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

Clinical utility of diagnosing lynch syndrome in gynecologic oncology: a joint statement from four japanese academic societies.

International journal of clinical oncology·2026
Same author

Metastatic Testicular Germ Cell Tumor in a Patient With Neurofibromatosis Type 1: Treatment With Trametinib Based on <i>NF1</i> Gene Mutation.

IJU case reports·2026
Same author

AI-Based Analysis of Tumor-Infiltrating Lymphocytes and Homologous Recombination in Ovarian Cancer: JGOG3025-A1 Study.

Cancer science·2026
Same author

Tokishakuyakusan suppresses endometriosis and may be associated with improved fertility during attempts to conceive: A mouse model study.

Journal of reproductive immunology·2026
Same author

Early-onset pyomyoma mimicking post-embolization syndrome after uterine artery embolization: a case report and review of the literature.

Journal of infection and chemotherapy : official journal of the Japan Society of Chemotherapy·2026
Same author

Deep learning for intraoperative recognition of critical structures in total hysterectomy.

Surgical endoscopy·2026

Related Experiment Video

Updated: Aug 21, 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

6.9K

Development of a deep learning method for improving diagnostic accuracy for uterine sarcoma cases.

Yusuke Toyohara1, Kenbun Sone2, Katsuhiko Noda3

  • 1Department of Obstetrics and Gynecology, Graduate School of Medicine, Faculty of Medicine, The University of Tokyo, 7-3-1 Hongo Bunkyo-Ku, Tokyo, 113-8655, Japan.

Scientific Reports
|November 17, 2022
PubMed
Summary

Deep neural network (DNN) models enhance preoperative MRI accuracy for uterine sarcomas, improving diagnosis. This AI approach aids radiologists, especially practitioners, in distinguishing rare tumors from leiomyomas.

More Related Videos

Non-Invasive Ultrasound Assessment of Endometrial Cancer Progression in Pax8-Directed Deletion of the Tumor Suppressors Arid1a and Pten in Mice
07:44

Non-Invasive Ultrasound Assessment of Endometrial Cancer Progression in Pax8-Directed Deletion of the Tumor Suppressors Arid1a and Pten in Mice

Published on: February 17, 2023

1.6K
A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images
04:23

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images

Published on: April 21, 2023

1.9K

Related Experiment Videos

Last Updated: Aug 21, 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

6.9K
Non-Invasive Ultrasound Assessment of Endometrial Cancer Progression in Pax8-Directed Deletion of the Tumor Suppressors Arid1a and Pten in Mice
07:44

Non-Invasive Ultrasound Assessment of Endometrial Cancer Progression in Pax8-Directed Deletion of the Tumor Suppressors Arid1a and Pten in Mice

Published on: February 17, 2023

1.6K
A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images
04:23

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images

Published on: April 21, 2023

1.9K

Area of Science:

  • Radiology
  • Artificial Intelligence
  • Oncology

Background:

  • Uterine sarcomas present diagnostic challenges, often mimicking uterine leiomyomas on preoperative imaging.
  • Accurate preoperative diagnosis is crucial for effective treatment and prognosis of uterine sarcomas.

Purpose of the Study:

  • To evaluate the efficacy of deep neural network (DNN) models in improving the accuracy of preoperative MRI-based diagnosis for uterine sarcomas.
  • To assess the diagnostic performance of DNN models compared to human radiologists with varying expertise levels.

Main Methods:

  • DNN models were trained using 15 MRI sequences from 63 uterine sarcoma and 200 uterine leiomyoma patients.
  • Six radiologists (3 specialists, 3 practitioners) interpreted the MRI images for validation.
  • Diagnostic accuracy was compared between radiologists with and without DNN assistance.

Main Results:

  • The combination of axial T2WI, sagittal T2WI, and diffusion-weighted imaging achieved 91.3% accuracy, surpassing practitioners (80.1%) and nearing specialists (88.3%).
  • Radiologists' accuracy improved with DNN assistance: specialists to 89.6% and practitioners to 92.3%.
  • DNN models demonstrated significant value in enhancing diagnostic accuracy and bridging skill gaps among interpreters.

Conclusions:

  • DNN models offer a valuable tool for improving preoperative MRI diagnosis of uterine sarcomas.
  • This AI-driven approach can standardize diagnostic accuracy, particularly for rare tumors.
  • The developed DNN models show potential as a universal tool for deep learning in rare tumor imaging.