Development of a Novel Machine Learning Model for Automatic Assessment of Quality of Transvaginal Ultrasound Images From Multi‐Annotator Labels
This novel machine learning model offers an automated method of assessing the quality of TVUS images. The tool has the potential to provide real-time feedback to those performing TVUS, reduce the need for repeated imaging, and improve the diagnosis of gynaecological pathology.
Authors
- Alison Deslandes (ORCID: https://orcid.org/0000-0001-7094-3950)
- Hsiang‐Ting Chen (ORCID: https://orcid.org/0000-0003-0873-2698)
- G. Condous
- Jodie Avery (ORCID: https://orcid.org/0000-0002-8857-9162)
- Mathew Leonardi (ORCID: https://orcid.org/0000-0001-5538-6906)
- H. Wang
- M. Louise Hull (ORCID: https://orcid.org/0000-0003-1813-3971)
- Daniel Petashvili
- Gustavo Carnerio
Institutions
- Nepean Hospital (AU)
- Australian Institute of Business (AU)
- University of Surrey (GB)
- Australian Centre for Robotic Vision (AU)
- The University of Adelaide (AU)
- McMaster University (CA)
Publication Details
- Journal
- Australasian Journal of Ultrasound in Medicine
- Published
- 2025-09-22
- DOI
- https://doi.org/10.1002/ajum.70026
- Citations
- 1
- Primary Topic
- Colorectal Cancer Screening and Detection
- Type
- article
- Field-Weighted Citation Impact
- 1.05
Funders
- Australian Government
- University of Adelaide