TY - JOUR
T1 - Artificial Intelligence in Surgical Training for Kidney Cancer: A Systematic Review of the Literature
AU - Rodriguez Peñaranda, Natali
AU - Eissa, Ahmed
AU - Ferretti, Stefania
AU - Bianchi, Giampaolo
AU - Di Bari, Stefano
AU - Farinha, Rui
AU - Piazza, Pietro
AU - Checcucci, Enrico
AU - Belenchón, Inés Rivero
AU - Veccia, Alessandro
AU - Gomez Rivas, Juan
AU - Taratkin, Mark
AU - Kowalewski, Karl-Friedrich
AU - Rodler, Severin
AU - De Backer, Pieter
AU - Cacciamani, Giovanni Enrico
AU - De Groote, Ruben
AU - Gallagher, Anthony G.
AU - Mottrie, Alexandre
AU - Micali, Salvatore
AU - Puliatti, Stefano
N1 - Publisher Copyright:
© 2023 by the authors.
PY - 2023/9/27
Y1 - 2023/9/27
N2 - The prevalence of renal cell carcinoma (RCC) is increasing due to advanced imaging techniques. Surgical resection is the standard treatment, involving complex radical and partial nephrectomy procedures that demand extensive training and planning. Furthermore, artificial intelligence (AI) can potentially aid the training process in the field of kidney cancer. This review explores how artificial intelligence (AI) can create a framework for kidney cancer surgery to address training difficulties. Following PRISMA 2020 criteria, an exhaustive search of PubMed and SCOPUS databases was conducted without any filters or restrictions. Inclusion criteria encompassed original English articles focusing on AI’s role in kidney cancer surgical training. On the other hand, all non-original articles and articles published in any language other than English were excluded. Two independent reviewers assessed the articles, with a third party settling any disagreement. Study specifics, AI tools, methodologies, endpoints, and outcomes were extracted by the same authors. The Oxford Center for Evidence-Based Medicine’s evidence levels were employed to assess the studies. Out of 468 identified records, 14 eligible studies were selected. Potential AI applications in kidney cancer surgical training include analyzing surgical workflow, annotating instruments, identifying tissues, and 3D reconstruction. AI is capable of appraising surgical skills, including the identification of procedural steps and instrument tracking. While AI and augmented reality (AR) enhance training, challenges persist in real-time tracking and registration. The utilization of AI-driven 3D reconstruction proves beneficial for intraoperative guidance and preoperative preparation. Artificial intelligence (AI) shows potential for advancing surgical training by providing unbiased evaluations, personalized feedback, and enhanced learning processes. Yet challenges such as consistent metric measurement, ethical concerns, and data privacy must be addressed. The integration of AI into kidney cancer surgical training offers solutions to training difficulties and a boost to surgical education. However, to fully harness its potential, additional studies are imperative.
AB - The prevalence of renal cell carcinoma (RCC) is increasing due to advanced imaging techniques. Surgical resection is the standard treatment, involving complex radical and partial nephrectomy procedures that demand extensive training and planning. Furthermore, artificial intelligence (AI) can potentially aid the training process in the field of kidney cancer. This review explores how artificial intelligence (AI) can create a framework for kidney cancer surgery to address training difficulties. Following PRISMA 2020 criteria, an exhaustive search of PubMed and SCOPUS databases was conducted without any filters or restrictions. Inclusion criteria encompassed original English articles focusing on AI’s role in kidney cancer surgical training. On the other hand, all non-original articles and articles published in any language other than English were excluded. Two independent reviewers assessed the articles, with a third party settling any disagreement. Study specifics, AI tools, methodologies, endpoints, and outcomes were extracted by the same authors. The Oxford Center for Evidence-Based Medicine’s evidence levels were employed to assess the studies. Out of 468 identified records, 14 eligible studies were selected. Potential AI applications in kidney cancer surgical training include analyzing surgical workflow, annotating instruments, identifying tissues, and 3D reconstruction. AI is capable of appraising surgical skills, including the identification of procedural steps and instrument tracking. While AI and augmented reality (AR) enhance training, challenges persist in real-time tracking and registration. The utilization of AI-driven 3D reconstruction proves beneficial for intraoperative guidance and preoperative preparation. Artificial intelligence (AI) shows potential for advancing surgical training by providing unbiased evaluations, personalized feedback, and enhanced learning processes. Yet challenges such as consistent metric measurement, ethical concerns, and data privacy must be addressed. The integration of AI into kidney cancer surgical training offers solutions to training difficulties and a boost to surgical education. However, to fully harness its potential, additional studies are imperative.
KW - RAPN
KW - partial nephrectomy
KW - radical nephrectomy
KW - kidney cancer
KW - renal cancer
KW - annotation
KW - deep learning
KW - computer vision
KW - artificial neural network
KW - artificial intelligence
KW - training
KW - augmented reality
KW - simulation
UR - http://www.scopus.com/inward/record.url?scp=85173837120&partnerID=8YFLogxK
U2 - 10.3390/diagnostics13193070
DO - 10.3390/diagnostics13193070
M3 - Article
C2 - 37835812
SN - 2075-4418
VL - 13
SP - 1
EP - 17
JO - Diagnostics
JF - Diagnostics
IS - 19
M1 - 3070
ER -
