Evidence map›Paper›PMID 39607513›Full record

ReviewUrolithiasis2024

UrologiQ: AI-based accurate detection, measurement and reporting of stones in CT-KUB scans.

Abhijith Yenikekaluva, Syed Furqan Azeez, Apeksha Sakegaonkar, Aamir Mohammed Shariff, Mehul Wankhede, Shivam Gaikwad, Viharika Pavuluri, S H Anand, Jithunath Madathiparambil Ramanathan

Abstract readReview
PubMed Publisher
In one paragraph

Review in Urolithiasis, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed, 1 pooled it
–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

2 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. 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

9 authors.

Abhijith YenikekaluvaDepartment of Research, Biocliq Technologies Private Limited, Bangalore, Karnataka, 560043, India. abhijit@biocliq.com.
Syed Furqan AzeezDepartment of Research, Biocliq Technologies Private Limited, Bangalore, Karnataka, 560043, India.
Apeksha SakegaonkarDepartment of Research, Biocliq Technologies Private Limited, Bangalore, Karnataka, 560043, India.
Aamir Mohammed ShariffDepartment of Research, Biocliq Technologies Private Limited, Bangalore, Karnataka, 560043, India.
Mehul WankhedeDepartment of Research, Biocliq Technologies Private Limited, Bangalore, Karnataka, 560043, India.
Shivam GaikwadDepartment of Research, Biocliq Technologies Private Limited, Bangalore, Karnataka, 560043, India.
Viharika PavuluriDepartment of Radiology, Siddaganga Medical College, Tumkur, India.
S H AnandDepartment of Radiology, Siddaganga Medical College, Tumkur, India.
Jithunath Madathiparambil RamanathanDepartment of Urology, Elite Mission Hospital, Thrissur, India.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Kidney stone disease is becoming increasingly common worldwide, with its prevalence increasing annually across all age groups, races, and geographic regions. This sharp increase may be due to significant changes in dietary habits. Early and accurate detection of kidney stones is crucial for timely intervention and prevention of complications. This article discusses the role of artificial intelligence (AI) in detecting kidney stones and managing surgical treatments. Recent advances in AI techniques have introduced new tools that improve the diagnosis and analysis of medical images. AI can use CT-KUB image data to accurately detect the location of kidney stones and measure their size more efficiently than manual methods. AI-based detection methods ensure greater precision and consistency in stone identification and measurement. These improvements can help doctors plan treatments more effectively, resulting in a higher success rate for patients. Integrating AI into kidney stone detection and analysis significantly improves treatment planning and patient management, leading to better patient outcomes and overall quality of healthcare.

Indexed as

Artificial IntelligenceKidney CalculiTomography, X-Ray ComputedHumansNephrolithiasisNon-contrast computed tomography scan of the kidneys, ureter, and bladder (NCCT-KUB)Percutaneous nephrolithotomy (PCNL)Retrograde intrarenal surgery (RIRS)

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.