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Volume 24
Issue 16
10.3390/s24165192
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This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Article

Feature-Based Classification of Mild Cognitive Impairment and Alzheimer’s Disease Based on Optical Coherence Tomographic Angiographic Image

by
Sarinporn Visitsattapongse
1,
Areerat Maneerat
1,
Adisak Trinavarat
2,
Chatchawan Rattanabannakit
3,
Ekkaphop Morkphrom
3,
Vorapun Senanarong
3,
Varalak Srinonprasert
3,
Dittapong Songsaeng
4,
La-ongsri Atchaneeyasakul
2,* and
Chuchart Pintavirooj
1,*
1
School of Engineering, King Mongkut’s Institute of Technology Ladkrabang, Bangkok 10520, Thailand
2
Department of Ophthalmology, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok 10700, Thailand
3
Department of Medicine, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok 10700, Thailand
4
Department of Radiology, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok 10700, Thailand
*
Authors to whom correspondence should be addressed.
Sensors 2024, 24(16), 5192; https://doi.org/10.3390/s24165192 (registering DOI)
Submission received: 26 June 2024 / Revised: 5 August 2024 / Accepted: 9 August 2024 / Published: 11 August 2024
(This article belongs to the Section Biomedical Sensors)
Versions Notes

Abstract

Alzheimer’s disease is a type of neurodegenerative disorder that is characterized by the progressive degeneration of brain cells, leading to cognitive decline and memory loss. It is the most common cause of dementia and affects millions of people worldwide. While there is currently no cure for Alzheimer’s disease, early detection and treatment can help to slow the progression of symptoms and improve quality of life. This research presents a diagnostic tool for classifying mild cognitive impairment and Alzheimer’s diseases using feature-based machine learning applied to optical coherence tomographic angiography images (OCT-A). Several features are extracted from the OCT-A image, including vessel density in five sectors, the area of the foveal avascular zone, retinal thickness, and novel features based on the histogram of the range-filtered OCT-A image. To ensure effectiveness for a diverse population, a large local database for our study was collected. The promising results of our study, with the best accuracy of 92.17% will provide an efficient diagnostic tool for early detection of Alzheimer’s disease.
Keywords: coherence tomographic angiography; Alzheimer’s disease; machine learning model; OCT-A coherence tomographic angiography; Alzheimer’s disease; machine learning model; OCT-A

Share and Cite

MDPI and ACS Style

Visitsattapongse, S.; Maneerat, A.; Trinavarat, A.; Rattanabannakit, C.; Morkphrom, E.; Senanarong, V.; Srinonprasert, V.; Songsaeng, D.; Atchaneeyasakul, L.-o.; Pintavirooj, C. Feature-Based Classification of Mild Cognitive Impairment and Alzheimer’s Disease Based on Optical Coherence Tomographic Angiographic Image. Sensors 2024, 24, 5192. https://doi.org/10.3390/s24165192

AMA Style

Visitsattapongse S, Maneerat A, Trinavarat A, Rattanabannakit C, Morkphrom E, Senanarong V, Srinonprasert V, Songsaeng D, Atchaneeyasakul L-o, Pintavirooj C. Feature-Based Classification of Mild Cognitive Impairment and Alzheimer’s Disease Based on Optical Coherence Tomographic Angiographic Image. Sensors. 2024; 24(16):5192. https://doi.org/10.3390/s24165192

Chicago/Turabian Style

Visitsattapongse, Sarinporn, Areerat Maneerat, Adisak Trinavarat, Chatchawan Rattanabannakit, Ekkaphop Morkphrom, Vorapun Senanarong, Varalak Srinonprasert, Dittapong Songsaeng, La-ongsri Atchaneeyasakul, and Chuchart Pintavirooj. 2024. "Feature-Based Classification of Mild Cognitive Impairment and Alzheimer’s Disease Based on Optical Coherence Tomographic Angiographic Image" Sensors 24, no. 16: 5192. https://doi.org/10.3390/s24165192

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