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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...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
SAGE Publications
2023
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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. |
format | Online Article Text |
id | pubmed-10492475 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | SAGE Publications |
record_format | MEDLINE/PubMed |
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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