Publications by authors named "J C Avery"
Objectives: The development of valuable artificial intelligence (AI) tools to assist with ultrasound diagnosis depends on algorithms developed using high-quality data. This study aimed to test the intra- and interobserver agreement of a proposed image-quality scoring system to quantify the quality of gynecological transvaginal ultrasound (TVS) images, which could be used in clinical practice and AI tool development.
Methods: A proposed scoring system to quantify TVS image quality was created following a review of the literature.
Phenotypic plasticity in body growth enables organisms to cope with unpredictable paucities in resource availability. Growth traits influence survival and reproductive success, and thereby, population persistence, and early-life resource availability may govern lifetime patterns in growth, reproductive success, and survival. The influence of early-life environment is decidedly consequential for indeterminately growing ectotherms, which rely on available resources and ambient temperatures to maximize fitness throughout life.
View Article and Find Full Text PDFObjectives: To investigate if frailty status alters following solid organ transplantation (lung, liver, kidney and heart) without rehabilitation intervention.
Research Design And Methods: Studies published between 1 January 2000 and 30 May 2023 were searched across five databases. Studies measuring frailty, using a validated or established frailty measure, pre- and post-transplant were included.
Objective: To identify gaps in existing evidence on preconception health interventions to improve the health outcomes of adolescents, young adults, and their offspring.
Study Design: Evidence gap map (EGM) METHODS: Following the Campbell guidelines, we included reviews and interventional studies identified through searches on Medline and other electronic databases from 2010 to July 18th, 2023. Dual screening of titles/abstracts and full texts was conducted on Covidence software, followed by quality assessment and development of 2D-EGM using the EPPI-Reviewer and Mapper software.
Article Synopsis
- - Endometriosis, affecting about 10% of individuals assigned female at birth, is difficult to diagnose and often requires surgical or MRI imaging methods, with MRI being quicker but less precise.
- - A significant diagnostic indicator is the obliteration of the Pouch of Douglas, yet even experienced clinicians find it hard to accurately classify this from MRI images, which makes training reliable AI models a challenge.
- - The paper introduces the HAICOMM methodology, which combines multi-rater and multi-modal learning with human-AI collaboration, leading to more accurate diagnoses of endometriosis compared to traditional methods and improving classification accuracy.