| Title: |
Approaches and Limitations of Machine Learning for Synthetic Ultrasound Generation ; A Scoping Review |
| Authors: |
Mendez, Mauro; Sundararaman, Shruthi; Probyn, Linda; Tyrrell, Pascal N. |
| Contributors: |
Novo Nordisk |
| Source: |
Journal of Ultrasound in Medicine ; volume 42, issue 12, page 2695-2706 ; ISSN 0278-4297 1550-9613 |
| Publisher Information: |
Wiley |
| Publication Year: |
2023 |
| Collection: |
Wiley Online Library (Open Access Articles via Crossref) |
| Description: |
This scoping review examines the emerging field of synthetic ultrasound generation using machine learning (ML) models in radiology. Nineteen studies were analyzed, revealing three primary methodological strategies: unconditional generation, conditional generation, and domain translation. Synthetic ultrasound is mainly used to augment training datasets and as training material for radiologists. Blind expert assessment and Fréchet Inception Distance are common evaluation methods. Current limitations include the need for large training datasets, manual annotations for controllable generation, and insufficient research on incorporating new domain knowledge. While generative ultrasound models show promise, further work is required for clinical implementation. |
| Document Type: |
article in journal/newspaper |
| Language: |
English |
| DOI: |
10.1002/jum.16332 |
| Availability: |
https://doi.org/10.1002/jum.16332; https://onlinelibrary.wiley.com/doi/pdf/10.1002/jum.16332 |
| Rights: |
http://creativecommons.org/licenses/by-nc-nd/4.0/ |
| Accession Number: |
edsbas.CAED291A |
| Database: |
BASE |