Evidence map›Paper›PMID 37693931›Full record

ReviewTherapeutic advances in urology

The role of 'artificial intelligence, machine learning, virtual reality, and radiomics' in PCNL: a review of publication trends over the last 30 years.

Carlotta Nedbal, Clara Cerrato, Victoria Jahrreiss, Daniele Castellani, Amelia Pietropaolo, Andrea Benedetto Galosi, Bhaskar Kumar Somani

Open access · goldAbstract readReview
In one paragraph

Review in Therapeutic advances in urology. 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
4.1field-weighted citation impact, top 5% of its field
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, 16 citations in OpenAlex.

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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 at 3 institutions in 3 countries.

Carlotta NedbalUrology Unit, Azienda Ospedaliero-Universitaria delle Marche, Polytechnic University of Marche, Ancona, Italy.
Clara CerratoDepartment of Urology, University Hospitals Southampton, NHS Trust, Southampton, UK.
Victoria JahrreissDepartment of Urology, University Hospitals Southampton, NHS Trust, Southampton, UK.
Daniele CastellaniUrology Unit, Azienda Ospedaliero-Universitaria delle Marche, Polytechnic University of Marche, Ancona, Italy.
Amelia PietropaoloDepartment of Urology, University Hospitals Southampton, NHS Trust, Southampton, UK.ORCID https://orcid.org/0000-0001-7631-3108
Andrea Benedetto GalosiUrology Unit, Azienda Ospedaliero-Universitaria delle Marche, Polytechnic University of Marche, Ancona, Italy.
Bhaskar Kumar SomaniProfessor and Consultant Urological Surgeon, University Hospital Southampton NHS Trust, Tremona Road, Southampton, SO16 6YD, UK.ORCID https://orcid.org/0000-0002-6248-6478
Marche Polytechnic University · ITComprehensive Cancer Center Vienna · ATUniversity Hospital Southampton NHS Foundation Trust · GB

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: We wanted to analyze the trend of publications in a period of 30 years from 1994 to 2023, on the application of 'artificial intelligence (AI), machine learning (ML), virtual reality (VR), and radiomics in percutaneous nephrolithotomy (PCNL)'. We conducted this study by looking at published papers associated with AI and PCNL procedures, including simulation training, with preoperative and intraoperative applications. Materials and Methods: Although MeSH terms research on the PubMed database, we performed a comprehensive review of the literature from 1994 to 2023 for all published papers on 'AI, ML, VR, and radiomics' in 'PCNL', with papers in all languages included. Papers were divided into three 10-year periods: Period 1 (1994-2003), Period 2 (2004-2013), and Period 3 (2014-2023). Results: Over a 30-year timeframe, 143 papers have been published on the subject with 116 (81%) published in the last decade, with a relative increase from Period 2 to Period 3 of +427% ( Conclusion: An interest in the application of AI in PCNL procedures has increased in the last 30 years, and a steep rise has been witnessed in the last 10 years. As new technologies are developed, their application in devices for training and automated systems for precise renal puncture and outcome prediction seems to play a leading role in modern-day AI-based publication trends on PCNL.

Indexed as

Artificial intelligencekidney calculimachine learningPCNLradiomicssimulationurolithiasis

Identifiers

PMID37693931
PMCPMC10492475
OpenAlexW4386549174

What OpenQuestion holds

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LicenceCC BY-NC
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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.