Evidence map›Paper›PMID 39335738›Full record

ReviewDiagnostics (Basel, Switzerland)2024

Artificial Intelligence Tools in Pediatric Urology: A Comprehensive Review of Recent Advances.

Adiba Tabassum Chowdhury, Abdus Salam, Mansura Naznine, Da'ad Abdalla, Lauren Erdman, Muhammad E H Chowdhury, Tariq O Abbas

Abstract readReview
In one paragraph

Review in Diagnostics (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing 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

7 citing papers in PubMed.

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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

7 authors.

Adiba Tabassum ChowdhuryDepartment of Electrical and Electronic Engineering, University of Dhaka, Dhaka 1000, Bangladesh.ORCID 0009-0001-6363-9018
Abdus SalamDepartment of Electrical & Computer Engineering, Rajshahi University of Engineering & Technology, Rajshashi 6204, Bangladesh.ORCID 0009-0007-0787-1342
Mansura NaznineDepartment of Computer Science & Engineering, Rajshahi University of Engineering & Technology, Rajshashi 6204, Bangladesh.ORCID 0009-0007-0296-9981
Da'ad AbdallaFaculty of Medicine, University of Khartoum, Khartoum 11115, Sudan.ORCID 0000-0002-6476-6123
Lauren ErdmanJames M. Anderson Center for Health Systems Excellence, Cincinnati, OH 45255, USA.
Muhammad E H ChowdhuryElectrical Engineering, Qatar University, Doha 2713, Qatar.ORCID 0000-0003-0744-8206
Tariq O AbbasPediatric Urology Section, Sidra Medicine, Doha 26999, Qatar.ORCID 0000-0002-8425-4029

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Artificial intelligence (AI) is providing novel answers to long-standing clinical problems, and it is quickly changing pediatric urology. This thorough analysis focuses on current developments in AI technologies that improve pediatric urology diagnosis, treatment planning, and surgery results. Deep learning algorithms help detect problems with previously unheard-of precision in disorders including hydronephrosis, pyeloplasty, and vesicoureteral reflux, where AI-powered prediction models have demonstrated promising outcomes in boosting diagnostic accuracy. AI-enhanced image processing methods have significantly improved the quality and interpretation of medical images. Examples of these methods are deep-learning-based segmentation and contrast limited adaptive histogram equalization (CLAHE). These methods guarantee higher precision in the identification and classification of pediatric urological disorders, and AI-driven ground truth construction approaches aid in the standardization of and improvement in training data, resulting in more resilient and consistent segmentation models. AI is being used for surgical support as well. AI-assisted navigation devices help with difficult operations like pyeloplasty by decreasing complications and increasing surgical accuracy. AI also helps with long-term patient monitoring, predictive analytics, and customized treatment strategies, all of which improve results for younger patients. However, there are practical, ethical, and legal issues with AI integration in pediatric urology that need to be carefully navigated. To close knowledge gaps, more investigation is required, especially in the areas of AI-driven surgical methods and standardized ground truth datasets for pediatric radiologic image segmentation. In the end, AI has the potential to completely transform pediatric urology by enhancing patient care, increasing the effectiveness of treatments, and spurring more advancements in this exciting area.

Indexed as

artificial intelligenceartificial intelligence applicationsdiagnostic accuracymedical imagingpediatric medicinepediatric urologypredictive modelingsurgical challenge

Identifiers

PMID39335738
PMCPMC11431426

What OpenQuestion holds

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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.