Evidence map›Paper›PMID 41116271›Full record

ArticleCurrent medical imaging2025

Sentiment Analysis of Radiological Narratives and Intracranial MRI Hemorrhage Images for the Prediction of Dementia and Alzheimer's Disease.

K Balasaranya, P Ezhumalai, N R Shanker

Abstract read
In one paragraph

Article in Current medical imaging, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

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5 · Who and what money

Authors and funding

3 authors.

K BalasaranyaDepartment of CSE, R.M.D. Engineering College, Kavareipattai, Chennai, India.ORCID 0000-0002-2934-7185
P EzhumalaiDepartment of CSE, R.M.D. Engineering College, Kavareipattai, Chennai, India.ORCID 0000-0003-0778-1682
N R ShankerDepartment of CSE, Aalim Muhammed Salegh College of Engineering, Chennai, India.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

<p> Introduction: Intracranial hemorrhage (IH) causes dementia and Alzheimer's disease in the later stages. Until now, the accurate, early detection of IH, its prognosis, and therapeutic interventions have been a challenging task. Objective: A Multimodal Joint Fusion Sentiment Analysis (MJFSA) framework is proposed for the early detection and classification of IH, as well as sentiment analysis to support prognosis and therapeutic report generation. </p> <p> Methodology: MJFSA integrates radiological images and the radiological clinical narrative reports (RCNRs). In the proposed MJFSA model, MRI brain images are enhanced using the modified Contrast Limited Adaptive Histogram Equalization (M-CLAHE) algorithm. Enhanced images are processed with the proposed Tuned Temporal-GAN (Tuned-T-GAN) algorithm to generate temporal images. RCNRs are generated for temporal images using the Microsoft-Phi2 language model. Temporal images are processed with the Tuned-Vision Image Transformer (T-ViT) model to extract image features. On the other hand, the Bio-Bidirectional Encoder Representation Transformer (Bio-BERT) processes the RCNR texts for text feature extraction. Temporal image and RCNR text features are used for IH classification, such as intracerebral hemorrhage (ICH), epidural hemorrhage (EDH), subdural hemorrhage (SDH), and intraventricular hemorrhage (IVH), resulting in sentiment analysis for prognosis and therapeutic reports. </p> <p> Results: The MJFSA model has achieved an accuracy of 96.5% in prognosis sentiment analysis and 94.5% in therapeutic sentiment analysis. </p> <p> Discussion: The Multimodal Joint Fusion Sentiment Analysis (MJFSA) framework detects IH and classifies it using sentiment analysis for prognosis and therapeutic report generation. </p> <p> Conclusion: The MJFSA model's prognosis and therapeutic sentiment analysis report aims to support the early identification and management of risk factors associated with dementia and Alzheimer's disease. </p>.

Indexed as

Clinical narratives.Dementia predictionIntracranial hemorrhagePrognosisTherapeutic sentimentVision Image Transformer (ViT)

Identifiers

PMID41116271
PMCPMC13137354

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.