How AI/ML detects organ age?
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Artificial Intelligence (AI) and Machine Learning (ML) techniques are utilized to detect organ age by analyzing medical images such as magnetic resonance images (MRI) and organoids. Deep learning models, like convolutional neural networks (CNNs), are trained on large datasets of organ images to predict organ age accurately . These models can identify features associated with aging in specific organs, enabling the estimation of organ age based on anatomical changes. Additionally, AI algorithms integrated with convolutional block attention modules (CBAM) can evaluate the vitality and aging progression of organoids by analyzing phenotypic parameters like cell diameter and gene expression. By leveraging AI/ML, researchers can gain insights into the aging process of organs, potentially leading to improved health assessments and personalized interventions.
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| Papers (5) | Insight |
|---|---|
| The AI algorithm in the study uses YOLOv3 with CBAM to evaluate organoid age based on parameters like diameter and number, correlating with senescence markers. | |
24 Jan 2021 | The paper proposes a 3D CNN architecture utilizing MRI scans for organ-based age estimation, demonstrating superior performance compared to other methods for brain and knee age estimation. |
| AI/ML detects organ age by training convolutional neural networks on liver and pancreas MRIs to predict abdominal age, identifying genetic and non-genetic factors associated with abdominal aging. | |
| Not addressed in the paper. | |
Open access•Posted Content | The paper proposes a 3D CNN architecture for organ-based age estimation using MRI scans, enhancing accuracy compared to traditional chronological age assessment methods. |
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