Deep Learning for FAST Quality Assessment. (30th June 2022)

• Record Type: Journal Article
• Authors: Taye, Mesfin; Morrow, Dustin; Cull, John; Smith, Dane Hudson; Hagan, Martin
• Is Part Of: Journal of ultrasound in medicine. Volume 42:Number 1(2023)
• Page Start: 71
• Page End: 79
• Publication Date: 2022-06-30
• Subjects: FAST -- ultrasound -- deep learning -- autoencoder -- convolutional neural network; Ultrasonics in medicine -- Periodicals; Ultrasonics; Ultrasonography; Ultrasonics in medicine; Electronic journals; Periodicals; Periodicals; 616.07543
• DOI: 10.1002/jum.16045
• Languages: English
• ISSNs: 0278-4297

Abstract

Abstract : Objectives: To determine the feasibility of using a deep learning (DL) algorithm to assess the quality of focused assessment with sonography in trauma (FAST) exams. Methods: Our dataset consists of 441 FAST exams, classified as good‐quality or poor‐quality, with 3161 videos. We first used convolutional neural networks (CNNs), pretrained on the Imagenet dataset and fine‐tuned on the FAST dataset. Second, we trained a CNN autoencoder to compress FAST images, with a 20–1 compression ratio. The compressed codes were input to a two‐layer classifier network. To train the networks, each video was labeled with the quality of the exam, and the frames were labeled with the quality of the video. For inference, a video was classified as poor‐quality if half the frames were classified as poor‐quality by the network, and an exam was classified as poor‐quality if half the videos were classified as poor‐quality. Results: The results with the encoder‐classifier networks were much better than the transfer learning results with CNNs. This was primarily because the Imagenet dataset is not a good match for the ultrasound quality assessment problem. The DL models produced video sensitivities and specificities of 99% and 98% on held‐out test sets. Conclusions: Using an autoencoder to compress FAST images is a very effective way to obtain features that can be used to predict exam quality. These features are more suitable than those obtained from CNNs pretrained on Imagenet.