Evidence map›Paper›PMID 41632213›Full record

ArticleUrolithiasis2026

Multimodal attention-driven network with blockchain integrity for reliable kidney stone diagnosis.

Mudassir Khan, Izhar Husain

Abstract read
PubMed Publisher
In one paragraph

Article in Urolithiasis, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
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

2 authors.

Mudassir KhanDepartment of Computer Science, College of Computer Science, Applied College Tanumah, King Khalid University, Abha, Saudi Arabia. mkmiyob@kku.edu.sa.
Izhar HusainDepartment of Public Health, College of Applied Medical Sciences, Khamis Mashayt, King Khalid University, Almahalah, 62561, Abha, Saudi Arabia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Renal calculi are the hard mineral deposits that obstructed in the renal system of the human. Dehydration, hormonal imbalance and urinary obstruction leads to formation of renal calculi leads to the chronic Kidney disease end with the renal failure. The early detection and treatment is the key to prevent the irreversible complication like scarring and renal failure. With the Ultrasound imaging, detection of renal calculi remains to be challenging till data due to its speckle noise, low tissue contrast and varying anatomical variability. To address this limitations, our study proposed the multimodal attention driven network with blockchain integrity that integrates the complementary global and local representations of the medical data. The developed cross attention fusion module with the interpretability layer strengthens the decision to extract outputs. Additionally, blockchain with smart contracts is utilized to ensure privacy, integrity, and auditability of data in clinical applications. Experimental outcomes prove that Mutimodal attention-Driven network acquires better diagnostic performance with 98.5% accuracy and 99.1% AUC, making it a secure and trustworthy solution for healthcare environments.

Indexed as

BlockchainKidney CalculiHumansUltrasonographyLight weight attention network and Blockchain-Based data securityRenal calculusUltrasound imagingVision transformer

Identifiers

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.