Evidence map›Paper›PMID 42344495›Full record

ArticleFrontiers in medicine2026

An intelligent gradient-guided hybrid inpainting framework for brain MRI reconstruction and Alzheimer's disease classification in connected healthcare systems.

Chhaya Yadav, Sunita Yadav, Arvind Panwar, Massimo Donelli, Achin Jain

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Frontiers in medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

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

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Chhaya YadavSchool of Computer Science and Engineering, Galgotias University, Greater Noida, Uttar Pradesh, India.
Sunita YadavInderprastha Engineering College, Ghaziabad, Uttar Pradesh, India.
Arvind PanwarSchool of Computer Science and Engineering, Galgotias University, Greater Noida, Uttar Pradesh, India.
Massimo DonelliDepartment of Civil, Environmental and Mechanical Engineering, University of Trento, Trento, Italy.
Achin JainDepartment of Information Technology, Bharati Vidyapeeth's College of Engineering, New Delhi, India.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Brain magnetic resonance imaging (MRI) is essential for early Alzheimer's disease diagnosis, yet clinical scans are often degraded by motion artifacts, signal loss, or incomplete acquisitions. Image inpainting offers a promising preprocessing solution, but existing methods have limitations: deep learning models such as LaMa generate visually plausible reconstructions but may compromise structural fidelity, while classical diffusion-based approaches like OpenCV Telea preserve local continuity but tend to oversmooth complex anatomy. This study proposes a

Indexed as

Alzheimer's diseasebrain MRIconnected healthcaredeep learninggradient-guided hybrid frameworkimage inpaintingintelligent healthcare systemsmedical image reconstruction

Identifiers

PMID42344495
PMCPMC13286836

What OpenQuestion holds

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Registered trials

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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.