Artificial Intelligence in Surgical Training for Kidney Cancer: A Systematic Review of the Literature

Rodriguez Peñaranda, Natali; Eissa, Ahmed; Ferretti, Stefania; Bianchi, Giampaolo; Di Bari, Stefano; Farinha, Rui; Piazza, Pietro; Checcucci, Enrico; Belenchón, Inés Rivero; Veccia, Alessandro; Gomez Rivas, Juan; Taratkin, Mark; Kowalewski, Karl-Friedrich; Rodler, Severin; De Backer, Pieter; Cacciamani, Giovanni Enrico; De Groote, Ruben; Gallagher, Anthony G.; Mottrie, Alexandre; Micali, Salvatore; Puliatti, Stefano

doi:10.3390/diagnostics13193070

Cited by 8 publications

(21 citation statements)

References 79 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…These models are composed of non-linear systems, allowing the AI to learn and make decisions in a flexible and intelligent manner. The main applications involve performance evaluation and the implementation of training model fidelity [ 36 ].…”

Section: Resultsmentioning

confidence: 99%

Simulation and Training in Robot-Assisted Urological Surgery: From Model to Patient

Proietti,

Flammia,

Licari

et al. 2024

JCM

View full text Add to dashboard Cite

(1) Background: Simulation-based training has revolutionized surgical education, providing a solution to the changing demands of surgical training and performance. The increasing demand for standardized training in robotic surgery has accelerated the adoption of simulation-based training as a necessary component of modern surgical education. This study examines the existing literature on training approaches employed in robot-assisted urological surgery; (2) Methods: The authors conducted a standardized search of online databases. Upon collecting the articles, the authors assessed their relevance and content before proceeding with the drafting of the text; (3) Results: The use of simulators is supported by convincing evidence that shows an advantage in the acquisition of robotic skills. Urological societies have created detailed training programs for robotic surgery that guide beginners through the entire process of skill acquisition; (4) Conclusions: The future landscape for robotic urology training is likely to involve organized, obligatory, and centralized training, which may be overseen by urologic associations.

show abstract

Section: Resultsmentioning

confidence: 99%

Simulation and Training in Robot-Assisted Urological Surgery: From Model to Patient

Proietti,

Flammia,

Licari

et al. 2024

JCM

View full text Add to dashboard Cite

show abstract

“…The only way to substitute human hands for this task was the development of a dedicated software that was taught to recognize the target organ and to superimpose the 3DVM over it automatically, using AI. The use of this technology is currently pushing informatics over its limits, and the employment of AI could potentially revolutionize healthcare and consequently urological care [ 24 , 25 , 26 ]. We then decided to explore AI algorithms for organs and for 3DVM automatic recognitions [ 27 , 28 ].…”

Section: Discussionmentioning

confidence: 99%

Automatic 3D Augmented-Reality Robot-Assisted Partial Nephrectomy Using Machine Learning: Our Pioneer Experience

Piana,

Amparore,

Sica

et al. 2024

Cancers

Self Cite

View full text Add to dashboard Cite

The aim of “Precision Surgery” is to reduce the impact of surgeries on patients’ global health. In this context, over the last years, the use of three-dimensional virtual models (3DVMs) of organs has allowed for intraoperative guidance, showing hidden anatomical targets, thus limiting healthy-tissue dissections and subsequent damage during an operation. In order to provide an automatic 3DVM overlapping in the surgical field, we developed and tested a new software, called “ikidney”, based on convolutional neural networks (CNNs). From January 2022 to April 2023, patients affected by organ-confined renal masses amenable to RAPN were enrolled. A bioengineer, a software developer, and a surgeon collaborated to create hyper-accurate 3D models for automatic 3D AR-guided RAPN, using CNNs. For each patient, demographic and clinical data were collected. A total of 13 patients were included in the present study. The average anchoring time was 11 (6–13) s. Unintended 3D-model automatic co-registration temporary failures happened in a static setting in one patient, while this happened in one patient in a dynamic setting. There was one failure; in this single case, an ultrasound drop-in probe was used to detect the neoplasm, and the surgery was performed under ultrasound guidance instead of AR guidance. No major intraoperative nor postoperative complications (i.e., Clavien Dindo > 2) were recorded. The employment of AI has unveiled several new scenarios in clinical practice, thanks to its ability to perform specific tasks autonomously. We employed CNNs for an automatic 3DVM overlapping during RAPN, thus improving the accuracy of the superimposition process.

show abstract

“…It enables surgeons to fluently utilize various technologies throughout the operation. This enables surgeons to personalize surgical strategies and training [50]; therefore, it is crucial to provide accurate and up-to-date data on the various surgical strategies for partial nephrectomy in order to feed quality data into the AI models, which are essential for the algorithm to generate effective strategies tailored to each individual case.…”

Section: Discussionmentioning

confidence: 99%

Comparison of Perioperative, Functional, and Oncologic Outcomes of Open vs. Robot-Assisted Off-Clamp Partial Nephrectomy: A Propensity Scored Match Analysis

Mastroianni,

Chiacchio,

Perpepaj

et al. 2024

Sensors

View full text Add to dashboard Cite

Off-clamp partial nephrectomy represents one of the latest developments in nephron-sparing surgery, with the goal of preserving renal function and reducing ischemia time. The aim of this study was to evaluate and compare the functional, oncologic, and perioperative outcomes between off-clamp robot-assisted partial nephrectomy (off-C RAPN) and off-clamp open partial nephrectomy (off-C OPN) through a propensity score-matched (PSM) analysis. A 1:1 PSM analysis was used to balance variables potentially affecting postoperative outcomes. To report surgical quality, 1 year trifecta was used. Univariable Cox regression analysis was performed to identify predictors of trifecta achievement. The Kaplan–Meier method was used to compare cancer-specific survival (CSS), overall survival (OS), disease-free survival (DFS), and metastasis-free survival (MFS) probabilities between groups. Overall, 542 patients were included. After PSM analysis, two homogeneous cohorts of 147 patients were obtained. The off-C RAPN cohort experienced shorter length of stay (LoS) (3.4 days vs. 5.4 days; p < 0.001), increased likelihoods of achieving 1 year trifecta (89.8% vs. 80.3%; p = 0.03), lower postoperative Clavien–Dindo ≤ 2 complications (1.3% vs. 18.3%, p < 0.001), and lower postoperative transfusion rates (3.4% vs. 12.2%, p = 0.008). At univariable analysis, the surgical approach (off-C RAPN vs. off-C OPN, OR 2.22, 95% CI 1.09–4.46, p = 0.02) was the only predictor of 1 year trifecta achievement. At Kaplan–Meier analysis, no differences were observed between the two groups in terms of OS (log-rank p = 0.451), CSS (log-rank p = 0.476), DFS (log-rank p = 0.678), and MFS (log-rank p = 0.226). Comparing RAPN and OPN in a purely off-clamp scenario, the minimally invasive approach proved to be a feasible and safe surgical approach, with a significantly lower LoS and minor rate of postoperative complications and transfusions as a result of improved surgical quality expressed by higher 1 year trifecta achievement.

show abstract

Artificial Intelligence in Surgical Training for Kidney Cancer: A Systematic Review of the Literature

Cited by 8 publications

References 79 publications

Simulation and Training in Robot-Assisted Urological Surgery: From Model to Patient

Simulation and Training in Robot-Assisted Urological Surgery: From Model to Patient

Automatic 3D Augmented-Reality Robot-Assisted Partial Nephrectomy Using Machine Learning: Our Pioneer Experience

Comparison of Perioperative, Functional, and Oncologic Outcomes of Open vs. Robot-Assisted Off-Clamp Partial Nephrectomy: A Propensity Scored Match Analysis

Contact Info

Product

Resources

About