The Age-specific Alzheimer 's Disease Prediction with Characteristic Constraints in Nonuniform Time Span

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Bibliographic Details
Main Authors:	Hong, Xin, Huang, Kaifeng
Format:	Preprint
Published:	2025
Subjects:	Computer Vision and Pattern Recognition
Online Access:	Acceder al recurso
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_version_	1866908677177344000
author	Hong, Xin Huang, Kaifeng
author_facet	Hong, Xin Huang, Kaifeng
contents	Alzheimer's disease is a debilitating disorder marked by a decline in cognitive function. Timely identification of the disease is essential for the development of personalized treatment strategies that aim to mitigate its progression. The application of generated images for the prediction of Alzheimer's disease poses challenges, particularly in accurately representing the disease's characteristics when input sequences are captured at irregular time intervals. This study presents an innovative methodology for sequential image generation, guided by quantitative metrics, to maintain the essential features indicative of disease progression. Furthermore, an age-scaling factor is integrated into the process to produce age-specific MRI images, facilitating the prediction of advanced stages of the disease. The results obtained from the ablation study suggest that the inclusion of quantitative metrics significantly improves the accuracy of MRI image synthesis. Furthermore, the application of age-scaled pixel loss contributed to the enhanced iterative generation of MRI images. In terms of long-term disease prognosis, the Structural Similarity Index reached a peak value of 0.882, indicating a substantial degree of similarity in the synthesized images.
format	Preprint
id	arxiv_https___arxiv_org_abs_2511_21530
institution	arXiv
publishDate	2025
record_format	arxiv
spellingShingle	The Age-specific Alzheimer 's Disease Prediction with Characteristic Constraints in Nonuniform Time Span Hong, Xin Huang, Kaifeng Computer Vision and Pattern Recognition Alzheimer's disease is a debilitating disorder marked by a decline in cognitive function. Timely identification of the disease is essential for the development of personalized treatment strategies that aim to mitigate its progression. The application of generated images for the prediction of Alzheimer's disease poses challenges, particularly in accurately representing the disease's characteristics when input sequences are captured at irregular time intervals. This study presents an innovative methodology for sequential image generation, guided by quantitative metrics, to maintain the essential features indicative of disease progression. Furthermore, an age-scaling factor is integrated into the process to produce age-specific MRI images, facilitating the prediction of advanced stages of the disease. The results obtained from the ablation study suggest that the inclusion of quantitative metrics significantly improves the accuracy of MRI image synthesis. Furthermore, the application of age-scaled pixel loss contributed to the enhanced iterative generation of MRI images. In terms of long-term disease prognosis, the Structural Similarity Index reached a peak value of 0.882, indicating a substantial degree of similarity in the synthesized images.
title	The Age-specific Alzheimer 's Disease Prediction with Characteristic Constraints in Nonuniform Time Span
topic	Computer Vision and Pattern Recognition
url	https://arxiv.org/abs/2511.21530

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