Presentations

by the IMAGENDO® team

World Congress on Endometriosis 2023

Oral Presentation

Avery J, Leonardi M, Abeygunasekara N, Bliss E, Johnson N, Condous G, Wang R, Hull ML “Imaging modalities for the non-invasive diagnosis of Endometriosis: A Cochrane review update”

Oral Presentation

Deslandes A, Avery J, Chen T, Leonardi M, Knox S, Pannucio C, Hull ML, Condous C “A quantitative grading system for the assessment TVUS image quality”

Poster

Abeygunasekara N, O Hara R, Gonzalez-Chica D, March L, Fairweather, Hull ML, Avery J “General Practitioners Perspectives on the Diagnostic Delay of Endometriosis”

2023

Australian Society for Ultrasound in Medicine (ASUM) Maicas G, Leonardi M, Avery J (Invited speaker), Panuccio C, Carneiro G, Hull ML, Condous G “Enhancing the detection of Pouch of Douglas obliteration for endometriosis diagnosis with Artificial Intelligence, using magnetic resonance imaging and unpaired endometriosis ultrasounds”. Sydney.

12th Congress of the Asia Pacific Initiative on Reproduction (ASPIRE) Avery J, Pirotta S, Jiskoot G, Gibson-Helm M (Oral) “The “HERAQoL-P”: Development of a Meaningful, International Quality of Life Tool for PCOS”. Adelaide

South Australian Rural Health Education and Research Conference (SARHRE) Avery J, O Hara R, Abeygunasekara N, Gonzalez-Chica D, Hull ML (Workshop) “Triagendo: Increasing health professional awareness of endometriosis”. Barossa Valley.

Conference on Computer Vision and Pattern Recognition (CVPR) Wang H, Chen Y, Ma C, Avery J, Hull ML, Carneiro G (Oral) “Multi-modal Learning with Missing Modality via Shared-Specific Feature Modelling”. Barossa Valley.

Robinson Research Institute Symposium
Endometriosis Research Team

2022

RANZCOG Annual Scientific Meeting
Avery J, Hull ML, Carneiro G, Condous G, Abbott J,
Leonardi M, Wang H, O Hara R, Sirop A “IMAGENDO – non-invasive diagnosis of endometriosis using machine learning”. Gold Coast.

RCOG – BSGI Meeting Hull ML, Avery J, Condous G, Leonardi M, Zhang Y, Wang H, Carneiro G (Oral and Poster) “IMAGENDO: Combining ultrasound and magnetic resonance imaging using artificial intelligence to reduce diagnostic delay”. London (Virtual)

Australian Society for Ultrasound in Medicine (ASUM) Avery J, Deslandes A, Leonardi M, Condous G, Carneiro G, Hull ML (Oral and Poster) “Imagendo® – Non-invasive diagnosis of endometriosis using machine learning”. Adelaide.

European Conference on Computer Vision (ECCV)
Wang H, Zhang J, Chen Y, Ma C, Avery J, Hull L, Gustavo C “Uncertainty-aware Multi-modal Learning via Cross-modal Random Network Prediction”. Tel Aviv.

2020

World Congress on Endometriosis (WCE) Maicas G, Condous G, Leonardi M, Avery J, Panuccio C, Carneiro G, Hull ML (Oral) “Artificial Intelligence for Sliding Sign Detection to Diagnose Endometriosis”.

ISUOG Virtual World Congress Leonardi M, Maicas G, Avery J, Panuccio C, Carneiro G, Hull ML, Condous G (Poster) “Machine learning to diagnose rectouterine pouch obliteration with the sliding sign on transvaginal ultrasound”.