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

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 publi...

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Autores principales: Nedbal, Carlotta, Cerrato, Clara, Jahrreiss, Victoria, Castellani, Daniele, Pietropaolo, Amelia, Galosi, Andrea Benedetto, Somani, Bhaskar Kumar
Formato: Online Artículo Texto
Lenguaje:English
Publicado: SAGE Publications 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10492475/
https://www.ncbi.nlm.nih.gov/pubmed/37693931
http://dx.doi.org/10.1177/17562872231196676
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author Nedbal, Carlotta
Cerrato, Clara
Jahrreiss, Victoria
Castellani, Daniele
Pietropaolo, Amelia
Galosi, Andrea Benedetto
Somani, Bhaskar Kumar
author_facet Nedbal, Carlotta
Cerrato, Clara
Jahrreiss, Victoria
Castellani, Daniele
Pietropaolo, Amelia
Galosi, Andrea Benedetto
Somani, Bhaskar Kumar
author_sort Nedbal, Carlotta
collection PubMed
description 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% (p = 0.0027). There was a gradual increase in areas such as automated diagnosis of larger stones, automated intraoperative needle targeting, and VR simulators in surgical planning and training. This increase was most marked in Period 3 with automated targeting with 52 papers (45%), followed by the application of AI, ML, and radiomics in predicting operative outcomes (22%, n = 26) and VR for simulation (18%, n = 21). Papers on technological innovations in PCNL (n = 9), intelligent construction of personalized protocols (n = 6), and automated diagnosis (n = 2) accounted for 15% of publications. A rise in automated targeting for PCNL and PCNL training between Period 2 and Period 3 was +247% (p = 0.0055) and +200% (p = 0.0161), respectively. 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.
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spelling pubmed-104924752023-09-10 The role of ‘artificial intelligence, machine learning, virtual reality, and radiomics’ in PCNL: a review of publication trends over the last 30 years Nedbal, Carlotta Cerrato, Clara Jahrreiss, Victoria Castellani, Daniele Pietropaolo, Amelia Galosi, Andrea Benedetto Somani, Bhaskar Kumar Ther Adv Urol Virtual, Augmented and Mixed Reality in Urology-From Prevention to Follow-up 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% (p = 0.0027). There was a gradual increase in areas such as automated diagnosis of larger stones, automated intraoperative needle targeting, and VR simulators in surgical planning and training. This increase was most marked in Period 3 with automated targeting with 52 papers (45%), followed by the application of AI, ML, and radiomics in predicting operative outcomes (22%, n = 26) and VR for simulation (18%, n = 21). Papers on technological innovations in PCNL (n = 9), intelligent construction of personalized protocols (n = 6), and automated diagnosis (n = 2) accounted for 15% of publications. A rise in automated targeting for PCNL and PCNL training between Period 2 and Period 3 was +247% (p = 0.0055) and +200% (p = 0.0161), respectively. 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. SAGE Publications 2023-09-08 /pmc/articles/PMC10492475/ /pubmed/37693931 http://dx.doi.org/10.1177/17562872231196676 Text en © The Author(s), 2023 https://creativecommons.org/licenses/by-nc/4.0/This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 License (https://creativecommons.org/licenses/by-nc/4.0/) which permits non-commercial use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access pages (https://us.sagepub.com/en-us/nam/open-access-at-sage).
spellingShingle Virtual, Augmented and Mixed Reality in Urology-From Prevention to Follow-up
Nedbal, Carlotta
Cerrato, Clara
Jahrreiss, Victoria
Castellani, Daniele
Pietropaolo, Amelia
Galosi, Andrea Benedetto
Somani, Bhaskar Kumar
The role of ‘artificial intelligence, machine learning, virtual reality, and radiomics’ in PCNL: a review of publication trends over the last 30 years
title The role of ‘artificial intelligence, machine learning, virtual reality, and radiomics’ in PCNL: a review of publication trends over the last 30 years
title_full The role of ‘artificial intelligence, machine learning, virtual reality, and radiomics’ in PCNL: a review of publication trends over the last 30 years
title_fullStr The role of ‘artificial intelligence, machine learning, virtual reality, and radiomics’ in PCNL: a review of publication trends over the last 30 years
title_full_unstemmed The role of ‘artificial intelligence, machine learning, virtual reality, and radiomics’ in PCNL: a review of publication trends over the last 30 years
title_short The role of ‘artificial intelligence, machine learning, virtual reality, and radiomics’ in PCNL: a review of publication trends over the last 30 years
title_sort role of ‘artificial intelligence, machine learning, virtual reality, and radiomics’ in pcnl: a review of publication trends over the last 30 years
topic Virtual, Augmented and Mixed Reality in Urology-From Prevention to Follow-up
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10492475/
https://www.ncbi.nlm.nih.gov/pubmed/37693931
http://dx.doi.org/10.1177/17562872231196676
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