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Sun Y, Bi Q, Wang X, Hu X, Li H, Li X, Ma T, Lu J, Chan P, Shu N, Han Y. Prediction of Conversion From Amnestic Mild Cognitive Impairment to Alzheimer's Disease Based on the Brain Structural Connectome. Front Neurol 2019; 9:1178. [PMID: 30687226 PMCID: PMC6335339 DOI: 10.3389/fneur.2018.01178] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2018] [Accepted: 12/20/2018] [Indexed: 12/12/2022] Open
Abstract
Background: Early prediction of disease progression in patients with amnestic mild cognitive impairment (aMCI) is important for early diagnosis and intervention of Alzheimer's disease (AD). Previous brain network studies have suggested topological disruptions of the brain connectome in aMCI patients. However, whether brain connectome markers at baseline can predict longitudinal conversion from aMCI to AD remains largely unknown. Methods: In this study, 52 patients with aMCI and 26 demographically matched healthy controls from a longitudinal cohort were evaluated. During 2 years of follow-up, 26 patients with aMCI were retrospectively classified as aMCI converters and 26 patients remained stable as aMCI non-converters based on whether they were subsequently diagnosed with AD. For each participant, diffusion tensor imaging at baseline and deterministic tractography were used to map the whole-brain white matter structural connectome. Graph theoretical analysis was applied to investigate the convergent and divergent connectivity patterns of structural connectome between aMCI converters and non-converters. Results: Disrupted topological organization of the brain structural connectome were identified in both aMCI converters and non-converters. More severe disruptions of structural connectivity in aMCI converters compared with non-converters were found, especially in the default-mode network regions and connections. Finally, a support vector machine-based classification demonstrated the good discriminative ability of structural connectivity in differentiating aMCI patients from controls with an accuracy of 98%, and in discriminating converters from non-converters with an accuracy of 81%. Conclusion: Our study provides potential structural connectome/connectivity-based biomarkers for predicting disease progression in aMCI, which is important for the early diagnosis of AD.
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Affiliation(s)
- Yu Sun
- Department of Neurology, XuanWu Hospital of Capital Medical University, Beijing, China
| | - Qiuhui Bi
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China.,Center for Collaboration and Innovation in Brain and Learning Sciences, Beijing Normal University, Beijing, China.,Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
| | - Xiaoni Wang
- Department of Neurology, XuanWu Hospital of Capital Medical University, Beijing, China
| | - Xiaochen Hu
- Department of Psychiatry and Psychotherapy, Medical Faculty, University of Cologne, Cologne, Germany
| | - Huijie Li
- Department of Psychology, University of Chinese Academy of Sciences, Beijing, China.,CAS Key Laboratory of Behavioral Science, Institute of Psychology, Beijing, China
| | - Xiaobo Li
- Department of Biomedical Engineering, New Jersey Institute of Technology, Newark, NJ, United States
| | - Ting Ma
- Department of Electronic and Information Engineering, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, China
| | - Jie Lu
- Department of Radiology, XuanWu Hospital of Capital Medical University, Beijing, China
| | - Piu Chan
- Department of Neurology, XuanWu Hospital of Capital Medical University, Beijing, China.,Beijing Institute of Geriatrics, XuanWu Hospital of Capital Medical University, Beijing, China.,National Clinical Research Center for Geriatric Disorders, Beijing, China
| | - Ni Shu
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China.,Center for Collaboration and Innovation in Brain and Learning Sciences, Beijing Normal University, Beijing, China.,Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
| | - Ying Han
- Department of Neurology, XuanWu Hospital of Capital Medical University, Beijing, China.,Beijing Institute of Geriatrics, XuanWu Hospital of Capital Medical University, Beijing, China.,National Clinical Research Center for Geriatric Disorders, Beijing, China.,Center of Alzheimer's Disease, Beijing Institute for Brain Disorders, Beijing, China
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52
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Topol EJ. High-performance medicine: the convergence of human and artificial intelligence. Nat Med 2019; 25:44-56. [PMID: 30617339 DOI: 10.1038/s41591-018-0300-7] [Citation(s) in RCA: 2121] [Impact Index Per Article: 424.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2018] [Accepted: 11/12/2018] [Indexed: 11/08/2022]
Abstract
The use of artificial intelligence, and the deep-learning subtype in particular, has been enabled by the use of labeled big data, along with markedly enhanced computing power and cloud storage, across all sectors. In medicine, this is beginning to have an impact at three levels: for clinicians, predominantly via rapid, accurate image interpretation; for health systems, by improving workflow and the potential for reducing medical errors; and for patients, by enabling them to process their own data to promote health. The current limitations, including bias, privacy and security, and lack of transparency, along with the future directions of these applications will be discussed in this article. Over time, marked improvements in accuracy, productivity, and workflow will likely be actualized, but whether that will be used to improve the patient-doctor relationship or facilitate its erosion remains to be seen.
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Affiliation(s)
- Eric J Topol
- Department of Molecular Medicine, Scripps Research, La Jolla, CA, USA.
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53
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Loh E. Medicine and the rise of the robots: a qualitative review of recent advances of artificial intelligence in health. BMJ LEADER 2018. [DOI: 10.1136/leader-2018-000071] [Citation(s) in RCA: 62] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
Abstract
Artificial intelligence (AI) has the potential to significantly transform the role of the doctor and revolutionise the practice of medicine. This qualitative review paper summarises the past 12 months of health research in AI, across different medical specialties, and discusses the current strengths as well as challenges, relating to this emerging technology. Doctors, especially those in leadership roles, need to be aware of how quickly AI is advancing in health, so that they are ready to lead the change required for its adoption by the health system. Key points: ‘AI has now been shown to be as effective as humans in the diagnosis of various medical conditions, and in some cases, more effective.’ When it comes to predicting suicide attempts, recent research suggest AI is better than human beings. ‘AI’s current strength is in its ability to learn from a large dataset and recognise patterns that can be used to diagnose conditions, putting it in direct competition with medical specialties that are involved in diagnostic tests that involve pattern recognition, such as pathology and radiology’. The current challenges in AI include legal liability and attribution of negligence when errors occur, and the ethical issues relating to patient choices. ‘AI systems can also be developed with, or learn, biases, that will need to be identified and mitigated’. As doctors and health leaders, we need to start preparing the profession to be supported by, partnered with, and, in future, potentially be replaced by, AI and advanced robotics systems.
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Vogel JW, Vachon-Presseau E, Pichet Binette A, Tam A, Orban P, La Joie R, Savard M, Picard C, Poirier J, Bellec P, Breitner JCS, Villeneuve S. Brain properties predict proximity to symptom onset in sporadic Alzheimer's disease. Brain 2018; 141:1871-1883. [PMID: 29688388 PMCID: PMC5972641 DOI: 10.1093/brain/awy093] [Citation(s) in RCA: 40] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2017] [Revised: 02/20/2018] [Accepted: 02/25/2018] [Indexed: 11/16/2022] Open
Abstract
See Tijms and Visser (doi:10.1093/brain/awy113) for a scientific commentary on this article.Alzheimer's disease is preceded by a lengthy 'preclinical' stage spanning many years, during which subtle brain changes occur in the absence of overt cognitive symptoms. Predicting when the onset of disease symptoms will occur is an unsolved challenge in individuals with sporadic Alzheimer's disease. In individuals with autosomal dominant genetic Alzheimer's disease, the age of symptom onset is similar across generations, allowing the prediction of individual onset times with some accuracy. We extend this concept to persons with a parental history of sporadic Alzheimer's disease to test whether an individual's symptom onset age can be informed by the onset age of their affected parent, and whether this estimated onset age can be predicted using only MRI. Structural and functional MRIs were acquired from 255 ageing cognitively healthy subjects with a parental history of sporadic Alzheimer's disease from the PREVENT-AD cohort. Years to estimated symptom onset was calculated as participant age minus age of parental symptom onset. Grey matter volume was extracted from T1-weighted images and whole-brain resting state functional connectivity was evaluated using degree count. Both modalities were summarized using a 444-region cortical-subcortical atlas. The entire sample was divided into training (n = 138) and testing (n = 68) sets. Within the training set, individuals closer to or beyond their parent's symptom onset demonstrated reduced grey matter volume and altered functional connectivity, specifically in regions known to be vulnerable in Alzheimer's disease. Machine learning was used to identify a weighted set of imaging features trained to predict years to estimated symptom onset. This feature set alone significantly predicted years to estimated symptom onset in the unseen testing data. This model, using only neuroimaging features, significantly outperformed a similar model instead trained with cognitive, genetic, imaging and demographic features used in a traditional clinical setting. We next tested if these brain properties could be generalized to predict time to clinical progression in a subgroup of 26 individuals from the Alzheimer's Disease Neuroimaging Initiative, who eventually converted either to mild cognitive impairment or to Alzheimer's dementia. The feature set trained on years to estimated symptom onset in the PREVENT-AD predicted variance in time to clinical conversion in this separate longitudinal dataset. Adjusting for participant age did not impact any of the results. These findings demonstrate that years to estimated symptom onset or similar measures can be predicted from brain features and may help estimate presymptomatic disease progression in at-risk individuals.
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Affiliation(s)
- Jacob W Vogel
- Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
- Centre for the Studies on Prevention of Alzheimer’s Disease, Douglas Mental Health University Institute Research Centre, Montreal, Quebec, Canada
| | | | - Alexa Pichet Binette
- Centre for the Studies on Prevention of Alzheimer’s Disease, Douglas Mental Health University Institute Research Centre, Montreal, Quebec, Canada
| | - Angela Tam
- Centre for the Studies on Prevention of Alzheimer’s Disease, Douglas Mental Health University Institute Research Centre, Montreal, Quebec, Canada
- Centre de recherche de l'Institut universitaire de gériatrie de Montréal, Montreal, Quebec, Canada
| | - Pierre Orban
- Centre for the Studies on Prevention of Alzheimer’s Disease, Douglas Mental Health University Institute Research Centre, Montreal, Quebec, Canada
- Centre de recherche de l'Institut universitaire de gériatrie de Montréal, Montreal, Quebec, Canada
- Department of Psychiatry, University of Montreal, Montreal, Quebec, Canada
| | - Renaud La Joie
- Memory and Aging Center, University of California, San Francisco, California, USA
| | - Mélissa Savard
- Centre for the Studies on Prevention of Alzheimer’s Disease, Douglas Mental Health University Institute Research Centre, Montreal, Quebec, Canada
| | - Cynthia Picard
- Centre for the Studies on Prevention of Alzheimer’s Disease, Douglas Mental Health University Institute Research Centre, Montreal, Quebec, Canada
- Department of Psychiatry, McGill University, Montreal, Quebec, Canada
| | - Judes Poirier
- Centre for the Studies on Prevention of Alzheimer’s Disease, Douglas Mental Health University Institute Research Centre, Montreal, Quebec, Canada
- Department of Psychiatry, McGill University, Montreal, Quebec, Canada
- McGill University and Genome Quebec Innovation Centre, Quebec, Canada
| | - Pierre Bellec
- Centre de recherche de l'Institut universitaire de gériatrie de Montréal, Montreal, Quebec, Canada
- Department of Computer Science and Operations Research, University of Montreal, Montreal, QC, Canada
| | - John C S Breitner
- Centre for the Studies on Prevention of Alzheimer’s Disease, Douglas Mental Health University Institute Research Centre, Montreal, Quebec, Canada
- Department of Psychiatry, McGill University, Montreal, Quebec, Canada
| | - Sylvia Villeneuve
- Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
- Centre for the Studies on Prevention of Alzheimer’s Disease, Douglas Mental Health University Institute Research Centre, Montreal, Quebec, Canada
- Department of Psychiatry, McGill University, Montreal, Quebec, Canada
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Ienca M, Vayena E, Blasimme A. Big Data and Dementia: Charting the Route Ahead for Research, Ethics, and Policy. Front Med (Lausanne) 2018; 5:13. [PMID: 29468161 PMCID: PMC5808247 DOI: 10.3389/fmed.2018.00013] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2017] [Accepted: 01/16/2018] [Indexed: 11/13/2022] Open
Abstract
Emerging trends in pervasive computing and medical informatics are creating the possibility for large-scale collection, sharing, aggregation and analysis of unprecedented volumes of data, a phenomenon commonly known as big data. In this contribution, we review the existing scientific literature on big data approaches to dementia, as well as commercially available mobile-based applications in this domain. Our analysis suggests that big data approaches to dementia research and care hold promise for improving current preventive and predictive models, casting light on the etiology of the disease, enabling earlier diagnosis, optimizing resource allocation, and delivering more tailored treatments to patients with specific disease trajectories. Such promissory outlook, however, has not materialized yet, and raises a number of technical, scientific, ethical, and regulatory challenges. This paper provides an assessment of these challenges and charts the route ahead for research, ethics, and policy.
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Affiliation(s)
- Marcello Ienca
- Health Ethics and Policy Laboratory, Department of Health Sciences and Technology, ETH Zurich, Zurich, Switzerland
| | - Effy Vayena
- Health Ethics and Policy Laboratory, Department of Health Sciences and Technology, ETH Zurich, Zurich, Switzerland
| | - Alessandro Blasimme
- Health Ethics and Policy Laboratory, Department of Health Sciences and Technology, ETH Zurich, Zurich, Switzerland
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Youn YC, Choi SH, Shin HW, Kim KW, Jang JW, Jung JJ, Hsiung GYR, Kim S. Detection of cognitive impairment using a machine-learning algorithm. Neuropsychiatr Dis Treat 2018; 14:2939-2945. [PMID: 30464478 PMCID: PMC6219269 DOI: 10.2147/ndt.s171950] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/29/2022] Open
Abstract
PURPOSE The Mini-Mental State Examination (MMSE) is one of the most frequently used bedside screening measures of cognition. However, the Korean Dementia Screening Questionnaire (KDSQ) is an easier and more reliable screening method. Instead, other clinical variables and raw data were used for this study without the consideration of a cutoff value. The objective of this study was to develop a machine-learning algorithm for the detection of cognitive impairment (CI) based on the KDSQ and the MMSE. PATIENTS AND METHODS The original dataset from the Clinical Research Center for Dementia of South Korea study was obtained. In total, 9,885 and 300 patients were randomly allocated to the training and test datasets, respectively. We selected up to 24 variables including sex, age, education duration, diabetes mellitus, and hypertension. We trained a machine-learning algorithm using TensorFlow based on the training dataset and then calculated its accuracy using the test dataset. The cost was calculated by conducting a logistic regression. RESULTS The accuracy of the model in predicting CI based on the KDSQ only, the MMSE only, and the combination of the KDSQ and MMSE was 84.3%, 88.3%, and 86.3%, respectively. For the KDSQ, the sensitivity for detecting CI was 91.50% and the specificity for detecting normal cognition (NL) was 59.60%. The sensitivity of the MMSE was 94.35%, and the specificity was 59.62%. When combining the KDSQ and the MMSE, the sensitivity for detecting CI was 91.5% and the specificity for detecting NL was 61.5%. CONCLUSION The algorithm predicting CI based on the MMSE is superior. However, the KDSQ can be administered more easily in clinical practice and the algorithm using KDSQ is a comparable screening tool.
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Affiliation(s)
- Young Chul Youn
- Department of Neurology, College of Medicine, Chung-Ang University, Seoul, South Korea
| | - Seong Hye Choi
- Department of Neurology, Inha University College of Medicine, Incheon, South Korea
| | - Hae-Won Shin
- Department of Neurology, College of Medicine, Chung-Ang University, Seoul, South Korea
| | - Ko Woon Kim
- Department of Neurology, Chonbuk National University Medical School and Hospital, Chonbuk, South Korea
| | - Jae-Won Jang
- Department of Neurology, Kangwon National University Hospital, Chuncheon, South Korea
| | - Jason J Jung
- Department of Computer Engineering, Chung-Ang University, Seoul, South Korea
| | - Ging-Yuek Robin Hsiung
- Division of Neurology, Department of Medicine, University of British Columbia, Vancouver, BC, Canada
| | - SangYun Kim
- Department of Neurology, Seoul National University College of Medicine and Seoul National University Bundang Hospital, Seoul, South Korea,
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