by the IMAGENDO® team

Upcoming in 2024

(March) Society for Reproductive Investigation’s 71st Annual Scientific Meeting: Avery J et al. (Poster) “IMAGENDO Endometriosis Diagnosis Using Artificial Intelligence: Improving Detection with Magnetic Resonance Imaging, Leveraging Unpaired Endometriosis Ultrasounds”. Vancouver.

(April) The American Institute of Ultrasound in Medicine UltraCon: Avery J et al. (Oral) “Combining Unpaired Endometriosis Ultrasounds and Magnetic Resonance Imaging Using Artificial Intelligence, To Detect Pouch of Douglas Obliteration for Endometriosis Diagnosis: The IMAGENDO Study”. Austin, USA.

(May) The 21st IEEE International Symposium on Biomedical Imaging (ISBI): Petashvilli D, Wang H, Avery J, et al. “Learning Subjective Image Quality Assessment for Transvaginal Ultrasound Scans from Multi-Rater Labels”. Athens.

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”


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


Zhang Y, Avery JC “A multimodal Artificial Intelligence analysis of endometriosis imaging markers”


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.

20th IEEE International Symposium on Biomedical Imaging (ISBI): Zhang Y, Wang H, Butler D, To MS. Avery J, Hull ML, Carneiro G (Oral) “Distilling Missing Modality Knowledge from Ultrasound for Endometriosis Diagnosis with Magnetic Resonance Images”. Colombia.

The Abdominal Radiology Group of Australia and New Zealand (ARGANZ) White S, Knox S, Avery JC, Hull ML, Wang H, Zhang Y, Carneiro G, To MS “Development and validation of a machine learning system for automated routine 2-dimensional morphometric measurements on female pelvic MRI”. Adelaide.

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.

45th IEEE Engineering in Medicine and Biology Society (EMBC) Butler D, Wang H, Zhang Y, To MS, Condous G, Leonardi M, Knox S, Avery J, Hull ML, Carneiro G (Oral) “The Effectiveness of Self-supervised Pre-training for Multi-modal Endometriosis Classification”. Sydney.

Robinson Research Institute Symposium
Endometriosis Research Team


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.


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”.