Publications by authors named "Jenny M Vo-Phamhi"
Article Synopsis
- * A total of 403 patients' ultrasound images were analyzed, and the DL model achieved significant diagnostic performance metrics, including sensitivity and accuracy for differentiating between normal and various levels of steatosis.
- * The findings indicate that this DL program is effective for detecting and categorizing liver fat, suggesting its potential as a valuable tool in clinical settings for monitoring liver health.
Rationale And Objectives: To compare follow-up imaging and surgical cost implications of the Society of Radiologists in Ultrasound (SRU) guidelines, 2017 and 2022 European (EUR) guidelines, 2020 Canadian Association of Radiologists (CAR) recommendations, and 2013 American College of Radiology (ACR) White Paper for managing incidentally detected gallbladder polyps.
Materials And Methods: 253 consecutive patients with gallbladder polyps identified on ultrasound were independently reviewed by three radiologists for polyp size and morphology. Electronic medical records were reviewed for patient demographics, cholecystectomy (if performed) pathological findings, or any subsequent diagnosis of gallbladder cancer.
The democratization of ultrasound imaging refers to the process of making ultrasound technology more accessible. Traditionally, ultrasound imaging has been predominately used in specialized medical facilities by trained professionals. Advancements in technology and changes in the health-care landscape have inspired efforts to broaden the availability of ultrasound imaging to various settings such as remote and resource-limited areas.
View Article and Find Full Text PDFRecent advancements in in situ methods, such as multiplexed in situ RNA hybridization and in situ RNA sequencing, have deepened our understanding of the way biological processes are spatially organized in tissues. Automated image processing and spot-calling algorithms for analyzing in situ transcriptomics images have many parameters which need to be tuned for optimal detection. Having ground truth datasets (images where there is very high confidence on the accuracy of the detected spots) is essential for evaluating these algorithms and tuning their parameters.
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