Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Kidney Transplant II: Surgical Procedure01:26

Kidney Transplant II: Surgical Procedure

222
Preoperative ManagementThe primary goals of preoperative management in kidney transplantation are to optimize the patient’s metabolic state and prepare them for surgery through diet adjustments, necessary dialysis, and tailored medical treatment. This phase also involves comprehensive infection screening and patient education about the surgical procedure and postoperative care to improve outcomes and adherence.Medical ManagementA comprehensive evaluation is required for both the living...
222

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Ten-year update on the European Association of Urology Robotic Section (ERUS) fellowship training for robot-assisted radical prostatectomy.

BJUI compass·2026
Same author

External validation of the preoperative risk evaluation for partial nephrectomy (PREP) score.

BJUI compass·2026
Same author

External validation of the European association of urology biochemical recurrence risk groups to predict mortality after radical prostatectomy or radiation therapy in a North American cohort.

BJUI compass·2026
Same author

Confocal laser microscopy in urology: leading the adoption of real-time surgical pathology.

BJU international·2026
Same author

The need for a dedicated surgical consent framework for robotic telesurgery: a global call to action.

Journal of robotic surgery·2026
Same author

Delphi consensus on ex vivo fluorescence confocal microscopy in robot-assisted radical prostatectomy.

BJU international·2026

Related Experiment Video

Updated: Dec 23, 2025

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

8.6K

Predicting intra-operative and postoperative consequential events using machine-learning techniques in patients

Mahendra Bhandari1, Anubhav Reddy Nallabasannagari2, Madhu Reddiboina2

  • 1Vattikuti Urology Institute, Henry Ford Hospital, Detroit, MI, USA.

BJU International
|April 22, 2020
PubMed
Summary

Machine-learning models accurately predict intra-operative events (IOEs) and postoperative events (POEs) in patients undergoing robot-assisted partial nephrectomy. These models show promise for improving patient outcomes by enabling timely interventions.

Keywords:
deep learningintra-operative complicationsmachine learningpostoperative complicationspostoperative morbidityrobot-assisted partial nephrectomy

More Related Videos

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

7.3K
Robot-Assisted Kidney Transplantation
07:30

Robot-Assisted Kidney Transplantation

Published on: July 19, 2021

4.1K

Related Experiment Videos

Last Updated: Dec 23, 2025

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

8.6K
Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

7.3K
Robot-Assisted Kidney Transplantation
07:30

Robot-Assisted Kidney Transplantation

Published on: July 19, 2021

4.1K

Area of Science:

  • Urology
  • Surgical Oncology
  • Artificial Intelligence in Medicine

Background:

  • Robot-assisted partial nephrectomy is a common procedure for kidney tumors.
  • Predicting intra-operative events (IOEs) and postoperative events (POEs) is crucial for patient recovery.
  • Current methods for predicting surgical complications have limitations.

Purpose of the Study:

  • To develop and evaluate machine-learning (ML) models for predicting IOEs and POEs after robot-assisted partial nephrectomy.
  • To assess the performance of ML models using various metrics like AUC-ROC and PR-AUC.
  • To explore the potential clinical utility of ML in improving surgical outcomes.

Main Methods:

  • Utilized the Vattikuti Collective Quality Initiative multi-institutional dataset.
  • Constructed ML models (logistic regression, random forest, neural networks) to predict IOEs and POEs.
  • Employed patient demographics, preoperative, and intra-operative data for model training and validation.

Main Results:

  • IOE and POE rates were 5.62% and 20.98%, respectively.
  • The best IOE prediction model achieved an AUC-ROC of 0.858 and PR-AUC of 0.590.
  • The best POE prediction model achieved an AUC-ROC of 0.875 and PR-AUC of 0.706.

Conclusions:

  • ML models demonstrated encouraging performance in predicting surgical events.
  • Further validation with larger, multi-institutional datasets is needed.
  • These ML models have the potential for clinical application to avert complications and enhance patient outcomes.