IrvineIrvineCaliforniaUSA
| | - Steven G. Potkin
- Department of Psychiatry and Human BehaviorUniversity of California, IrvineIrvineCaliforniaUSA
| | - Adam M. Brickman
- Department of NeurologyCollege of Physicians and SurgeonsColumbia University, New YorkTaub Institute for Research on Alzheimer's Disease and the Aging BrainNew YorkUSA
| | - Diana H. Rosas
- Massachusetts General HospitalHarvard UniversityBostonMassachusettsUSA
| | - Michael A. Yassa
- Department of Psychiatry and Human BehaviorUniversity of California, IrvineIrvineCaliforniaUSA
- Center for the Neurobiology of Learning and MemoryUniversity of California, IrvineIrvineCaliforniaUSA
- Department of Neurobiology and BehaviorUniversity of California, IrvineIrvineCaliforniaUSA
| | - Wayne Silverman
- Department of PediatricsIrvine Medical CenterUniversity of CaliforniaOrangeCaliforniaUSA
| | - Ira T. Lott
- Department of PediatricsIrvine Medical CenterUniversity of CaliforniaOrangeCaliforniaUSA
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30
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Kaul V, Enslin S, Gross SA. History of artificial intelligence in medicine. Gastrointest Endosc 2020; 92:807-812. [PMID: 32565184 DOI: 10.1016/j.gie.2020.06.040] [Citation(s) in RCA: 196] [Impact Index Per Article: 49.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/30/2020] [Accepted: 06/11/2020] [Indexed: 02/06/2023]
Abstract
Artificial intelligence (AI) was first described in 1950; however, several limitations in early models prevented widespread acceptance and application to medicine. In the early 2000s, many of these limitations were overcome by the advent of deep learning. Now that AI systems are capable of analyzing complex algorithms and self-learning, we enter a new age in medicine where AI can be applied to clinical practice through risk assessment models, improving diagnostic accuracy and workflow efficiency. This article presents a brief historical perspective on the evolution of AI over the last several decades and the introduction and development of AI in medicine in recent years. A brief summary of the major applications of AI in gastroenterology and endoscopy are also presented, which are reviewed in further detail by several other articles in this issue of Gastrointestinal Endoscopy.
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Affiliation(s)
- Vivek Kaul
- Division of Gastroenterology & Hepatology, University of Rochester Medical Center, Rochester, New York, USA
| | - Sarah Enslin
- Division of Gastroenterology & Hepatology, University of Rochester Medical Center, Rochester, New York, USA
| | - Seth A Gross
- Division of Gastroenterology & Hepatology, NYU Langone Health System, New York, New York, USA
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31
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Buegler M, Harms R, Balasa M, Meier IB, Exarchos T, Rai L, Boyle R, Tort A, Kozori M, Lazarou E, Rampini M, Cavaliere C, Vlamos P, Tsolaki M, Babiloni C, Soricelli A, Frisoni G, Sanchez-Valle R, Whelan R, Merlo-Pich E, Tarnanas I. Digital biomarker-based individualized prognosis for people at risk of dementia. ALZHEIMER'S & DEMENTIA: DIAGNOSIS, ASSESSMENT & DISEASE MONITORING 2020; 12:e12073. [PMID: 32832589 PMCID: PMC7437401 DOI: 10.1002/dad2.12073] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/18/2020] [Accepted: 06/30/2020] [Indexed: 12/23/2022]
Abstract
Background Research investigating treatments and interventions for cognitive decline fail due to difficulties in accurately recognizing behavioral signatures in the presymptomatic stages of the disease. For this validation study, we took our previously constructed digital biomarker‐based prognostic models and focused on generalizability and robustness of the models. Method We validated prognostic models characterizing subjects using digital biomarkers in a longitudinal, multi‐site, 40‐month prospective study collecting data in memory clinics, general practitioner offices, and home environments. Results Our models were able to accurately discriminate between healthy subjects and individuals at risk to progress to dementia within 3 years. The model was also able to differentiate between people with or without amyloid neuropathology and classify fast and slow cognitive decliners with a very good diagnostic performance. Conclusion Digital biomarker prognostic models can be a useful tool to assist large‐scale population screening for the early detection of cognitive impairment and patient monitoring over time.
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Affiliation(s)
| | | | - Mircea Balasa
- Global Brain Health Institute San Francisco, California USA
| | | | - Themis Exarchos
- Bioinformatics and Human Electrophysiology Laboratory Corfu Greece
| | - Laura Rai
- Trinity College Institute of Neuroscience College Green, Dublin Ireland
| | - Rory Boyle
- Trinity College Institute of Neuroscience College Green, Dublin Ireland
| | - Adria Tort
- Institut d'Investigació Biomèdica August Pi i Sunyer Carrer del Rosselló, Barcelona Spain
| | - Maha Kozori
- Greek Association for Alzheimer's Disease and Related Disorders, Thessaloniki Greece
| | - Eutuxia Lazarou
- Greek Association for Alzheimer's Disease and Related Disorders, Thessaloniki Greece
| | | | | | | | - Magda Tsolaki
- 1st Department of Neurology AHEPA University Hospital, Thessaloniki Greece.,Information Technologies Institute Centre for Research and Technology Hellas (CERTH); Aristotle University of Thessaloniki, Thermi Greece
| | - Claudio Babiloni
- Department of Physiology and Pharmacology University of Rome, Roma Italy.,San Raffaele Cassino, Cassino (FR), Italy
| | - Andrea Soricelli
- 1st Department of Neurology AHEPA University Hospital, Thessaloniki Greece.,University of Naples Parthenope, Napoli Italy
| | - Giovanni Frisoni
- University of Geneva, Geneva Switzerland.,Laboratory of Neuroimaging and Alzheimer's Epidemiology IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia Italy.,Memory Clinic and LANVIE, Geneva Switzerland.,University of Brescia, Brescia Italy
| | - Raquel Sanchez-Valle
- IDIBAPS Neurological Tissue Bank Hospital Clinic, Barcelona Spain.,Institut d'Investigació Biomèdica August Pi i Sunyer, Barcelona Spain.,Alzheimer's Disease and Other Cognitive Disorders Unit Hospital Clínic Institut d'Investigacions Biomediques August Pi i Sunyer (IDIBAPS), Barcelona Spain
| | - Robert Whelan
- Trinity College Institute of Neuroscience College Green, Dublin Ireland
| | | | - Ioannis Tarnanas
- Altoida Inc. Houston, Texas USA.,Global Brain Health Institute San Francisco, California USA.,Hellenic Initiative Against Alzheimer's Disease, Johns Hopkins Precision Medicine Center, Baltimore, Maryland, United States and BiHeLab, Ionian University, Kerkira, Greece
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β-amyloid and tau drive early Alzheimer's disease decline while glucose hypometabolism drives late decline. Commun Biol 2020; 3:352. [PMID: 32632135 PMCID: PMC7338410 DOI: 10.1038/s42003-020-1079-x] [Citation(s) in RCA: 51] [Impact Index Per Article: 12.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2019] [Accepted: 06/15/2020] [Indexed: 12/31/2022] Open
Abstract
Clinical trials focusing on therapeutic candidates that modify β-amyloid (Aβ) have repeatedly failed to treat Alzheimer’s disease (AD), suggesting that Aβ may not be the optimal target for treating AD. The evaluation of Aβ, tau, and neurodegenerative (A/T/N) biomarkers has been proposed for classifying AD. However, it remains unclear whether disturbances in each arm of the A/T/N framework contribute equally throughout the progression of AD. Here, using the random forest machine learning method to analyze participants in the Alzheimer’s Disease Neuroimaging Initiative dataset, we show that A/T/N biomarkers show varying importance in predicting AD development, with elevated biomarkers of Aβ and tau better predicting early dementia status, and biomarkers of neurodegeneration, especially glucose hypometabolism, better predicting later dementia status. Our results suggest that AD treatments may also need to be disease stage-oriented with Aβ and tau as targets in early AD and glucose metabolism as a target in later AD. Here the authors analyze the Alzheimer’s Disease Neuroimaging Initiative dataset using random forest machine learning methods and determine that Aβ and tau biomarkers are better predictors of early dementia status, while glucose hypometabolism is a better predictor of later dementia status. These results suggest the need for stage-oriented Alzheimer’s disease treatments.
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33
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Zhu W, Xie L, Han J, Guo X. The Application of Deep Learning in Cancer Prognosis Prediction. Cancers (Basel) 2020; 12:E603. [PMID: 32150991 PMCID: PMC7139576 DOI: 10.3390/cancers12030603] [Citation(s) in RCA: 120] [Impact Index Per Article: 30.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2020] [Revised: 02/28/2020] [Accepted: 03/02/2020] [Indexed: 12/11/2022] Open
Abstract
Deep learning has been applied to many areas in health care, including imaging diagnosis, digital pathology, prediction of hospital admission, drug design, classification of cancer and stromal cells, doctor assistance, etc. Cancer prognosis is to estimate the fate of cancer, probabilities of cancer recurrence and progression, and to provide survival estimation to the patients. The accuracy of cancer prognosis prediction will greatly benefit clinical management of cancer patients. The improvement of biomedical translational research and the application of advanced statistical analysis and machine learning methods are the driving forces to improve cancer prognosis prediction. Recent years, there is a significant increase of computational power and rapid advancement in the technology of artificial intelligence, particularly in deep learning. In addition, the cost reduction in large scale next-generation sequencing, and the availability of such data through open source databases (e.g., TCGA and GEO databases) offer us opportunities to possibly build more powerful and accurate models to predict cancer prognosis more accurately. In this review, we reviewed the most recent published works that used deep learning to build models for cancer prognosis prediction. Deep learning has been suggested to be a more generic model, requires less data engineering, and achieves more accurate prediction when working with large amounts of data. The application of deep learning in cancer prognosis has been shown to be equivalent or better than current approaches, such as Cox-PH. With the burst of multi-omics data, including genomics data, transcriptomics data and clinical information in cancer studies, we believe that deep learning would potentially improve cancer prognosis.
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Affiliation(s)
- Wan Zhu
- Department of Preventive Medicine, Institute of Biomedical Informatics, Cell Signal Transduction Laboratory, Bioinformatics center, School of Basic Medical Sciences, Henan University, Kaifeng 475004, China;
- Department of Anesthesia, Stanford University, 300 Pasteur Drive, Stanford, CA 94305, USA
| | - Longxiang Xie
- Department of Preventive Medicine, Institute of Biomedical Informatics, Cell Signal Transduction Laboratory, Bioinformatics center, School of Basic Medical Sciences, Henan University, Kaifeng 475004, China;
| | - Jianye Han
- Department of Computer Science, University of Illinois, Urbana Champions, IL 61820, USA;
| | - Xiangqian Guo
- Department of Preventive Medicine, Institute of Biomedical Informatics, Cell Signal Transduction Laboratory, Bioinformatics center, School of Basic Medical Sciences, Henan University, Kaifeng 475004, China;
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Kitamura G, Deible C. Retraining an open-source pneumothorax detecting machine learning algorithm for improved performance to medical images. Clin Imaging 2020; 61:15-19. [PMID: 31954346 DOI: 10.1016/j.clinimag.2020.01.008] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2019] [Revised: 12/06/2019] [Accepted: 01/07/2020] [Indexed: 11/26/2022]
Abstract
PURPOSE To validate a machine learning model trained on an open source dataset and subsequently optimize it to chest X-rays with large pneumothoraces from our institution. METHODS The study was retrospective in nature. The open-source chest X-ray (CXR8) dataset was dichotomized to cases with pneumothorax (PTX) and all other cases (non-PTX), resulting in 41,946 non-PTX and 4696 PTX cases for the training set and 11,120 non-PTX and 541 PTX cases for the validation set. A limited supervision machine learning model was constructed to incorporate both localized and unlocalized pathology. Cases were then queried from our health system from 2013 to 2017. A total of 159 pneumothorax and 682 non-pneumothorax cases were available for the training set. For the validation set, 48 pneumothorax and 1287 non-pneumothorax cases were available. The model was trained, a receiver operator curve (ROC) was created, and output metrics, including area under the curve (AUC), sensitivity and specificity were calculated. RESULTS Initial training of the model using the CXR8 dataset resulted in an AUC of 0.90 for pneumothorax detection. Naively inferring our own validation dataset on the CXR8 trained model output an AUC of 0.59. After re-training the model with our own training dataset, the validation dataset inference output an AUC of 0.90. CONCLUSION Our study showed that even though you may get great results on open-source datasets, those models may not translate well to real world data without an intervening retraining process.
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Affiliation(s)
- Gene Kitamura
- University of Pittsburgh Medical Center (UPMC) and University of Pittsburgh, UPMC Department of Radiology, 200 Lothrop St., Pittsburgh, PA 15213, USA.
| | - Christopher Deible
- University of Pittsburgh Medical Center (UPMC) and University of Pittsburgh, UPMC Department of Radiology, 200 Lothrop St., Pittsburgh, PA 15213, USA.
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Park JH. Machine-Learning Algorithms Based on Screening Tests for Mild Cognitive Impairment. Am J Alzheimers Dis Other Demen 2020; 35:1533317520927163. [PMID: 32602347 PMCID: PMC10623967 DOI: 10.1177/1533317520927163] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
BACKGROUND The mobile screening test system for mild cognitive impairment (mSTS-MCI) was developed and validated to address the low sensitivity and specificity of the Montreal Cognitive Assessment (MoCA) widely used clinically. OBJECTIVE This study was to evaluate the efficacy machine learning algorithms based on the mSTS-MCI and Korean version of MoCA. METHOD In total, 103 healthy individuals and 74 patients with MCI were randomly divided into training and test data sets, respectively. The algorithm using TensorFlow was trained based on the training data set, and then its accuracy was calculated based on the test data set. The cost was calculated via logistic regression in this case. RESULT Predictive power of the algorithms was higher than those of the original tests. In particular, the algorithm based on the mSTS-MCI showed the highest positive-predictive value. CONCLUSION The machine learning algorithms predicting MCI showed the comparable findings with the conventional screening tools.
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Affiliation(s)
- Jin-Hyuck Park
- Department of Occupational Therapy, College of Medical Science, Soonchunhyang University, Asan, Korea
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36
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Zandifar A, Fonov VS, Ducharme S, Belleville S, Collins DL. MRI and cognitive scores complement each other to accurately predict Alzheimer's dementia 2 to 7 years before clinical onset. Neuroimage Clin 2019; 25:102121. [PMID: 31931400 PMCID: PMC6957831 DOI: 10.1016/j.nicl.2019.102121] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2019] [Revised: 11/17/2019] [Accepted: 12/10/2019] [Indexed: 01/19/2023]
Abstract
BACKGROUND Predicting cognitive decline and the eventual onset of dementia in patients with Mild Cognitive Impairment (MCI) is of high value for patient management and potential cohort enrichment in pharmaceutical trials. We used cognitive scores and MRI biomarkers from a single baseline visit to predict the onset of dementia due to AD in an amnestic MCI (aMCI) population over a nine-year follow-up period. METHOD All aMCI subjects from ADNI1, ADNI2, and ADNI-GO with available baseline neurocognitive scores and T1w MRI were included in the study (n = 756). We built a Naïve Bayes classifier for every year over a 9-year follow-up period and tested each one with Leave one out cross validation. RESULTS We reached 87% prediction accuracy at five years follow-up with an AUC > 0.85 from two to seven years (peaking at 0.92 at five years). Both neurocognitive scores and MRI biomarkers were needed to make the prognostic models highly sensitive and specific, especially for longer follow-ups. MRI features are more sensitive, while cognitive features bring specificity to the prediction. CONCLUSION Combining cognitive scores and MRI biomarkers yield accurate prediction years before onset of dementia. Such a tool may be helpful in selecting patients that would most benefit from lifestyle changes, and eventually early treatments that would slow cognitive decline and delay the onset of dementia.
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Affiliation(s)
- Azar Zandifar
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, 3801 University Street, Room WB320, Montreal, QC H3A 2B4, Canada; Department of Biomedical Engineering, McGill University, Montreal, Canada.
| | - Vladimir S Fonov
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, 3801 University Street, Room WB320, Montreal, QC H3A 2B4, Canada.
| | - Simon Ducharme
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, 3801 University Street, Room WB320, Montreal, QC H3A 2B4, Canada; Department of Psychiatry, McGill University Health Centre, McGill University, Montreal, Canada.
| | - Sylvie Belleville
- Institut Universitaire de Gériatrie de Montréal, Montreal, Canada; Department of Psychology, Centre de Recherche en Neuropsychologie et Cognition, Université de Montréal, Montreal, Canada.
| | - D Louis Collins
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, 3801 University Street, Room WB320, Montreal, QC H3A 2B4, Canada; Department of Biomedical Engineering, McGill University, Montreal, Canada.
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Comparative Machine-Learning Approach: A Follow-Up Study on Type 2 Diabetes Predictions by Cross-Validation Methods. MACHINES 2019. [DOI: 10.3390/machines7040074] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/16/2023]
Abstract
(1) Background: Diabetes is a common chronic disease and a leading cause of death. Early diagnosis gives patients with diabetes the opportunity to improve their dietary habits and lifestyle and manage the disease successfully. Several studies have explored the use of machine learning (ML) techniques to predict and diagnose this disease. In this study, we conducted experiments to predict diabetes in Pima Indian females with particular ML classifiers. (2) Method: A Pima Indian diabetes dataset (PIDD) with 768 female patients was considered for this study. Different data mining operations were performed to a conduct comparative analysis of four different ML classifiers: Naïve Bayes (NB), J48, Logistic Regression (LR), and Random Forest (RF). These models were analyzed by different cross-validation (K = 5, 10, 15, and 20) values, and the performance measurements of accuracy, precision, F-score, recall, and AUC were calculated for each model. (3) Results: LR was found to have the highest accuracy (0.77) for all ‘k’ values. When k = 5, the accuracy of J48, NB, and RF was found to be 0.71, 0.76, and 0.75. For k = 10, the accuracy of J48, NB, and RF was found to be 0.73, 0.76, 0.74, while for k = 15, 20, the accuracy of NB was found to be 0.76. The accuracy of J48 and RF was found to be 0.76 when k = 15, and 0.75 when k = 20. Other parameters, such as precision, f-score, recall, and AUC, were also considered in evaluations to rank the algorithms. (4) Conclusion: The present study on PIDD sought to identify an optimized ML model, using with cross-validation methods. The AUC of LR was 0.83, RF 0.82, and NB 0.81). These three were ranked as the best models for predicting whether a patient is diabetic or not.
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Bilgel M, Jedynak BM. Predicting time to dementia using a quantitative template of disease progression. ALZHEIMER'S & DEMENTIA (AMSTERDAM, NETHERLANDS) 2019; 11:205-215. [PMID: 30859120 PMCID: PMC6396328 DOI: 10.1016/j.dadm.2019.01.005] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
INTRODUCTION Characterization of longitudinal trajectories of biomarkers implicated in sporadic Alzheimer's disease (AD) in decades before clinical diagnosis is important for disease prevention and monitoring. METHODS We used a multivariate Bayesian model to temporally align 1369 Alzheimer's disease Neuroimaging Initiative participants based on the similarity of their longitudinal biomarker measures and estimated a quantitative template of the temporal evolution of cerebrospinal fluid Aβ 1 - 42 , p- ta u 181 p , and t-tau and hippocampal volume, brain glucose metabolism, and cognitive measurements. We computed biomarker trajectories as a function of time to AD dementia and predicted AD dementia onset age in a disjoint sample. RESULTS Quantitative template showed early changes in verbal memory, cerebrospinal fluid Aβ1-42 and p-tau181p, and hippocampal volume. Mean error in predicted AD dementia onset age was < 1.5 years. DISCUSSION Our method provides a quantitative approach for characterizing the natural history of AD starting at preclinical stages despite the lack of individual-level longitudinal data spanning the entire disease timeline.
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Affiliation(s)
- Murat Bilgel
- Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA
| | - Bruno M. Jedynak
- Dept. of Mathematics and Statistics, Portland State University, Portland, OR, USA
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Low SK, Nakamura Y. The road map of cancer precision medicine with the innovation of advanced cancer detection technology and personalized immunotherapy. Jpn J Clin Oncol 2019; 49:596-603. [PMID: 31135897 DOI: 10.1093/jjco/hyz073] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2019] [Revised: 04/05/2019] [Accepted: 05/09/2019] [Indexed: 12/12/2022] Open
Abstract
The advancement of cancer genomics research due to the development of next generation sequencing technologies is going to bring the promise of cancer precision medicine, in turn revolutionizing cancer detection and treatment. In this review, we will discuss the possible road map for implementation of cancer precision medicine into the clinical practice by mainly focusing on the role of liquid biopsy, particularly circulating tumor DNA, as a potential tool for cancer screening, selection of an appropriate drug(s), surveillance of minimal residual diseases, and early detection of recurrence. We will also review the current status of genome-driven oncology and emerging field of immunotherapies that could be provided to patients to improve their clinical outcome and quality of life. Lastly, we will discuss the usefulness of artificial intelligence that facilitate complex data integration in our health care/medical care system.
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Affiliation(s)
- Siew-Kee Low
- Project for Immunogenomics, Cancer Precision Medicine Center, Japanese Foundation for Cancer Research, Tokyo, Japan
| | - Yusuke Nakamura
- Project for Immunogenomics, Cancer Precision Medicine Center, Japanese Foundation for Cancer Research, Tokyo, Japan
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Yan CQ, Zhou P, Wang X, Tu JF, Hu SQ, Huo JW, Wang ZY, Shi GX, Zhang YN, Li JQ, Wang J, Liu CZ. Efficacy and neural mechanism of acupuncture treatment in older adults with subjective cognitive decline: study protocol for a randomised controlled clinical trial. BMJ Open 2019; 9:e028317. [PMID: 31601583 PMCID: PMC6797373 DOI: 10.1136/bmjopen-2018-028317] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/31/2022] Open
Abstract
INTRODUCTION Subjective cognitive decline (SCD) refers to individuals' perceived decline in memory and/or other cognitive abilities relative to their previous level of performance, while objective neuropsychological deficits are not observed. SCD may represent a preclinical phase of Alzheimer's disease. At this very early stage of decline, intervention could slow the rate of incipient decline to prolong and preserve cognitive and functional abilities. However, there is no effective treatment recommended for individuals with SCD. Acupuncture, as a non-pharmacological intervention, has been widely employed for patients with cognitive disorders. METHODS AND ANALYSIS The proposed study is a randomised, assessor-blinded and placebo-controlled study that investigates the efficacy and mechanism of acupuncture in SCD. Sixty patients with SCD will be randomly allocated either into an acupuncture group or a sham acupuncture group. They will receive 24 sessions of real acupuncture treatment or identical treatment sessions using a placebo needle. Global cognitive changes based on a multidomain neuropsychological test battery will be evaluated to detect the clinical efficacy of acupuncture treatment at baseline and end of treatment. MRI scans will be used to explore acupuncture-related neuroplasticity changes. Correlation analyses will be performed to investigate the relationships between the changes in brain function and symptom improvement. ETHICS AND DISSEMINATION The trial was approved by the research ethics committee. The results of the study will be published in a peer-reviewed academic journal and will also be disseminated electronically through conference presentations. TRIAL REGISTRATION NUMBER NCT03444896.
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Affiliation(s)
- Chao-Qun Yan
- School of Acupuncture-Moxibustion and Tuina, Beijing University of Chinese Medicine, Beijing, China
- Department of Acupuncture and moxibustion, Dongzhimen Hospital Affiliated to Beijing University of Chinese Medicine, Beijing, China
| | - Ping Zhou
- School of Acupuncture-Moxibustion and Tuina, Beijing University of Chinese Medicine, Beijing, China
| | - Xu Wang
- School of Life Sciences, Beijing University of Chinese Medicine, Beijing, China
| | - Jian Feng Tu
- School of Acupuncture-Moxibustion and Tuina, Beijing University of Chinese Medicine, Beijing, China
| | - Shang-Qing Hu
- School of Acupuncture-Moxibustion and Tuina, Beijing University of Chinese Medicine, Beijing, China
| | - Jian-Wei Huo
- Beijing Hospital of Traditional Chinese Medicine, Capital Medical University, Beijing, China
| | - Zhong-Yan Wang
- Beijing Hospital of Traditional Chinese Medicine, Capital Medical University, Beijing, China
| | - Guang-Xia Shi
- School of Acupuncture-Moxibustion and Tuina, Beijing University of Chinese Medicine, Beijing, China
| | - Ya-Nan Zhang
- Beijing Hospital of Traditional Chinese Medicine, Capital Medical University, Beijing, China
| | - Jun-Qiu Li
- Beijing Hospital of Traditional Chinese Medicine, Capital Medical University, Beijing, China
| | - Jun Wang
- Department of Acupuncture and moxibustion, Dongzhimen Hospital Affiliated to Beijing University of Chinese Medicine, Beijing, China
| | - Cun-Zhi Liu
- School of Acupuncture-Moxibustion and Tuina, Beijing University of Chinese Medicine, Beijing, China
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Albright J. Forecasting the progression of Alzheimer's disease using neural networks and a novel preprocessing algorithm. ALZHEIMER'S & DEMENTIA (NEW YORK, N. Y.) 2019; 5:483-491. [PMID: 31650004 PMCID: PMC6804703 DOI: 10.1016/j.trci.2019.07.001] [Citation(s) in RCA: 28] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
INTRODUCTION There is a 99.6% failure rate of clinical trials for drugs to treat Alzheimer's disease, likely because Alzheimer's disease (AD) patients cannot be easily identified at early stages. This study investigated machine learning approaches to use clinical data to predict the progression of AD in future years. METHODS Data from 1737 patients were processed using the "All-Pairs" technique, a novel methodology created for this study involving the comparison of all possible pairs of temporal data points for each patient. Machine learning models were trained on these processed data and evaluated using a separate testing data set (110 patients). RESULTS A neural network model was effective (mAUC = 0.866) at predicting the progression of AD, both in patients who were initially cognitively normal and in patients suffering from mild cognitive impairment. DISCUSSION Such a model could be used to identify patients at early stages of AD and who are therefore good candidates for clinical trials for AD therapeutics.
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Affiliation(s)
- Jack Albright
- Corresponding author. Tel.: (650) 434-3518; Fax: (650) 471-6048.
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Grassi M, Rouleaux N, Caldirola D, Loewenstein D, Schruers K, Perna G, Dumontier M. A Novel Ensemble-Based Machine Learning Algorithm to Predict the Conversion From Mild Cognitive Impairment to Alzheimer's Disease Using Socio-Demographic Characteristics, Clinical Information, and Neuropsychological Measures. Front Neurol 2019; 10:756. [PMID: 31379711 PMCID: PMC6646724 DOI: 10.3389/fneur.2019.00756] [Citation(s) in RCA: 36] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2019] [Accepted: 07/01/2019] [Indexed: 01/18/2023] Open
Abstract
Background: Despite the increasing availability in brain health related data, clinically translatable methods to predict the conversion from Mild Cognitive Impairment (MCI) to Alzheimer's disease (AD) are still lacking. Although MCI typically precedes AD, only a fraction of 20-40% of MCI individuals will progress to dementia within 3 years following the initial diagnosis. As currently available and emerging therapies likely have the greatest impact when provided at the earliest disease stage, the prompt identification of subjects at high risk for conversion to AD is of great importance in the fight against this disease. In this work, we propose a highly predictive machine learning algorithm, based only on non-invasively and easily in-the-clinic collectable predictors, to identify MCI subjects at risk for conversion to AD. Methods: The algorithm was developed using the open dataset from the Alzheimer's Disease Neuroimaging Initiative (ADNI), employing a sample of 550 MCI subjects whose diagnostic follow-up is available for at least 3 years after the baseline assessment. A restricted set of information regarding sociodemographic and clinical characteristics, neuropsychological test scores was used as predictors and several different supervised machine learning algorithms were developed and ensembled in final algorithm. A site-independent stratified train/test split protocol was used to provide an estimate of the generalized performance of the algorithm. Results: The final algorithm demonstrated an AUROC of 0.88, sensitivity of 77.7%, and a specificity of 79.9% on excluded test data. The specificity of the algorithm was 40.2% for 100% sensitivity. Conclusions: The algorithm we developed achieved sound and high prognostic performance to predict AD conversion using easily clinically derived information that makes the algorithm easy to be translated into practice. This indicates beneficial application to improve recruitment in clinical trials and to more selectively prescribe new and newly emerging early interventions to high AD risk patients.
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Affiliation(s)
- Massimiliano Grassi
- Department of Clinical Neurosciences, Hermanas Hospitalarias, Villa San Benedetto Menni Hospital, FoRiPsi, Albese con Cassano, Italy
- Department of Biomedical Sciences, Humanitas University, Milan, Italy
| | - Nadine Rouleaux
- Faculty of Science and Engineering, Institute of Data Science, Maastricht University, Maastricht, Netherlands
| | - Daniela Caldirola
- Department of Clinical Neurosciences, Hermanas Hospitalarias, Villa San Benedetto Menni Hospital, FoRiPsi, Albese con Cassano, Italy
- Department of Biomedical Sciences, Humanitas University, Milan, Italy
| | - David Loewenstein
- Department of Psychiatry and Behavioral Sciences, Miller School of Medicine, University of Miami, Miami, FL, United States
- Wien Center for Alzheimer's Disease and Memory Disorders, Mount Sinai Medical Center Miami Beach, Miami Beach, FL, United States
- Center for Cognitive Neuroscience and Aging, Miller School of Medicine, University of Miami, Miami, FL, United States
| | - Koen Schruers
- Research Institute of Mental Health and Neuroscience and Department of Psychiatry and Neuropsychology, Faculty of Health, Medicine and Life Sciences, Maastricht University, Maastricht, Netherlands
| | - Giampaolo Perna
- Department of Clinical Neurosciences, Hermanas Hospitalarias, Villa San Benedetto Menni Hospital, FoRiPsi, Albese con Cassano, Italy
- Department of Biomedical Sciences, Humanitas University, Milan, Italy
- Department of Psychiatry and Behavioral Sciences, Miller School of Medicine, University of Miami, Miami, FL, United States
- Research Institute of Mental Health and Neuroscience and Department of Psychiatry and Neuropsychology, Faculty of Health, Medicine and Life Sciences, Maastricht University, Maastricht, Netherlands
| | - Michel Dumontier
- Faculty of Science and Engineering, Institute of Data Science, Maastricht University, Maastricht, Netherlands
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Astell AJ, Bouranis N, Hoey J, Lindauer A, Mihailidis A, Nugent C, Robillard JM. Technology and Dementia: The Future is Now. Dement Geriatr Cogn Disord 2019; 47:131-139. [PMID: 31247624 PMCID: PMC6643496 DOI: 10.1159/000497800] [Citation(s) in RCA: 68] [Impact Index Per Article: 13.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/16/2023] Open
Abstract
BACKGROUND Technology has multiple potential applications to dementia from diagnosis and assessment to care delivery and supporting ageing in place. OBJECTIVES To summarise key areas of technology development in dementia and identify future directions and implications. METHOD Members of the US Alzheimer's Association Technology Professional Interest Area involved in delivering the annual pre-conference summarised existing knowledge on current and future technology developments in dementia. RESULTS The main domains of technology development are as follows: (i) diagnosis, assessment and monitoring, (ii) maintenance of functioning, (iii) leisure and activity, (iv) caregiving and management. CONCLUSIONS The pace of technology development requires urgent policy, funding and practice change, away from a narrow medical approach, to a holistic model that facilitates future risk reduction and prevention strategies, enables earlier detection and supports implementation at scale for a meaningful and fulfilling life with dementia.
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Affiliation(s)
- Arlene J. Astell
- Department of Occupational Sciences and Occupational Therapy, University of Toronto, Toronto, Ontario, Canada,Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada,Toronto Rehabilitation Institute, Toronto, Toronto, Ontario, Canada,School of Psychology and Clinical Language Sciences, University of Reading, Reading, United Kingdom,*Arlene J. Astell, School of Psychology & Clinical Language Sciences, University of Reading, Reading (UK), E-Mail
| | - Nicole Bouranis
- Layton Aging and Alzheimer's Disease Center, Oregon Health and Science University, Portland, Oregon, USA
| | - Jesse Hoey
- David R. Cheriton School of Computer Science, University of Waterloo, Waterloo, Ontario, Canada
| | - Allison Lindauer
- Oregon Roybal Center for Aging and Technology (ORCATECH), Oregon Health and Science University, Portland, Oregon, USA
| | - Alex Mihailidis
- Department of Occupational Sciences and Occupational Therapy, University of Toronto, Toronto, Ontario, Canada
| | - Chris Nugent
- School of Computing, Ulster University, Northern Ireland, United Kingdom
| | - Julie M. Robillard
- Faculty of Medicine, University of British Columbia, Vancouver, British Columbia, Canada
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A clinically-translatable machine learning algorithm for the prediction of Alzheimer's disease conversion: further evidence of its accuracy via a transfer learning approach. Int Psychogeriatr 2019; 31:937-945. [PMID: 30426918 PMCID: PMC6517088 DOI: 10.1017/s1041610218001618] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
Abstract
BACKGROUND In a previous study, we developed a highly performant and clinically-translatable machine learning algorithm for a prediction of three-year conversion to Alzheimer's disease (AD) in subjects with Mild Cognitive Impairment (MCI) and Pre-mild Cognitive Impairment. Further tests are necessary to demonstrate its accuracy when applied to subjects not used in the original training process. In this study, we aimed to provide preliminary evidence of this via a transfer learning approach. METHODS We initially employed the same baseline information (i.e. clinical and neuropsychological test scores, cardiovascular risk indexes, and a visual rating scale for brain atrophy) and the same machine learning technique (support vector machine with radial-basis function kernel) used in our previous study to retrain the algorithm to discriminate between participants with AD (n = 75) and normal cognition (n = 197). Then, the algorithm was applied to perform the original task of predicting the three-year conversion to AD in the sample of 61 MCI subjects that we used in the previous study. RESULTS Even after the retraining, the algorithm demonstrated a significant predictive performance in the MCI sample (AUC = 0.821, 95% CI bootstrap = 0.705-0.912, best balanced accuracy = 0.779, sensitivity = 0.852, specificity = 0.706). CONCLUSIONS These results provide a first indirect evidence that our original algorithm can also perform relevant generalized predictions when applied to new MCI individuals. This motivates future efforts to bring the algorithm to sufficient levels of optimization and trustworthiness that will allow its application in both clinical and research settings.
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Tran BX, McIntyre RS, Latkin CA, Phan HT, Vu GT, Nguyen HLT, Gwee KK, Ho CSH, Ho RCM. The Current Research Landscape on the Artificial Intelligence Application in the Management of Depressive Disorders: A Bibliometric Analysis. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2019; 16:ijerph16122150. [PMID: 31216619 PMCID: PMC6617113 DOI: 10.3390/ijerph16122150] [Citation(s) in RCA: 39] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/07/2019] [Revised: 06/07/2019] [Accepted: 06/11/2019] [Indexed: 12/22/2022]
Abstract
Artificial intelligence (AI)-based techniques have been widely applied in depression research and treatment. Nonetheless, there is currently no systematic review or bibliometric analysis in the medical literature about the applications of AI in depression. We performed a bibliometric analysis of the current research landscape, which objectively evaluates the productivity of global researchers or institutions in this field, along with exploratory factor analysis (EFA) and latent dirichlet allocation (LDA). From 2010 onwards, the total number of papers and citations on using AI to manage depressive disorder have risen considerably. In terms of global AI research network, researchers from the United States were the major contributors to this field. Exploratory factor analysis showed that the most well-studied application of AI was the utilization of machine learning to identify clinical characteristics in depression, which accounted for more than 60% of all publications. Latent dirichlet allocation identified specific research themes, which include diagnosis accuracy, structural imaging techniques, gene testing, drug development, pattern recognition, and electroencephalography (EEG)-based diagnosis. Although the rapid development and widespread use of AI provide various benefits for both health providers and patients, interventions to enhance privacy and confidentiality issues are still limited and require further research.
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Affiliation(s)
- Bach Xuan Tran
- Institute for Preventive Medicine and Public Health, Hanoi Medical University, Hanoi 100000, Vietnam.
- Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD 21218, USA.
| | - Roger S McIntyre
- Institute of Medical Science, University of Toronto, Toronto, ON M5S 1A8, Canada.
- Mood Disorders Psychopharmacology Unit, University Health Network, Toronto, ON M5G 2C4, Canada.
- Department of Psychiatry, University of Toronto, Toronto, ON M5T 1R8, Canada.
- Department of Toxicology and Pharmacology, University of Toronto, Toronto, ON M5S 1A8, Canada.
| | - Carl A Latkin
- Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD 21218, USA.
| | - Hai Thanh Phan
- Institute for Global Health Innovations, Duy Tan University, Da Nang 550000, Vietnam.
| | - Giang Thu Vu
- Center of Excellence in Evidence-based Medicine, Nguyen Tat Thanh University, Ho Chi Minh City 700000, Vietnam.
| | - Huong Lan Thi Nguyen
- Institute for Global Health Innovations, Duy Tan University, Da Nang 550000, Vietnam.
| | - Kenneth K Gwee
- Department of Psychological Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 119228, Singapore.
| | - Cyrus S H Ho
- Department of Psychological Medicine, National University Hospital, Singapore 119074, Singapore.
| | - Roger C M Ho
- Center of Excellence in Evidence-based Medicine, Nguyen Tat Thanh University, Ho Chi Minh City 700000, Vietnam.
- Department of Psychological Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 119228, Singapore.
- Institute for Health Innovation and Technology (iHealthtech), National University of Singapore, Singapore 119077, Singapore.
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Gauthier S, Ng KP, Pascoal TA, Zhang H, Rosa-Neto P. Targeting Alzheimer's Disease at the Right Time and the Right Place: Validation of a Personalized Approach to Diagnosis and Treatment. J Alzheimers Dis 2019; 64:S23-S31. [PMID: 29504543 PMCID: PMC6004905 DOI: 10.3233/jad-179924] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Cautious optimism is appropriate for a near future (five years) time frame for a number of drugs acting on the different pathophysiological components of Alzheimer’s disease (amyloid deposition, tau hyperphosphorylation, neuroinflammation, vascular changes, to name the most important known so far). Since the relative weight of these components will be different between individuals and will even change over time for each individual, a ‘one drug fit for all’ approach is no longer defensible. Precision medicine using biomarkers in the diagnosis and treatment of Alzheimer’s disease is the new strategy.
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Affiliation(s)
- Serge Gauthier
- McGill Center for Studies in Aging, Douglas Mental Health Research Institute, Montreal, Canada
| | - Kok Pin Ng
- Department of Neurology, National Neuroscience Institute, Singapore
| | - Tharick A Pascoal
- McGill Center for Studies in Aging, Douglas Mental Health Research Institute, Montreal, Canada
| | - Hua Zhang
- Department of Neurology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Pedro Rosa-Neto
- McGill Center for Studies in Aging, Douglas Mental Health Research Institute, Montreal, Canada
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Tam A, Dansereau C, Iturria-Medina Y, Urchs S, Orban P, Sharmarke H, Breitner J, Bellec P. A highly predictive signature of cognition and brain atrophy for progression to Alzheimer's dementia. Gigascience 2019; 8:giz055. [PMID: 31077314 PMCID: PMC6511068 DOI: 10.1093/gigascience/giz055] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2018] [Revised: 03/07/2019] [Accepted: 04/21/2019] [Indexed: 12/14/2022] Open
Abstract
BACKGROUND Clinical trials in Alzheimer's disease need to enroll patients whose cognition will decline over time, if left untreated, in order to demonstrate the efficacy of an intervention. Machine learning models used to screen for patients at risk of progression to dementia should therefore favor specificity (detecting only progressors) over sensitivity (detecting all progressors), especially when the prevalence of progressors is low. Here, we explore whether such high-risk patients can be identified using cognitive assessments and structural neuroimaging by training machine learning tools in a high-specificity regime. RESULTS A multimodal signature of Alzheimer's dementia was first extracted from the ADNI1 dataset. We then validated the predictive value of this signature on ADNI1 patients with mild cognitive impairment (N = 235). The signature was optimized to predict progression to dementia over 3 years with low sensitivity (55.1%) but high specificity (95.6%), resulting in only moderate accuracy (69.3%) but high positive predictive value (80.4%, adjusted for a "typical" 33% prevalence rate of true progressors). These results were replicated in ADNI2 (N = 235), with 87.8% adjusted positive predictive value (96.7% specificity, 47.3% sensitivity, 85.1% accuracy). CONCLUSIONS We found that cognitive measures alone could identify high-risk individuals, with structural measurements providing a slight improvement. The signature had comparable receiver operating characteristics to standard machine learning tools, yet a marked improvement in positive predictive value was achieved over the literature by selecting a high-specificity operating point. The multimodal signature can be readily applied for the enrichment of clinical trials.
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Affiliation(s)
- Angela Tam
- Centre de Recherche de l'Institut Universitaire de Gériatrie de Montréal, 4545 chemin Queen-Mary, Montréal, QC, H3W 1W4, Canada
- Centre for the Studies on Prevention of Alzheimer's Disease, Douglas Mental Health University Institute Research Centre, 6875 Lasalle Boulevard, Montréal, QC, H4H 1R3, Canada
| | - Christian Dansereau
- Centre de Recherche de l'Institut Universitaire de Gériatrie de Montréal, 4545 chemin Queen-Mary, Montréal, QC, H3W 1W4, Canada
- Département d'informatique et de recherche opérationnelle, Université de Montréal, 2920 chemin de la Tour, Montréal, QC, H3T 1J4, Canada
| | - Yasser Iturria-Medina
- Montreal Neurological Institute, McGill University, 3801 University Street, Montréal, QC, H3A 2B4, Canada
| | - Sebastian Urchs
- Centre de Recherche de l'Institut Universitaire de Gériatrie de Montréal, 4545 chemin Queen-Mary, Montréal, QC, H3W 1W4, Canada
- Montreal Neurological Institute, McGill University, 3801 University Street, Montréal, QC, H3A 2B4, Canada
| | - Pierre Orban
- Centre de Recherche de l'Institut Universitaire de Gériatrie de Montréal, 4545 chemin Queen-Mary, Montréal, QC, H3W 1W4, Canada
- Centre de Recherche de l'Institut Universitaire en Santé Mentale de Montréal, 7331 rue Hochelaga, Montréal, QC, H1N 3V2, Canada
- Département de psychiatrie, Université de Montréal, 2900 boulevard Édouard-Montpetit, Montréal, QC, H3T 1J4, Canada
| | - Hanad Sharmarke
- Centre de Recherche de l'Institut Universitaire de Gériatrie de Montréal, 4545 chemin Queen-Mary, Montréal, QC, H3W 1W4, Canada
| | - John Breitner
- Centre for the Studies on Prevention of Alzheimer's Disease, Douglas Mental Health University Institute Research Centre, 6875 Lasalle Boulevard, Montréal, QC, H4H 1R3, Canada
- Department of Psychiatry, McGill University, 1033 Pine Avenue West, Montréal, QC, H3A 1A1, Canada
| | - Pierre Bellec
- Centre de Recherche de l'Institut Universitaire de Gériatrie de Montréal, 4545 chemin Queen-Mary, Montréal, QC, H3W 1W4, Canada
- Département de psychologie, Université de Montréal, 90 avenue Vincent d'Indy, Montréal, QC, H3C 3J7, Canada
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Saber H, Somai M, Rajah GB, Scalzo F, Liebeskind DS. Predictive analytics and machine learning in stroke and neurovascular medicine. Neurol Res 2019; 41:681-690. [PMID: 31038007 DOI: 10.1080/01616412.2019.1609159] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
Abstract
Advances in predictive analytics and machine learning supported by an ever-increasing wealth of data and processing power are transforming almost every industry. Accuracy and precision of predictive analytics have significantly increased over the past few years and are evolving at an exponential pace. There have been significant breakthroughs in using Predictive Analytics in healthcare where it is held as the foundation of precision medicine. Yet, although the research in the field is expanding with the profuse volume of papers applying machine learning algorithms to medical data, very few have contributed meaningfully to clinical care. This lack of impact stands in stark contrast to the enormous relevance of machine learning to many other industries. Regardless of the status of its current contribution, the field of predictive analytics is expected to fundamentally change the way we diagnose and treat diseases, as well as the conduct of biomedical science research. In this review, we describe the main tools and techniques in predictive analytics and will analyze the trends in application of these techniques over the recent years. We will also provide examples of its application in medicine and more specifically in stroke and neurovascular research and outline current limitations.
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Affiliation(s)
- Hamidreza Saber
- a Wayne State Department of Neurology, Wayne State University , Detroit , MI , USA
| | - Melek Somai
- b Neuro-Epidemiology and Ageing Research Unit, School of Public Health, Imperial College London , London , UK
| | - Gary B Rajah
- c Wayne State Department of Neurosurgery, Wayne State University , Detroit , MI , USA
| | - Fabien Scalzo
- d Departement of Neurology, University of California Los Angeles , Los Angeles , CA , USA
| | - David S Liebeskind
- d Departement of Neurology, University of California Los Angeles , Los Angeles , CA , USA
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Li Y, Jiang J, Lu J, Jiang J, Zhang H, Zuo C. Radiomics: a novel feature extraction method for brain neuron degeneration disease using 18F-FDG PET imaging and its implementation for Alzheimer's disease and mild cognitive impairment. Ther Adv Neurol Disord 2019; 12:1756286419838682. [PMID: 30956687 PMCID: PMC6444412 DOI: 10.1177/1756286419838682] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2018] [Accepted: 02/25/2019] [Indexed: 01/13/2023] Open
Abstract
Background: Alzheimer’s disease (AD) is the most common form of progressive and irreversible dementia, and accurate diagnosis of AD at its prodromal stage is clinically important. Currently, computer-aided diagnosis of AD and mild cognitive impairment (MCI) using 18F-fluorodeoxy-glucose positron emission tomography (18F-FDG PET) imaging is usually based on low-level imaging features or deep learning methods, which have difficulties in achieving sufficient classification accuracy or lack clinical significance. This research therefore aimed to implement a new feature extraction method known as radiomics, to improve the classification accuracy and discover high-order features that can reveal pathological information. Methods: In this study, 18F-FDG PET and clinical assessments were collected in a cohort of 422 individuals [including 130 with AD, 130 with MCI, and 162 healthy controls (HCs)] from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) and 44 individuals (including 22 with AD, and 22 HCs) from Huashan Hospital, Shanghai, China. First, we performed a group comparison using a two-sample Student’s t test to determine the regions of interest (ROIs) based on 30 AD patients and 30 HCs from ADNI cohorts. Second, based on two time scans of 32 HCs from ADNI cohorts, we used Cronbach’s alpha coefficient for radiomic feature stability analyses. Pearson’s correlation coefficients were regarded as a feature selection criterion, to select effective features associated with the clinical cognitive scale [clinical dementia rating scale in its sum of boxes (CDRSB); Alzheimer’s disease assessment scale (ADAS)] with 500-times cross-validation. Finally, a support vector machine (SVM) was used to test the ability of the radiomic features to classify HCs, MCI and AD patients. Results: As a result, we identified brain regions which were mainly distributed in the temporal, occipital and frontal areas as ROIs. A total of 168 radiomic features of AD were stable (alpha > 0.8). The classification experiment led to maximal accuracies of 91.5%, 83.1% and 85.9% for classifying AD versus HC, MCI versus HCs and AD versus MCI. Conclusion: The research in this paper proved that the novel approach based on high-order radiomic features extracted from 18F-FDG PET brain images that can be used for AD and MCI computer-aided diagnosis.
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Affiliation(s)
- Yupeng Li
- Shanghai Institute for Advanced Communication and Data Science, Shanghai University, Shanghai, China
| | - Jiehui Jiang
- Shanghai Institute for Advanced Communication and Data Science, Shanghai University, T Building, 99 ShangDa Road, BaoShan District, Shanghai, 200444, China
| | - Jiaying Lu
- PET Center, Huashan Hospital, Fudan University, Shanghai, China
| | - Juanjuan Jiang
- Shanghai Institute for Advanced Communication and Data Science, Shanghai University, Shanghai, China
| | - Huiwei Zhang
- PET Center, Huashan Hospital, Fudan University, Shanghai, China
| | - Chuantao Zuo
- PET Center, Huashan Hospital, Fudan University, Shanghai, China
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Tsang G, Xie X, Zhou SM. Harnessing the Power of Machine Learning in Dementia Informatics Research: Issues, Opportunities, and Challenges. IEEE Rev Biomed Eng 2019; 13:113-129. [PMID: 30872241 DOI: 10.1109/rbme.2019.2904488] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
Dementia is a chronic and degenerative condition affecting millions globally. The care of patients with dementia presents an ever-continuing challenge to healthcare systems in the 21st century. Medical and health sciences have generated unprecedented volumes of data related to health and wellbeing for patients with dementia due to advances in information technology, such as genetics, neuroimaging, cognitive assessment, free texts, routine electronic health records, etc. Making the best use of these diverse and strategic resources will lead to high-quality care of patients with dementia. As such, machine learning becomes a crucial factor in achieving this objective. The aim of this paper is to provide a state-of-the-art review of machine learning methods applied to health informatics for dementia care. We collate and review the existing scientific methodologies and identify the relevant issues and challenges when faced with big health data. Machine learning has demonstrated promising applications to neuroimaging data analysis for dementia care, while relatively less effort has been made to make use of integrated heterogeneous data via advanced machine learning approaches. We further indicate future potential and research directions in applying advanced machine learning, such as deep learning, to dementia informatics.
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