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

You might also read

Related Articles

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

Sort by
Same author

High mobility group box 1 (HMGB1) levels in the placenta and in serum in preeclampsia.

American journal of reproductive immunology (New York, N.Y. : 1989)·2011
Same author

Destabilization of coxsackievirus b3 genome integrated with enhanced green fluorescent protein gene.

Intervirology·2011
Same author

[Clinicopathological features of primary splenic histiocytic sarcoma: a case report and literature review].

Zhonghua xue ye xue za zhi = Zhonghua xueyexue zazhi·2011
Same author

[Comparison of treatment with micro endoscopic discectomy and posterior lumbar interbody fusion using single and double B-Twin expandable spinal spacer].

Zhonghua wai ke za zhi [Chinese journal of surgery]·2011
Same author

Virtual transplantation in designing a facial prosthesis for extensive maxillofacial defects that cross the facial midline using computer-assisted technology.

The International journal of prosthodontics·2011
Same author

Total synthesis of phorboxazole A via de novo oxazole formation: convergent total synthesis.

Journal of the American Chemical Society·2010

Related Experiment Video

Updated: Jul 9, 2025

Author Spotlight: Revolutionizing Remote Surgery with Augmented Reality and Robotics for Enhanced Precision and Accessibility
07:46

Author Spotlight: Revolutionizing Remote Surgery with Augmented Reality and Robotics for Enhanced Precision and Accessibility

Published on: August 9, 2024

748

Machine learning and decision making in aortic arch repair.

Rashmi Nedadur1, Nitish Bhatt1, Jennifer Chung1

  • 1Peter Munk Cardiac Center, Toronto General Hospital, Toronto, Ontario, Canada.

The Journal of Thoracic and Cardiovascular Surgery
|November 28, 2023
PubMed
Summary

Machine learning models can predict death and stroke risk in aortic arch surgery. These models personalize surgical strategies by analyzing patient data and intraoperative decisions for better outcomes.

Keywords:
Shapley additive explanationsXGBoostaortic arch surgeryhypothermic circulatory arrestmachine learningneuroprotection strategy

More Related Videos

Novel and Innovative Hybrid Technique for Type A Aortic Dissection
06:26

Novel and Innovative Hybrid Technique for Type A Aortic Dissection

Published on: March 28, 2025

294
O-Ring Aortic Banding Versus Traditional Transverse Aortic Constriction for Modeling Pressure Overload-Induced Cardiac Hypertrophy
09:24

O-Ring Aortic Banding Versus Traditional Transverse Aortic Constriction for Modeling Pressure Overload-Induced Cardiac Hypertrophy

Published on: October 6, 2022

3.5K

Related Experiment Videos

Last Updated: Jul 9, 2025

Author Spotlight: Revolutionizing Remote Surgery with Augmented Reality and Robotics for Enhanced Precision and Accessibility
07:46

Author Spotlight: Revolutionizing Remote Surgery with Augmented Reality and Robotics for Enhanced Precision and Accessibility

Published on: August 9, 2024

748
Novel and Innovative Hybrid Technique for Type A Aortic Dissection
06:26

Novel and Innovative Hybrid Technique for Type A Aortic Dissection

Published on: March 28, 2025

294
O-Ring Aortic Banding Versus Traditional Transverse Aortic Constriction for Modeling Pressure Overload-Induced Cardiac Hypertrophy
09:24

O-Ring Aortic Banding Versus Traditional Transverse Aortic Constriction for Modeling Pressure Overload-Induced Cardiac Hypertrophy

Published on: October 6, 2022

3.5K

Area of Science:

  • Cardiovascular Surgery
  • Medical Artificial Intelligence
  • Health Informatics

Background:

  • Aortic arch surgery requires careful decisions on cannulation and temperature to minimize risks.
  • Individualized, data-driven strategies are needed to optimize patient outcomes.
  • Machine learning (ML) offers a promising approach to model surgical risks.

Purpose of the Study:

  • To develop and evaluate machine learning models for predicting mortality and stroke risk in elective aortic arch surgery.
  • To identify key patient characteristics and intraoperative decisions influencing adverse events.
  • To compare the predictive performance of ML models against traditional logistic regression.

Main Methods:

  • A cohort of 1323 patients undergoing elective aortic arch procedures was analyzed.
  • Logistic regression and XGBoost ML models were trained using 69 variables.
  • Shapely additive explanations (SHAP) were used to assess the importance of intraoperative decisions.

Main Results:

  • XGBoost models showed superior discrimination for death (AUC 0.77) and stroke (AUC 0.87) compared to logistic regression.
  • Intraoperative decisions were among the top predictors for both mortality and stroke.
  • SHAP analysis revealed patient-specific predictor weights, highlighting the personalized nature of risk.

Conclusions:

  • Machine learning accurately identifies patients at high risk of death and stroke after aortic arch surgery.
  • ML models enable tailored operative decisions for personalized risk reduction.
  • This data-driven approach surpasses traditional prediction models in optimizing patient care.