21 research outputs found
Application of Artificial Intelligence to Ultrasonography
The use of artificial intelligence (AI) technology in medicine has gained considerable attention, although its application in ultrasound medicine is still in its infancy. Deep learning, the main algorithm of AI technology, can be applied to intelligent ultrasound picture detection and classification. Describe the application status of AI in ultrasound imaging, including thyroid, breast, and liver disease applications. The merging of AI and ultrasound imaging can increase the accuracy and specificity of ultrasound diagnosis and decrease the percentage of incorrect diagnoses
Ultrasound Liver Fibrosis Diagnosis using Multi-indicator guided Deep Neural Networks
Accurate analysis of the fibrosis stage plays very important roles in
follow-up of patients with chronic hepatitis B infection. In this paper, a deep
learning framework is presented for automatically liver fibrosis prediction. On
contrary of previous works, our approach can take use of the information
provided by multiple ultrasound images. An indicator-guided learning mechanism
is further proposed to ease the training of the proposed model. This follows
the workflow of clinical diagnosis and make the prediction procedure
interpretable. To support the training, a dataset is well-collected which
contains the ultrasound videos/images, indicators and labels of 229 patients.
As demonstrated in the experimental results, our proposed model shows its
effectiveness by achieving the state-of-the-art performance, specifically, the
accuracy is 65.6%(20% higher than previous best).Comment: Jiali Liu and Wenxuan Wang are equal contributio
Ultrasound Detection of Subquadricipital Recess Distension
Joint bleeding is a common condition for people with hemophilia and, if
untreated, can result in hemophilic arthropathy. Ultrasound imaging has
recently emerged as an effective tool to diagnose joint recess distension
caused by joint bleeding. However, no computer-aided diagnosis tool exists to
support the practitioner in the diagnosis process. This paper addresses the
problem of automatically detecting the recess and assessing whether it is
distended in knee ultrasound images collected in patients with hemophilia.
After framing the problem, we propose two different approaches: the first one
adopts a one-stage object detection algorithm, while the second one is a
multi-task approach with a classification and a detection branch. The
experimental evaluation, conducted with annotated images, shows that the
solution based on object detection alone has a balanced accuracy score of
with a mean IoU value of , while the multi-task approach has a
higher balanced accuracy value () at the cost of a slightly lower mean
IoU value
Convolutional neural network classification of ultrasound images by liver fibrosis stages based on echo-envelope statistics
Introduction: Assessing the stage of liver fibrosis during the diagnosis and follow-up of patients with diffuse liver disease is crucial. The tissue structure in the fibrotic liver is reflected in the texture and contrast of an ultrasound image, with the pixel brightness indicating the intensity of the echo envelope. Therefore, the progression of liver fibrosis can be evaluated non-invasively by analyzing ultrasound images.Methods: A convolutional-neural-network (CNN) classification of ultrasound images was applied to estimate liver fibrosis. In this study, the colorization of the ultrasound images using echo-envelope statistics that correspond to the features of the images is proposed to improve the accuracy of CNN classification. In the proposed method, the ultrasound image is modulated by the 3rd- and 4th-order moments of pixel brightness. The two modulated images and the original image were then synthesized into a color image of RGB representation.Results and Discussion: The colorized ultrasound images were classified via transfer learning of VGG-16 to evaluate the effect of colorization. Of the 80 ultrasound images with liver fibrosis stages F1–F4, 38 images were accurately classified by the CNN using the original ultrasound images, whereas 47 images were classified by the proposed method
Improving Nonalcoholic Fatty Liver Disease Classification Performance With Latent Diffusion Models
Integrating deep learning with clinical expertise holds great potential for
addressing healthcare challenges and empowering medical professionals with
improved diagnostic tools. However, the need for annotated medical images is
often an obstacle to leveraging the full power of machine learning models. Our
research demonstrates that by combining synthetic images, generated using
diffusion models, with real images, we can enhance nonalcoholic fatty liver
disease (NAFLD) classification performance. We evaluate the quality of the
synthetic images by comparing two metrics: Inception Score (IS) and Fr\'{e}chet
Inception Distance (FID), computed on diffusion-generated images and generative
adversarial networks (GANs)-generated images. Our results show superior
performance for the diffusion-generated images, with a maximum IS score of
compared to for GANs, and a minimum FID score of compared
to for GANs. Utilizing a partially frozen CNN backbone (EfficientNet
v1), our synthetic augmentation method achieves a maximum image-level ROC AUC
of on a NAFLD prediction task.Comment: 24 pages, 9 figure