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 Experiment Video

Updated: May 21, 2026

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

Quantum machine learning for predicting anastomotic leak: a clinical study.

Vojtěch Novák1,2, Ivan Zelinka3,4, Lenka Přibylová5

  • 1Department of Computer Science, VSB-Technical University of Ostrava, Ostrava, Czech Republic. vojtech.novak.st1@vsb.cz.

Scientific Reports
|May 19, 2026
PubMed
Summary

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

A Dataset of Abdominal CT with Artery and Vein Segmentations for Colorectal Cancer Surgical Planning.

Scientific data·2026
Same author

Astrocyte-induced dynamics of a pyramidal cell with a dendrite-connected astrocyte.

Journal of computational neuroscience·2026
Same author

Radicality of mediastinal lymphadenectomy in anatomic lung resection for lung cancer: a comparative analysis of uniportal video-assisted thoracoscopic and robotic-assisted thoracoscopic approaches.

Surgical endoscopy·2026
Same author

Therapeutic role of venous sinus stenting in pediatric IIH: evidence review for clinical practice.

Acta neurochirurgica·2025
Same author

Pain and recovery after robotic vs. uniportal lobectomy for lung cancer: a comparative analysis.

Surgical endoscopy·2025
Same author

Rethinking lumbar puncture safety: pathophysiology, diagnostic uncertainty, and research gaps in herniation risk.

Acta neurologica Belgica·2025

Quantum Neural Networks (QNNs) show promise in predicting anastomotic leaks after colorectal surgery, offering higher specificity than classical models. Further validation is needed for clinical screening applications.

Area of Science:

  • Computational Science
  • Medical Informatics
  • Quantum Computing

Background:

  • Anastomotic leak is a critical complication after colorectal surgery.
  • Accurate prediction of anastomotic leaks is essential for patient outcomes.

Purpose of the Study:

  • To benchmark Quantum Neural Networks (QNNs) against classical machine learning models for anastomotic leak prediction.
  • To evaluate the performance of QNNs under realistic noise conditions.

Main Methods:

  • A 200-patient clinical dataset was used.
  • Simulated QNNs with ZZFeatureMap encoding and EfficientSU2/RealAmplitudes ansatze were employed.
  • Performance was compared against logistic regression, multi-layer perceptrons, and boosting algorithms.

More Related Videos

Creation of Colonic Anastomosis in Mice
07:22

Creation of Colonic Anastomosis in Mice

Published on: January 17, 2019

Related Experiment Videos

Last Updated: May 21, 2026

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

Creation of Colonic Anastomosis in Mice
07:22

Creation of Colonic Anastomosis in Mice

Published on: January 17, 2019

Main Results:

  • Specific QNN configurations demonstrated superior specificity and Negative Predictive Value compared to classical models at a clinically relevant sensitivity.
  • EfficientSU2-BFGS achieved the highest Area Under the Curve (AUC).
  • RealAmplitudes with CMA-ES maximized Average Precision, though classical models offered better probability calibration.

Conclusions:

  • QNNs exhibit robust discriminative performance for clinical screening of anastomotic leaks.
  • QNNs effectively minimize false positives, a crucial factor in clinical decision-making.
  • Larger, independent cohorts are required for further QNN validation in this domain.