Atrial Septal Defect Detection in Children Based on Ultrasound Video Using Multiple Instances Learning
AbstractThoracic echocardiography (TTE) can provide sufficient cardiac structure information, evaluate hemodynamics and cardiac function, and is an effective method for atrial septal defect (ASD) examination. This paper aims to study a deep learning method based on cardiac ultrasound video to assist in ASD diagnosis. We chose four standard views in pediatric cardiac ultrasound to identify atrial septal defects; the four standard views were as follows: subcostal sagittal view of the atrium septum (subSAS), apical four-chamber view (A4C), the low parasternal four-chamber view (LPS4C), and parasternal short-axis view of large...
Source: Journal of Digital Imaging - February 12, 2024 Category: Radiology Source Type: research
HIMSS-SIIM Enterprise Imaging Community White Papers: Reflections and Future Directions
(Source: Journal of Digital Imaging)
Source: Journal of Digital Imaging - February 9, 2024 Category: Radiology Source Type: research
Inconsistency between Human Observation and Deep Learning Models: Assessing Validity of Postmortem Computed Tomography Diagnosis of Drowning
AbstractDrowning diagnosis is a complicated process in the autopsy, even with the assistance of autopsy imaging and the on-site information from where the body was found. Previous studies have developed well-performed deep learning (DL) models for drowning diagnosis. However, the validity of the DL models was not assessed, raising doubts about whether the learned features accurately represented the medical findings observed by human experts. In this paper, we assessed the medical validity of DL models that had achieved high classification performance for drowning diagnosis. This retrospective study included autopsy cases a...
Source: Journal of Digital Imaging - February 9, 2024 Category: Radiology Source Type: research
Letter to the Editor Regarding the Article “Comparison of Transfer Learning Models in Pelvic Tilt and Rotation Measurement in Pediatric Anteroposterior Pelvic Radiographs”
(Source: Journal of Digital Imaging)
Source: Journal of Digital Imaging - February 9, 2024 Category: Radiology Source Type: research
A Proof of Concept: Optimized Jawbone-Reduction Model for Mandibular Fracture Surgery
AbstractPrevious research on computer-assisted jawbone reduction for mandibular fracture surgery has only focused on the relationship between fractured sections disregarding proper dental occlusion with the maxilla. To overcome malocclusion caused by overlooking dental articulation, this study aims to provide a model for jawbone reduction based on dental occlusion. After dental landmarks and fracture sectional features are extracted, the maxilla and two mandible segments are aligned first using the extracted dental landmarks. A swarm-based optimization is subsequently performed by simultaneously observing the fracture sect...
Source: Journal of Digital Imaging - February 8, 2024 Category: Radiology Source Type: research
Basal Cell Carcinoma Diagnosis with Fusion of Deep Learning and Telangiectasia Features
This study demonstrates a novel “fusion” technique for BCC vs non-BCC classification using ensemble learning on a combination of (a) handcrafted features from semantically segmented telangiectasia (U-Net-based) and (b) deep learning features generated from whole lesion images (EfficientNet-B5-based). This fusion method achieve s a binary classification accuracy of 97.2%, with a 1.3% improvement over the corresponding DL-only model, on a holdout test set of 395 images. An increase of 3.7% in sensitivity, 1.5% in specificity, and 1.5% in precision along with an AUC of 0.99 was also achieved. Metric improvements were demo...
Source: Journal of Digital Imaging - February 8, 2024 Category: Radiology Source Type: research
Classification of H. pylori Infection from Histopathological Images Using Deep Learning
AbstractHelicobacter pylori (H. pylori) is a widespread pathogenic bacterium, impacting over 4 billion individuals globally. It is primarily linked to gastric diseases, including gastritis, peptic ulcers, and cancer. The current histopathological method for diagnosingH. pylori involves labour-intensive examination of endoscopic biopsies by trained pathologists. However, this process can be time-consuming and may occasionally result in the oversight of small bacterial quantities. Our study explored the potential of five pre-trained models for binary classification of 204 histopathological images, distinguishing betweenH. py...
Source: Journal of Digital Imaging - February 8, 2024 Category: Radiology Source Type: research
Effects of Interobserver Segmentation Variability and Intensity Discretization on MRI-Based Radiomic Feature Reproducibility of Lipoma and Atypical Lipomatous Tumor
In conclusion, MRI radiomic features of lipoma and ALT are reproducible regardless of the segmentation approach and intensity discretization method, although a certain degree of interobserver variability highlights the need for a preliminary reliability analysis in future studies. (Source: Journal of Digital Imaging)
Source: Journal of Digital Imaging - February 8, 2024 Category: Radiology Source Type: research
Validation of Signal Intensity Gradient from TOF-MRA for Wall Shear Stress by Phase-Contrast MR
This study was registered on ClinicalTrials.gov with the identifier NCT04585971 on October 14, 2020. (Source: Journal of Digital Imaging)
Source: Journal of Digital Imaging - February 8, 2024 Category: Radiology Source Type: research
Deep Learning for Chest X-ray Diagnosis: Competition Between Radiologists with or Without Artificial Intelligence Assistance
This study aimed to assess the performance of a deep learning algorithm in helping radiologist achieve improved efficiency and accuracy in chest radiograph diagnosis. We adopted a deep learning algorithm to concurrently detect the presence of normal findings and 13 different abnormalities in chest radiographs and evaluated its performance in assisting radiologists. Each competing radiologist had to determine the presence or absence of these signs based on the label provided by the AI. The 100 radiographs were randomly divided into two sets for evaluation: one without AI assistance (control group) and one with AI assistance...
Source: Journal of Digital Imaging - February 8, 2024 Category: Radiology Source Type: research
Fast Real-Time Brain Tumor Detection Based on Stimulated Raman Histology and Self-Supervised Deep Learning Model
In this study, we employ coherent Raman scattering imaging method and a self-supervised deep learning model (VQVAE2) to enhance the speed of SRH image acquisition and feature representation, thereby enhancing the capability of automated real-time bedside diagnosis. Specifically, we propose the VQSRS network, which integrates vector quantization with a proxy task based on patch annotation for analysis of brain tumor subtypes. Training on images collected from the SRS microscopy system, our VQSRS demonstrates a significant speed enhancement over traditional techniques (e.g., 20 –30 min). Comparative studies in dimensional...
Source: Journal of Digital Imaging - February 7, 2024 Category: Radiology Source Type: research
Predicting T-Cell Lymphoma in Children From 18F-FDG PET-CT Imaging With Multiple Machine Learning Models
This study aimed to examine the feasibility of utilizing radiomics models derived from18F-FDG PET/CT imaging to screen for T-cell lymphoma in children with lymphoma. All patients had undergone18F-FDG PET/CT scans. Lesions were extracted from PET/CT and randomly divided into training and validation sets. Two different types of models were constructed as follows: features that are extracted from standardized uptake values (SUV)-associated parameters, and CT images were used to build SUV/CT-based model. Features that are derived from PET and CT images were used to build PET/CT-based model. Logistic regression (LR), linear sup...
Source: Journal of Digital Imaging - February 6, 2024 Category: Radiology Source Type: research
Signal Intensity Trajectories Clustering for Liver Vasculature Segmentation and Labeling (LiVaS) on Contrast-Enhanced MR Images: A Feasibility Pilot Study
This study aims to develop a semiautomated pipeline and user interface (LiVaS) for rapid segmentation and labeling of MRI liver vasculature and evaluate its time efficiency and accuracy against manual reference standard. Retrospective feasibility pilot study. Liver MR images from different scanners from 36 patients were included, and 4/36 patients were randomly selected for manual segmentation as referenced standard. The liver was segmented in each contrast phase and masks registered to the pre-contrast segmentation. Voxel-wise signal trajectories were clustered using the k-means algorithm. Voxel clusters that best segment...
Source: Journal of Digital Imaging - February 6, 2024 Category: Radiology Source Type: research
A Multi-center Dental Panoramic Radiography Image Dataset for Impacted Teeth, Periodontitis, and Dental Caries: Benchmarking Segmentation and Classification Tasks
In this study, our dataset comprises three datasets obtained from different hospitals. The first set has 4940 panoramic radiography images and corresponding labels from the Stemmatological Hospital of the General Hospital of Ningxia Medical University. The second set includes 716 panoramic radiography images and labels from the People ’s Hospital of Yinchuan City, Ningxia. The third dataset contains 880 panoramic radiography images and labels from a hospital in Shenzhen, Guangdong Province. This comprehensive dataset encompasses three types of dental diseases: impacted teeth, periodontitis, and dental caries. Specificall...
Source: Journal of Digital Imaging - February 6, 2024 Category: Radiology Source Type: research
Deep Learning-Assisted Diffusion Tensor Imaging for Evaluation of the Physis and Metaphysis
AbstractDiffusion tensor imaging of physis and metaphysis can be used as a biomarker to predict height change in the pediatric population. Current application of this technique requires manual segmentation of the physis which is time-consuming and introduces interobserver variability. UNET Transformers (UNETR) can be used for automatic segmentation to optimize workflow. Three hundred and eighty-five DTI scans from 191 subjects with mean age of 12.6 years ± 2.01 years were retrospectively used for training and validation. The mean Dice correlation coefficient was 0.81 for the UNETR model and 0.68 for the UNET. Manual ...
Source: Journal of Digital Imaging - February 6, 2024 Category: Radiology Source Type: research
