ArticleScientific reports2025
Secure healthcare data management using multimodal image fusion and dual watermarking.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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.
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.
Who cites it
1 citing paper in PubMed.
- Unsupervised Transformer Learning for Rapid and High-Quality MRI Data Acquisition.Health data science · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This study introduces a robust framework to address critical security requirements in digital healthcare systems, ensuring the confidentiality and integrity of medical data. It employs Laplacian redecomposition to fuse MRI and SPECT/PET images, creating a host image for embedding Aadhaar card image and a computed hash value. The embedding process integrates lifting wavelet transform, Hessenberg decomposition, and singular value decomposition to balance imperceptibility and robustness. A pseudo magic cube technique conceals the hash value, while an encryption scheme secures the watermarked image during transmission. Performance evaluations highlight strong results, with PSNR reaching 37.7895 dB, SSIM up to 0.9993, and NC up to 0.9998, demonstrating resilience against various image processing attacks. This framework provides a reliable and effective solution for safeguarding medical data, addressing the pressing need for secure digital healthcare systems in an era of increasing reliance on telehealth and electronic health records.
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Registered trials
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.