The Feasibility of Early Alzheimer’s Disease Diagnosis Using a Neural Network Hybrid Platform
Early diagnosis of Alzheimer’s Disease (AD) is critical for disease prevention and cure. However, currently, techniques with the required high sensitivity and specificity are lacking. Recently, with the advances and increased accessibility of data analysis tools, such as machine learning, research e...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-09-01
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| Series: | Biosensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2079-6374/12/9/753 |
| _version_ | 1827662939883044864 |
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| author | Xinke Yu Siddharth Srivastava Shan Huang Eric Y. Hayden David B. Teplow Ya-Hong Xie |
| author_facet | Xinke Yu Siddharth Srivastava Shan Huang Eric Y. Hayden David B. Teplow Ya-Hong Xie |
| author_sort | Xinke Yu |
| collection | DOAJ |
| description | Early diagnosis of Alzheimer’s Disease (AD) is critical for disease prevention and cure. However, currently, techniques with the required high sensitivity and specificity are lacking. Recently, with the advances and increased accessibility of data analysis tools, such as machine learning, research efforts have increasingly focused on using these computational methods to solve this challenge. Here, we demonstrate a convolutional neural network (CNN)-based AD diagnosis approach using the surface-enhanced Raman spectroscopy (SERS) fingerprints of human cerebrospinal fluid (CSF). SERS and CNN were combined for biomarker detection to analyze disease-associated biochemical changes in the CSF. We achieved very high reproducibility in double-blind experiments for testing the feasibility of our system on human samples. We achieved an overall accuracy of 92% (100% for normal individuals and 88.9% for AD individuals) based on the clinical diagnosis. Further, we observed an excellent correlation coefficient between our test score and the Clinical Dementia Rating (CDR) score. Our findings offer a substantial indication of the feasibility of detecting AD biomarkers using the innovative combination of SERS and machine learning. We are hoping that this will serve as an incentive for future research in the field. |
| first_indexed | 2024-03-10T00:35:56Z |
| format | Article |
| id | doaj.art-efe53f28f83d47d5837802abfa9f3f61 |
| institution | Directory Open Access Journal |
| issn | 2079-6374 |
| language | English |
| last_indexed | 2024-03-10T00:35:56Z |
| publishDate | 2022-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Biosensors |
| spelling | doaj.art-efe53f28f83d47d5837802abfa9f3f612023-11-23T15:18:35ZengMDPI AGBiosensors2079-63742022-09-0112975310.3390/bios12090753The Feasibility of Early Alzheimer’s Disease Diagnosis Using a Neural Network Hybrid PlatformXinke Yu0Siddharth Srivastava1Shan Huang2Eric Y. Hayden3David B. Teplow4Ya-Hong Xie5Department of Materials Science and Engineering, University of California, Los Angeles, CA 90095, USADepartment of Materials Science and Engineering, University of California, Los Angeles, CA 90095, USADepartment of Materials Science and Engineering, University of California, Los Angeles, CA 90095, USADepartment of Neurology, David Geffen School of Medicine at UCLA, University of California, Los Angeles, CA 90095, USADepartment of Neurology, David Geffen School of Medicine at UCLA, University of California, Los Angeles, CA 90095, USADepartment of Materials Science and Engineering, University of California, Los Angeles, CA 90095, USAEarly diagnosis of Alzheimer’s Disease (AD) is critical for disease prevention and cure. However, currently, techniques with the required high sensitivity and specificity are lacking. Recently, with the advances and increased accessibility of data analysis tools, such as machine learning, research efforts have increasingly focused on using these computational methods to solve this challenge. Here, we demonstrate a convolutional neural network (CNN)-based AD diagnosis approach using the surface-enhanced Raman spectroscopy (SERS) fingerprints of human cerebrospinal fluid (CSF). SERS and CNN were combined for biomarker detection to analyze disease-associated biochemical changes in the CSF. We achieved very high reproducibility in double-blind experiments for testing the feasibility of our system on human samples. We achieved an overall accuracy of 92% (100% for normal individuals and 88.9% for AD individuals) based on the clinical diagnosis. Further, we observed an excellent correlation coefficient between our test score and the Clinical Dementia Rating (CDR) score. Our findings offer a substantial indication of the feasibility of detecting AD biomarkers using the innovative combination of SERS and machine learning. We are hoping that this will serve as an incentive for future research in the field.https://www.mdpi.com/2079-6374/12/9/753SERSAlzheimer’s diseasebiosensingRaman spectroscopymachine learningneural networks |
| spellingShingle | Xinke Yu Siddharth Srivastava Shan Huang Eric Y. Hayden David B. Teplow Ya-Hong Xie The Feasibility of Early Alzheimer’s Disease Diagnosis Using a Neural Network Hybrid Platform Biosensors SERS Alzheimer’s disease biosensing Raman spectroscopy machine learning neural networks |
| title | The Feasibility of Early Alzheimer’s Disease Diagnosis Using a Neural Network Hybrid Platform |
| title_full | The Feasibility of Early Alzheimer’s Disease Diagnosis Using a Neural Network Hybrid Platform |
| title_fullStr | The Feasibility of Early Alzheimer’s Disease Diagnosis Using a Neural Network Hybrid Platform |
| title_full_unstemmed | The Feasibility of Early Alzheimer’s Disease Diagnosis Using a Neural Network Hybrid Platform |
| title_short | The Feasibility of Early Alzheimer’s Disease Diagnosis Using a Neural Network Hybrid Platform |
| title_sort | feasibility of early alzheimer s disease diagnosis using a neural network hybrid platform |
| topic | SERS Alzheimer’s disease biosensing Raman spectroscopy machine learning neural networks |
| url | https://www.mdpi.com/2079-6374/12/9/753 |
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