Artificial Intelligence in Surgical Training for Kidney Cancer: A Systematic Review of the Literature
Natali Rodriguez Peñaranda,
Ahmed Eissa,
Stefania Ferretti,
Giampaolo Bianchi,
Stefano Di Bari,
Rui Farinha,
Pietro Piazza,
Enrico Checcucci,
Inés Rivero Belenchón,
Alessandro Veccia,
Juan Gomez Rivas,
Mark Taratkin,
Karl-Friedrich Kowalewski,
Severin Rodler,
Pieter De Backer,
Giovanni Enrico Cacciamani,
Ruben De Groote,
Anthony G. Gallagher,
Alexandre Mottrie,
Salvatore Micali,
Stefano Puliatti
Affiliations
Natali Rodriguez Peñaranda
Department of Urology, Azienda Ospedaliero-Universitaria di Modena, Via Pietro Giardini, 1355, 41126 Baggiovara, Italy
Ahmed Eissa
Department of Urology, Azienda Ospedaliero-Universitaria di Modena, Via Pietro Giardini, 1355, 41126 Baggiovara, Italy
Stefania Ferretti
Department of Urology, Azienda Ospedaliero-Universitaria di Modena, Via Pietro Giardini, 1355, 41126 Baggiovara, Italy
Giampaolo Bianchi
Department of Urology, Azienda Ospedaliero-Universitaria di Modena, Via Pietro Giardini, 1355, 41126 Baggiovara, Italy
Stefano Di Bari
Department of Urology, Azienda Ospedaliero-Universitaria di Modena, Via Pietro Giardini, 1355, 41126 Baggiovara, Italy
Rui Farinha
Orsi Academy, 9090 Melle, Belgium
Pietro Piazza
Division of Urology, IRCCS Azienda Ospedaliero-Universitaria di Bologna, 40138 Bologna, Italy
Enrico Checcucci
Department of Surgery, FPO-IRCCS Candiolo Cancer Institute, 10060 Turin, Italy
Inés Rivero Belenchón
Urology and Nephrology Department, Virgen del Rocío University Hospital, 41013 Seville, Spain
Alessandro Veccia
Department of Urology, University of Verona, Azienda Ospedaliera Universitaria Integrata, 37126 Verona, Italy
Juan Gomez Rivas
Department of Urology, Hospital Clinico San Carlos, 28040 Madrid, Spain
Mark Taratkin
Institute for Urology and Reproductive Health, Sechenov University, 119435 Moscow, Russia
Karl-Friedrich Kowalewski
Department of Urology and Urosurgery, University Medical Center Mannheim, Medical Faculty Mannheim, Heidelberg University, 68167 Mannheim, Germany
Severin Rodler
Department of Urology, University Hospital LMU Munich, 80336 Munich, Germany
Pieter De Backer
Orsi Academy, 9090 Melle, Belgium
Giovanni Enrico Cacciamani
USC Institute of Urology, Catherine and Joseph Aresty Department of Urology, Keck School of Medicine, University of Southern California, Los Angeles, CA 90089, USA
Ruben De Groote
Orsi Academy, 9090 Melle, Belgium
Anthony G. Gallagher
Orsi Academy, 9090 Melle, Belgium
Alexandre Mottrie
Orsi Academy, 9090 Melle, Belgium
Salvatore Micali
Department of Urology, Azienda Ospedaliero-Universitaria di Modena, Via Pietro Giardini, 1355, 41126 Baggiovara, Italy
Stefano Puliatti
Department of Urology, Azienda Ospedaliero-Universitaria di Modena, Via Pietro Giardini, 1355, 41126 Baggiovara, Italy
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.
