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Classification of Transgenic Mice by Retinal Imaging Using SVMS. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:9063880. [PMID: 35814547 PMCID: PMC9259271 DOI: 10.1155/2022/9063880] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/31/2022] [Accepted: 06/03/2022] [Indexed: 11/17/2022]
Abstract
Alzheimer's disease is the neuro disorder which characterized by means of Amyloid– β (A β) in brain. However, accurate detection of this disease is a challenging task since the pathological issues of brain are complex in identification. In this paper, the changes associated with the retinal imaging for Alzheimer's disease are classified into two classes such as wild-type (WT) and transgenic mice model (TMM). For testing, optical coherence tomography (OCT) images are used to classify into two groups. The classification is implemented by support vector machines with the optimum kernel selection using a genetic algorithm. Among several kernel functions of SVM, the radial basis kernel function provides the better classification result. In order to deal with an effective classification using SVM, texture features of retinal images are extracted and selected. The overall accuracy reached 92% and 91% of precision for the classification of transgenic mice.
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Tian J, Smith G, Guo H, Liu B, Pan Z, Wang Z, Xiong S, Fang R. Modular machine learning for Alzheimer's disease classification from retinal vasculature. Sci Rep 2021; 11:238. [PMID: 33420208 PMCID: PMC7794289 DOI: 10.1038/s41598-020-80312-2] [Citation(s) in RCA: 36] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2020] [Accepted: 12/15/2020] [Indexed: 12/31/2022] Open
Abstract
Alzheimer's disease is the leading cause of dementia. The long progression period in Alzheimer's disease provides a possibility for patients to get early treatment by having routine screenings. However, current clinical diagnostic imaging tools do not meet the specific requirements for screening procedures due to high cost and limited availability. In this work, we took the initiative to evaluate the retina, especially the retinal vasculature, as an alternative for conducting screenings for dementia patients caused by Alzheimer's disease. Highly modular machine learning techniques were employed throughout the whole pipeline. Utilizing data from the UK Biobank, the pipeline achieved an average classification accuracy of 82.44%. Besides the high classification accuracy, we also added a saliency analysis to strengthen this pipeline's interpretability. The saliency analysis indicated that within retinal images, small vessels carry more information for diagnosing Alzheimer's diseases, which aligns with related studies.
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Affiliation(s)
- Jianqiao Tian
- J. Crayton Pruitt Family Department of Biomedical Engineering, University of Florida, Gainesville, FL, 32611, USA
| | - Glenn Smith
- Department of Clinical and Health Psychology, University of Florida, Gainesville, FL, 32611, USA
| | - Han Guo
- College of Electrical Engineering, Zhejiang University, Hangzhou, 310000, China
| | - Boya Liu
- School of Information and Telecommunication Engineering, Beijing University of Posts & Telecommunications, Beijing, 100876, China
| | - Zehua Pan
- School of Electrical and Information Engineering, Beijing Jiaotong University, Beijing, 100044, China
| | - Zijie Wang
- School of Mathematical Science, East China Normal University, Shanghai, 200062, China
| | - Shuangyu Xiong
- Department of Data Science, East China Normal University, Shanghai, 200062, China
| | - Ruogu Fang
- J. Crayton Pruitt Family Department of Biomedical Engineering, University of Florida, Gainesville, FL, 32611, USA. .,Department of Electrical and Computer Engineering, University of Florida, Gainesville, FL, 32611, USA. .,Center for Cognitive Aging and Memory, McKnight Brain Institute, University of Florida, Gainesville, FL, 32610, USA.
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Hodes JF, Oakley CI, O'Keefe JH, Lu P, Galvin JE, Saif N, Bellara S, Rahman A, Kaufman Y, Hristov H, Rajji TK, Fosnacht Morgan AM, Patel S, Merrill DA, Kaiser S, Meléndez-Cabrero J, Melendez JA, Krikorian R, Isaacson RS. Alzheimer's "Prevention" vs. "Risk Reduction": Transcending Semantics for Clinical Practice. Front Neurol 2019; 9:1179. [PMID: 30719021 PMCID: PMC6348710 DOI: 10.3389/fneur.2018.01179] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2018] [Accepted: 12/20/2018] [Indexed: 12/16/2022] Open
Abstract
The terms "prevention" and "risk reduction" are often used interchangeably in medicine. There is considerable debate, however, over the use of these terms in describing interventions that aim to preserve cognitive health and/or delay disease progression of Alzheimer's disease (AD) for patients seeking clinical care. Furthermore, it is important to distinguish between Alzheimer's disease prevention and Alzheimer's dementia prevention when using these terms. While prior studies have codified research-based criteria for the progressive stages of AD, there are no clear clinical consensus criteria to guide the use of these terms for physicians in practice. A clear understanding of the implications of each term will help guide clinical practice and clinical research. The authors explore the semantics and appropriate use of the terms "prevention" and "risk reduction" as they relate to AD in clinical practice.
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Affiliation(s)
- John F Hodes
- The Klingler College of Arts and Sciences, Marquette University, Milwaukee, WI, United States
| | - Carlee I Oakley
- Kansas City School of Medicine, University of Missouri, Kansas City, MO, United States
| | - James H O'Keefe
- Saint Luke's of Kansas City Mid America Heart Institute, Kansas City, MO, United States
| | - Peilin Lu
- Department of Neurology, Zhejiang University School of Medicine, Hangzhou Shi, China
| | - James E Galvin
- Comprehensive Center for Brain Health, Charles E. Schmidt College of Medicine, Florida Atlantic University, Boca Raton, FL, United States
| | - Nabeel Saif
- Weill Cornell Medicine, NewYork-Presbyterian Hospital, New York, NY, United States
| | - Sonia Bellara
- Weill Cornell Medicine, NewYork-Presbyterian Hospital, New York, NY, United States
| | - Aneela Rahman
- Weill Cornell Medicine, NewYork-Presbyterian Hospital, New York, NY, United States
| | - Yakir Kaufman
- Herzog Hospital, Hebrew University, Jerusalem, Israel
| | - Hollie Hristov
- Weill Cornell Medicine, NewYork-Presbyterian Hospital, New York, NY, United States
| | - Tarek K Rajji
- Centre for Addiction and Mental Health and University of Toronto, Toronto, ON, Canada
| | | | - Smita Patel
- NorthShore University HealthSystem, Evanston, IL, United States
| | - David A Merrill
- Department of Psychiatry, University of California, Los Angeles, Los Angeles, CA, United States
- Pacific Brain Health Center, Pacific Neuroscience Institute, Los Angeles, CA, United States
| | - Scott Kaiser
- Pacific Brain Health Center, Pacific Neuroscience Institute, Los Angeles, CA, United States
| | | | - Juan A Melendez
- Jersey Memory Assessment Service, Health and Community Services, Jersey, United Kingdom
| | - Robert Krikorian
- Department of Psychiatry & Behavioral Neuroscience, University of Cincinnati College of Medicine, Cincinnati, OH, United States
| | - Richard S Isaacson
- Weill Cornell Medicine, NewYork-Presbyterian Hospital, New York, NY, United States
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