Approaches and Limitations of Machine Learning for Synthetic Ultrasound Generation A Scoping Review

• Published in: Journal of ultrasound in medicine Vol. 42; no. 12; pp. 2695 - 2706
• Main Authors: Mendez, Mauro, Sundararaman, Shruthi, Probyn, Linda, Tyrrell, Pascal N.
• Format: Journal Article
• Language: English
• Published: 01.12.2023
• ISSN: 0278-4297; 1550-9613
• DOI: 10.1002/jum.16332

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.