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

Predicting Chemotherapy Response from Staging Laparoscopy Images.

medRxiv : the preprint server for health sciences·2026
Same author

[Determination of mercury in seven sorts of disinfectant by water-bathmicrowave digestion-atomic fluorescence spectrometry].

Wei sheng yan jiu = Journal of hygiene research·2018
Same author

Intracranial Large Artery Disease of Non-Atherosclerotic Origin: Recent Progress and Clinical Implications.

Journal of stroke·2018
Same author

Updates on Prevention of Cardioembolic Strokes.

Journal of stroke·2018
Same author

Supplementation of p40, a Lactobacillus rhamnosus GG-derived protein, in early life promotes epidermal growth factor receptor-dependent intestinal development and long-term health outcomes.

Mucosal immunology·2018
Same author

Salinity mediates the toxic effect of nano-TiO<sub>2</sub> on the juvenile olive flounder Paralichthys olivaceus.

The Science of the total environment·2018

Related Experiment Video

Updated: Jun 29, 2025

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

6.8K

Development of a Deep Learning System for Intraoperative Identification of Cancer Metastases.

Thomas Schnelldorfer1,2,3, Janil Castro3, Atoussa Goldar-Najafi4

  • 1Division of Surgical Oncology, Tufts Medical Center, Boston, MA.

Annals of Surgery
|April 5, 2024
PubMed
Summary

A new deep learning surgical guidance system (CASL) shows promise in identifying peritoneal metastases during laparoscopy. This AI system outperformed surgeons in a simulated environment, potentially improving cancer staging and reducing unnecessary biopsies.

More Related Videos

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

2.7K
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

8.2K

Related Experiment Videos

Last Updated: Jun 29, 2025

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

6.8K
Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

2.7K
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

8.2K

Area of Science:

  • Oncology
  • Surgical Technology
  • Artificial Intelligence

Background:

  • Peritoneal surface metastases are a common cause of cancer recurrence after surgery.
  • Surgeons may misidentify these metastases during staging laparoscopy.
  • Improved intraoperative detection is crucial for effective cancer treatment.

Purpose of the Study:

  • To develop and evaluate a deep learning surgical guidance system (CASL) for intraoperative identification of peritoneal surface metastases.
  • To compare the diagnostic performance of CASL with oncologic surgeons.

Main Methods:

  • CASL was developed using laparoscopy images from 132 gastrointestinal adenocarcinoma patients.
  • The system analyzed 4287 visible lesions and 3650 image patches of biopsied lesions.
  • CASL performance was benchmarked against 111 oncologic surgeons in a simulated clinical setting.

Main Results:

  • Surgeons' accuracy in recommending biopsies for metastases was 52%.
  • CASL achieved a higher area under the ROC curve (0.78) compared to surgeons (0.69).
  • A combined human-computer approach yielded an AUC of 0.79, improving metastasis identification by 5% and reducing biopsies by 28%.

Conclusions:

  • The developed AI system offers a potential pathway for intraoperative identification of peritoneal metastases.
  • Further development and multi-institutional clinical validation are necessary.
  • CASL demonstrates potential to enhance surgical staging accuracy and treatment planning.