ANZCTR - Registration

Please note the ANZCTR will be unattended from Friday 20 December 2024 for the holidays. The Registry will re-open on Tuesday 7 January 2025. Submissions and updates will not be processed during that time.

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Trial registered on ANZCTR

Registration number

ACTRN12623000646640

Ethics application status

Approved

Date submitted

22/03/2022

Date registered

15/06/2023

Date last updated

15/06/2023

Date data sharing statement initially provided

15/06/2023

Type of registration

Retrospectively registered

Titles & IDs

Public title

IMAGENDO: Diagnosing Endometriosis with Imaging and Artificial Intelligence

Scientific title

Non-invasive endometriosis diagnosis in women using machine learning

Secondary ID [1]

PHRDI000014

Universal Trial Number (UTN)

Trial acronym

IMAGENDO

Linked study record

Health condition

Health condition(s) or problem(s) studied:

endometriosis

Condition category

Condition code

Reproductive Health and Childbirth

Other reproductive health and childbirth disorders

Intervention/exposure

Study type

Observational

Patient registry

False

Target follow-up duration

Target follow-up type

Description of intervention(s) / exposure

There are two study arms:
Stage 1 - retrospective study - Women who have had an MRI or MRI and TV-US in the last 5 years will be contacted directly by mail by administration staff from our partner radiology and ultrasound clinics (including Benson Radiology, Specialist Imaging Partners, OmniGynaecare, O &G ) to obtain consent for these images and to follow up on any operation notes to confirm diagnosis. It is anticipated that it will take approximately 25 – 35 mins for the participant to complete their baseline data collection. Baseline data collection includes date of birth, height and weight, any previous surgery, any previous diagnostic imaging, Treating Specialist Doctor / Gynaecologist, Surgeon name and Hospital. Images and operation notes are de identified by admin staff from the clinic before being sent to the computer analysts. Participants will only need to read information and provide consent, and supply baseline information including date of birth, height and weight, any previous surgery, any previous diagnostic imaging, Treating Specialist Doctor / Gynaecologist, Surgeon name and Hospital. Once imaging and operation notes are received there will be no further observation. Images and medical history details will be entered into a machine learning algorithm designed specifically for this study. We will then compare the results from the algorithm to the documented diagnosis.

Stage 2: After being identified as eligible for the study, if they haven’t already had one, women will be invited to undertake a transvaginal endometriosis ultrasound scan and/or an MRI at one of the imaging partners. These scans take less than an hour to complete. They will then attend a follow up interview with their Gynaecologist at least one week prior to their surgery, for 15 minutes who will explain the findings of the scans. They will also attend a review appointment within one month after their surgery with their Gynaecologist. Operation notes will be accessed by the study team after surgery.
When consenting for the study, women will also be asked if they are willing to have their contact details including their name, date of birth, address, phone number, email address and treating specialist doctor entered on a secure electronic database and be contacted about future research questions that might arise from this project.
It is anticipated that it will take approximately 25 – 35 mins for the participant to complete their baseline data collection. Baseline data collection includes date of birth, height and weight, any previous surgery, any previous diagnostic imaging, Treating Specialist Doctor / Gynaecologist, Surgeon name and Hospital. The observation period will end at the follow up review appointment post surgery.

Intervention code [1]

Diagnosis / Prognosis

Comparator / control treatment

Laporascopic surgery will be used as a comparison for both Stage 1 and Stage 2. Surgical notes will be obtained directly from surgical clinics. No active involvement from participants will be required post surgery. The oveerall duration of observation for patients will be from imaging diagnosis until surgical notes are obtained.

Control group

Active

Outcomes

Primary outcome [1]

Diagnostic Test Accuracy for Stage 1:
Machine Learning algorithm (Transvaginal Ultrasound & MRI) compared with laparoscopic surgery for endometriosis - Composite sensitivities, specificities, diagnostic accuracy, PPV, NPV and the AUC will be calculated

Timepoint [1]

6 months after participant enrolment in study

Primary outcome [2]

Diagnostic Test Accuracy for Stage 2:
Machine Learning algorithm (Transvaginal Ultrasound & MRI) compared with laparoscopic surgery for endometriosis - Composite Sensitivities, specificities, diagnostic accuracy, PPV, NPV and the AUC will be calculated

Timepoint [2]

2 years after participant enrolment in study

Secondary outcome [1]

Time elapsed between imaging and surgery - sugical data will be requested directly from surgeons after patient consent.

Timepoint [1]

2 years after patient enrolment

Eligibility

Key inclusion criteria

Women with symptoms of endometriosis including:
o Period paid
o Other chronic pelvic pain
o Fatigue
o Dysmenorrhoea,
o Dyspareunia,
o Difficulty conceiving
• Women do not need to have regular menstrual cycles, and can be taking oral contraceptive or have a Mirena in place

Minimum age

18 Years

Maximum age

45 Years

Sex

Females

Can healthy volunteers participate?

Key exclusion criteria

• Women with cancer
• Women with bowel conditions such as Crohn’s Disease or Ulcerative Colitis
• Postmenopausal women
• Women with an intellectual disability/inability to give informed consent

Study design

Purpose

Screening

Duration

Cross-sectional

Selection

Defined population

Timing

Both

Statistical methods / analysis

As Machine learning is dependant on obtaining as mcuh data as possible, we have not coalbulated a sample size.
Composite Sensitivities, specificities, diagnostic accuracy, PPV, NPV and the AUC will be calculated

Recruitment

Recruitment status

Recruiting

Date of first participant enrolment

Anticipated

Actual

5/11/2020

Date of last participant enrolment

Anticipated

30/06/2024

Actual

Date of last data collection

Anticipated

30/06/2025

Actual

Sample size

Target

1000

Accrual to date

350

Final

Recruitment in Australia

Recruitment state(s)

ACT,NSW,NT,QLD,SA,TAS,WA,VIC

Recruitment outside Australia

Country [1]

Canada

State/province [1]

Ontario

Funding & Sponsors

Funding source category [1]

Charities/Societies/Foundations

Name [1]

Australian Gynaecological Endoscopy Society (AGES)

Address [1]

YRD Event Management
PO Box 717 Indooroopilly
QLD 4068 AUSTRALIA

Country [1]

Australia

Funding source category [2]

Charities/Societies/Foundations

Name [2]

Endometriosis Australia

Address [2]

C/O Weston Woodley & Robertson

PO Box 1070 North Sydney

NSW 2059 Australia

Country [2]

Australia

Funding source category [3]

Government body

Name [3]

Australian Government (MRFF 2020 Primary Health Care Research Data Infrastructure Grant)

Address [3]

Department of Industry, Science, Energy and Resources
10 Binara Street
CANBERRA ACT 2600

Country [3]

Australia

Funding source category [4]

Charities/Societies/Foundations

Name [4]

Australasian Society of Ultrasound in Medicine

Address [4]

Suite 3.01, 9 Help St
Chatswood NSW 2067 Australia

Country [4]

Australia

Primary sponsor type

University

Name

Robinson Research Institute, University of Adelaide

Address

Norwich House
Ground Floor, 55 King William Rd
North Adelaide SA, 5006

Country

Australia

Secondary sponsor category [1]

University

Name [1]

Australian Institute of Machine Learning, University of Adelaide

Address [1]

Corner Frome Road and, North Terrace, Adelaide SA 5000

Country [1]

Australia

Ethics approval

Ethics application status

Approved

Ethics committee name [1]

University of Adelaide Human Research Ethics Committee

Ethics committee address [1]

Office of Research Ethics, Compliance and Integrity
The University of Adelaide
Level 4, Rundle Mall Plaze
50 Rundle Mall
Adelaide SA 5000 Australia

Ethics committee country [1]

Australia

Date submitted for ethics approval [1]

15/08/2019

Approval date [1]

23/04/2020

Ethics approval number [1]

H-2020-051

Summary

Brief summary

Endometriosis is a chronic, inflammatory condition which can lead to chronic pelvic pain and infertility. There is no cure for this condition and the gold standard for diagnosis is laparoscopy (keyhole surgery) which is costly, has long wait times and is associated with risks. This study (Imagendo) will use artificial intelligence to create a diagnostic algorithm by analysing ultrasound and MRI endometriosis scans, providing general practitioners with an earlier, easily accessed, non-invasive, diagnosis of endometriosis.

Trial website

www.imagendo.org.au

Trial related presentations / publications

Public notes

Presentations: 2023: Australian Society for Ultrasound in Medicine (ASUM): Sydney, Avery J et al. (Invited speaker) Enhancing the detection of Pouch of Douglas obliteration for endometriosis diagnosis with Artificial Intelligence, using magnetic resonance imaging and unpaired endometriosis ultrasounds.
2023: 12th Congress of the Asia Pacific Initiative on Reproduction (ASPIRE 2023) Adelaide. Avery J et al (Oral) EXTRAPOLATING ENDOMETRIOSIS DIAGNOSIS USING IMAGING AND MACHINE LEARNING: THE IMAGENDO PROJECT
2023: 20th IEEE International Symposium on Biomedical Imaging (ISBI): Colombia: Zhang Y, et al. (Oral) “Distilling Missing Modality Knowledge from Ultrasound for Endometriosis Diagnosis with Magnetic Resonance Images”. Winner: best Oral Presentation
2023: World Congress on Endometriosis (WCE),  Zhang Y, Avery JC (Presenter), et al Oral: A multimodal AI analysis of endometriosis imaging markers”.
Posters: Deslandes A,  et al A quantitative grading system for the assessment TVUS image quality
2023: ARGANZ Adelaide, White S,  et al (Poster) Development and validation of a machine learning system for automated routine 2-dimensional morphometric measurements on female pelvic MRI
2023: Computer Vision and Pattern Recognition Conference (CVPR 2023) Wang H, et al. (Oral) "Multi-modal Learning with Missing Modality via Shared-Specific Feature Modeling".
45th IEEE Engineering in Medicine and Biology Society, (EMBC 23) Sydeney, Butler D,  et al.(Oral) “The Effectiveness of Self-supervised Pre-training for Multi-modal Endometriosis Classification”
2022: RANZCOG Meeting Gold Coast, Avery J et al IMAGENDO – non-invasive diagnosis of endometriosis using machine learning
11th Congress of the Asia Pacific Initiative on Reproduction ASPIRE: Warner R, Avery J et al (Oral) Associations between environmental exposures in the middle east area of operations and reproductive outcomes in Australian defence force veterans
2022: RCOG – BSGI Meeting Hull L, Avery J et al. Oral and Poster IMAGENDO: Combining ultrasound and magnetic resonance imaging using artificial intelligence to reduce diagnostic delay. (Virtual) London.
2022: Australian Society for Ultrasound in Medicine (ASUM) Meeting Adelaide Avery J. (Poster) Imagendo® – Non-Invasive diagnosis of endometriosis using machine learning. (Oral)
2022: European Conference on Computer Vision. Uncertainty-aware Multi-modal Learning via Cross-modal Random Network Prediction Wang, H. Avery J, et al. Uncertainty-Aware Multi-modal Learning via Cross-Modal Random Network Prediction.
2021: World Congress on Endometriosis (WCE) Maicas G, Avery J et al (Oral) Artificial Intelligence for Sliding Sign Detection to Diagnose Endometriosis.
2020: ISUOG Virtual World Congress Leonardi M, Avery J, et al (Poster) Machine learning to diagnose rectouterine pouch obliteration with the sliding sign on transvaginal ultrasound. 2020 UOG 56(S1)

Publications:
Butler D, Wang H, Zhang Y, To MS, Avery JC, Hull ML, Carneiro G. The Effectiveness of Self-supervised Pre-training for Multi-modal Endometriosis Classification, Proceedings of the 45th IEEE Engineering in Medicine and Biology Society, 2023 In Press.

H Wang, Y Chen, C Ma, AVERY J, L Hull, G Carneiro
Multi-Modal Learning With Missing Modality via Shared-Specific Feature Modelling. - Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp15878-15887 2023

Zhang Y, Wang H, Avery J, et al. “Distilling Missing Modality Knowledge from Ultrasound for Endometriosis Diagnosis with Magnetic Resonance Images”. Proceedings of 20th IEEE International Symposium on Biomedical Imaging (ISBI). In Press. https://ieeexplore.ieee.org/xpl/conhome/1000080/all-proceedings

Wang H, Zhang J, Chen Y, Ma C, AVERY J, Hull L, Carneiro G, Uncertainty-Aware Multi-modal Learning via Cross-Modal Random Network Prediction. Computer Vision–ECCV 2022: 17th European Conference, Tel Aviv, Israel, October 23–27, 2022, Proceedings, Part XXXVII, Pp 200-217

Leonardi M, Macais G, Avery J, Panuccio C. Carneiro G, Hull ML, Condous G. Machine learning to diagnose rectouterine pouch obliteration with the sliding sign on transvaginal ultrasound. ISUOG Virtual World Congress 2020 Ultrasound in Obstetrics and Gynecology 56(S1) doi: 10.1002/uog.23327

Maicas G, Leonardi M, Avery J, Panuccio C, Carneiro G, Hull ML, Condous G. Deep learning to diagnose pouch of Douglas obliteration with ultrasound sliding sign. Reprod Fertil. 2021 Aug 25;2(4):236-243. doi: 10.1530/RAF-21-0031. eCollection 2021 Dec.

Contacts

Principal investigator

Name

Prof Mary Louise Hull

Address

Robinson Research Institute
Ground Floor, 55 King William Rd
North Adelaide SA 5006

Country

Australia

Phone

+61403933312

Fax

louise.hull@adelaide.edu.au

Contact person for public queries

Name

Jodie C Avery

Address

Robinson Research Institute
Ground Floor, 55 King William Rd
North Adelaide SA 5006

Country

Australia

Phone

+61410519941

Fax

jodie.avery@adelaide.edu.au

Contact person for scientific queries

Name

Jodie C Avery

Address

Robinson Research Institute
Ground Floor, 55 King William Rd
North Adelaide SA 5006

Country

Australia

Phone

+61410519941

Fax

jodie.avery@adelaide.edu.au

Data sharing statement

Will individual participant data (IPD) for this trial be available (including data dictionaries)?

No/undecided IPD sharing reason/comment

What supporting documents are/will be available?

No Supporting Document Provided

Results publications and other study-related documents

Documents added manually

No documents have been uploaded by study researchers.

Documents added automatically

No additional documents have been identified.

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