IMAGENDO study

Imagendo is a research study using AI and imaging to diagnose endometriosis

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About IMAGENDO

Express your interest for our ADIMAGENDO workshops
Express your interest for our ADIMAGENDO workshops
Visit our Website
Visit our Website

Latest Publications

The problem with the ‘truth’: rethinking ground truth for artificial intelligence in endometriosis diagnosis
Global trends in the uptake of specialist diagnostic ultrasound and MRI scans for endometriosis: An international cross‐sectional survey - Avery - International Journal of Gynecology & Obstetrics - Wiley Online Library
Development of a Novel Machine Learning Model for Automatic Assessment of Quality of Transvaginal Ultrasound Images From Multi‐Annotator Labels - Deslandes - 2025 - Australasian Journal of Ultrasound in Medicine - Wiley Online Library
Unpaired multi-modal training and single-modal testing for detecting signs of endometriosis

IMAGENDO News

Is surgery necessary for my endometriosis or ‘suspected’ endo?
Is surgery necessary for my endometriosis or ‘suspected’ endo?
Three years for diagnosis — even when she knew what was wrong
Three years for diagnosis — even when she knew what was wrong
Radio Health Journal Podcast with Dr Jodie Avery
Radio Health Journal Podcast with Dr Jodie Avery
IMAGENDO reported by ABC news- read the story!
IMAGENDO reported by ABC news- read the story!
IMAGENDO reported by ABC news- watch the story!
IMAGENDO reported by ABC news- watch the story!
Yuan Zhang and IMAGENDO in Computer Vision News
Yuan Zhang and IMAGENDO in Computer Vision News

Our Publications

Are sonographers the future 'gold standard' in the diagnosis of endometriosis?
Human–AI collaborative multi-modal multi-rater learning for endometriosis diagnosis
Intra- and interobserver agreement of proposed objective transvaginal ultrasound image-quality scoring system for use in artificial intelligence algorithm development
Non-invasive diagnosis of endo: Part 2
Non-invasive diagnosis of endo: Part 1
AI as a teaching tool for gynae ultrasound
Uncertainty-Aware Multi-modal Learning via Cross-Modal Random Network Prediction
Deep Learning to diagnose POD obliteration with ultrasound

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