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Methodology for pediatric head computed tomography image segmentation and volumetric calculation using a tablet computer and stylus pen.
Childs Nerv Syst
December 2024
Department of Neurosurgery, Osaka Women's and Children's Hospital, Izumi, Osaka, 594-1101, Japan.
Purpose: This study presents a MATrix LABoratory (MATLAB)-based methodology for calculating intracranial volumes from head computed tomography (CT) data and compares it with established methods.
Methods: Regions of interest (ROI) were manually segmented on CT images using a stylus pen, facilitated by mirroring a computer desktop onto a tablet. The volumetric process involved three main steps: (1) calculating the volume of a single voxel, (2) counting the total number of voxels within the segmented ROI, and (3) multiplying this voxel count by the single-voxel volume.
Mitigation of AI adoption bias through an improved autonomous AI system for diabetic retinal disease.
NPJ Digit Med
December 2024
Department of Ophthalmology and Visual Sciences, University of Iowa, Iowa City, IA, USA.
Where adopted, Autonomous artificial Intelligence (AI) for Diabetic Retinal Disease (DRD) resolves longstanding racial, ethnic, and socioeconomic disparities, but AI adoption bias persists. This preregistered trial determined sensitivity and specificity of a previously FDA authorized AI, improved to compensate for lower contrast and smaller imaged area of a widely adopted, lower cost, handheld fundus camera (RetinaVue700, Baxter Healthcare, Deerfield, IL) to identify DRD in participants with diabetes without known DRD, in primary care. In 626 participants (1252 eyes) 50.
View Article and Find Full Text PDFQuantitative Liver Fat Assessment by Handheld Point-of-Care Ultrasound: A Technical Implementation and Pilot Study in Adults.
Ultrasound Med Biol
December 2024
Department of Radiology, Liver Imaging Group, University of California San Diego, La Jolla, CA, USA.
Objectives: To implement, examine the feasibility of, and evaluate the performance of quantitative ultrasound (QUS) with a handheld point-of-care US (POCUS) device for assessing liver fat in adults.
Materials And Methods: This prospective IRB-approved, HIPAA-compliant pilot study enrolled adults with overweight or obesity. Participants underwent chemical-shift-encoded magnetic resonance imaging to estimate proton density fat fraction (PDFF) and, within 1 mo, QUS with a POCUS device by expert sonographers and novice operators (no prior US scanning experience).
Enhancing Ultrasound Image Quality Across Disease Domains: Application of Cycle-Consistent Generative Adversarial Network and Perceptual Loss.
JMIR Biomed Eng
December 2024
Department of Bioengineering, University of California Los Angeles, Los Angeles, CA, United States.
Background: Numerous studies have explored image processing techniques aimed at enhancing ultrasound images to narrow the performance gap between low-quality portable devices and high-end ultrasound equipment. These investigations often use registered image pairs created by modifying the same image through methods like down sampling or adding noise, rather than using separate images from different machines. Additionally, they rely on organ-specific features, limiting the models' generalizability across various imaging conditions and devices.
View Article and Find Full Text PDFDeep learning model for diagnosis of venous thrombosis from lower extremity peripheral ultrasound imaging.
iScience
December 2024
Institute of Clinical Medicine, College of Medicine, National Cheng Kung University, Tainan 704, Taiwan.
Deep vein thrombosis (DVT) causes significant healthcare burdens worldwide. This study aims to establish a deep learning model for the diagnosis of DVT from the assessment of vein compressibility. Considering the complexity of ultrasound images, convolutional neural networks with UNet and residual neural network (ResNet) are established for image segmentation, from venous duplex ultrasonographic video images, obtained through standard and portable handheld ultrasound methods.
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