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

Associative Learning01:27

Associative Learning

575
Associative learning is a fundamental concept in behavioral psychology, wherein a connection is established between two stimuli or events, leading to a learned response. This process is critical in understanding how behaviors are acquired and modified. Conditioning, the mechanism through which associations are formed, can be divided into two main types: classical conditioning and operant conditioning, each elucidating different aspects of associative learning.
Classical conditioning, also known...
575

You might also read

Related Articles

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

Sort by
Same author

Machine learning models for non-invasive endometriosis triage using a laparoscopically and histologically verified cohort.

European journal of obstetrics, gynecology, and reproductive biology·2026
Same author

Coexisting adenomyosis in endometriosis: Similar pain and quality of life, with longer bleeding and bowel symptoms-A comparative cross-sectional study.

International journal of gynaecology and obstetrics: the official organ of the International Federation of Gynaecology and Obstetrics·2026
Same author

Treatment of ovarian endometriosis: Number 1 - 2026.

Revista brasileira de ginecologia e obstetricia : revista da Federacao Brasileira das Sociedades de Ginecologia e Obstetricia·2026
Same author

Optimal Laparoscopic Surgical Technique for Preserving Fertility and Ovarian Reserve in Patients with Endometrioma: A Systematic Review and Bayesian Network Meta-Analysis of Randomized Controlled Trials.

Journal of minimally invasive gynecology·2026
Same author

Noninvasive Imaging Diagnostics for Endometriosis.

Seminars in reproductive medicine·2026
Same author

Surgical Complexity, Disease Severity, and Direct Healthcare Costs of Endometriosis in the Brazilian Public Health System: A Cross-Sectional Analysis.

Journal of minimally invasive gynecology·2025

Related Experiment Video

Updated: Sep 12, 2025

Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping
10:25

Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping

Published on: September 25, 2019

48.4K

Uncovering Symptom-Lesion Associations Through Machine Learning.

Renato Silva Santana1, Renato Sátiro1, Henrique M Abrão2

  • 1Superior School of Agriculture Luiz de Queiroz-ESALQ, Universidade de São Paulo (Drs. Santana and Sátiro), São Paulo, São Paulo, Brazil.

Journal of Minimally Invasive Gynecology
|August 9, 2025
PubMed
Summary

Machine learning identified distinct endometriosis patient profiles based on symptoms and lesion locations. This analysis can help predict disease sites and guide surgical planning for endometriosis.

Keywords:
EndometriosisLesion siteMachine learningMultiple correspondence analysisSymptoms

More Related Videos

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
14:27

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

15.8K
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

8.3K

Related Experiment Videos

Last Updated: Sep 12, 2025

Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping
10:25

Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping

Published on: September 25, 2019

48.4K
Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
14:27

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

15.8K
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

8.3K

Area of Science:

  • Gynecologic Oncology
  • Reproductive Medicine
  • Medical Informatics

Background:

  • Endometriosis is a complex gynecological condition characterized by endometrial-like tissue outside the uterus.
  • The relationship between specific symptoms and the anatomical location of endometriosis lesions is not fully understood.
  • Accurate localization of lesions is crucial for effective surgical management.

Purpose of the Study:

  • To investigate the association between endometriosis symptoms and lesion sites using machine learning.
  • To identify distinct clinical profiles within endometriosis patients.
  • To explore the potential of symptom patterns in predicting lesion locations.

Main Methods:

  • Retrospective study of 726 patients with histologically confirmed endometriosis.
  • Analysis of clinical data using Multiple Correspondence Analysis (MCA), a machine learning technique.
  • Evaluation of associations between pre-operative symptoms and lesion locations identified during laparoscopic surgery.

Main Results:

  • MCA revealed three distinct patient profiles explaining 56.9% of the data variance.
  • Profile 1: Severe pain (dysmenorrhea, dyspareunia, acyclic pelvic pain) linked to retrocervical and rectosigmoid lesions.
  • Profile 2: Infertility associated with lower pain and ovarian, tubal, and pararectal lesions.
  • Profile 3: Mild-to-moderate pain not clearly linked to specific lesion sites.

Conclusions:

  • Machine learning analysis successfully identified distinct clinical profiles in endometriosis.
  • Symptom patterns are associated with specific anatomical locations of endometriosis lesions.
  • Symptom profiling offers a probabilistic framework to complement diagnostic reasoning and surgical planning in endometriosis care.