Ultrasound video analysis for skill level assessment in FAST ultrasound

ABSTRACT

FAST ultrasound is a medical procedure to assess for free fluid following physical trauma. FAST images can often be difficult to interpret and requires operators to be properly trained. Traditionally, skill is assessed by direct observation from experts, which is expensive and error prone. This project aims to use deep learning to provide automated skills assessment for FAST exams. Modified I3D networks, a type of modern neural network with a focus on action-based items, were retrained for this purpose. First, a network to identify the skill level of the users from all the ultrasound videos was trained using FAST videos of each vital region divided by novice, intermediate and expert users. Following this, 4 networks corresponding to skill level identification in each region were trained using the previously constructed model. The model’s performance was evaluated using k-fold cross-validation. Results found a testing accuracy of 82.6% for skills assessment using the modified I3D networks. These results are an improvement over the previous results for skill level evaluation, implying potential use of an I3D network for evaluating skill level from ultrasound video in the future with the proper finetuning.

KEYWORDS:

• Diagnostic ultrasound
• skills assessment
• deep learning

Acknowledgments

This research was enabled in part by support provided by Compute Ontario (www.computeontario.ca) and Compute Canada (www.computecanada.ca).

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Notes on contributors

Robert E. Tyrrell

Robert E. Tyrrell recently obtained his BCS (2020) from Carleton University (Ottawa, Canada). His areas of interest lie in Machine Learning and Image Recognition.

Matthew S. Holden

Matthew S. Holden is an Assistant Professor in the School of Computer Science at Carleton University (Ottawa, Canada). Previously, he was a postdoctoral fellow at the Malone Center for Engineering in Healthcare at Johns Hopkins University (Baltimore, USA). He completed his PhD (2018) and MSc (2014) in Computing at Queen's University (Kingston, Canada). He received his BScH (2012) in Applied Mathematics and Physics from Western University (London, Canada). His primary research interest is in Surgical Data Science.