Jodie Avery

ORCID: 0000-0002-8857-9162 · 36 papers in corpus
observational 2026
·doi:10.1016/j.jeud.2026.100174

Background Rapid advances in transvaginal ultrasound techniques to detect endometriosis (eTVUS) require navigation of a steep learning curve. Artificial intelligence (AI) is playing an ever-increasing role in ultrasound and holds potential …

2026
·doi:10.1201/9781003394068-7
review 2026
Radiography (London, England : 1995) ·doi:10.1016/j.radi.2026.103579

INTRODUCTION: Imaging is increasingly used for adult endometriosis, but evidence supporting adolescent imaging approaches remains unclear. This scoping review mapped evidence on imaging-based diagnosis of endometriosis in adolescents and yo…

2026

Endometriosis ultrasound reports are often unstructured free-text documents that require manual abstraction for downstream tasks such as analytics, machine learning model training, and clinical auditing. We present \textbf{EndoExtract}, an …

2026
·doi:10.48550/arxiv.2601.18154

Endometriosis ultrasound reports are often unstructured free-text documents that require manual abstraction for downstream tasks such as analytics, machine learning model training, and clinical auditing. We present \textbf{EndoExtract}, an …

2026
·doi:10.1145/3786579.3804934
review 2026
·doi:10.1080/23293691.2026.2636611

Endometriosis is a chronic inflammatory condition affecting millions worldwide, yet it remains underdiagnosed and undertreated—particularly among marginalized individuals. Drawing on medical sociology, feminist theory, and trauma studies, t…

2026

In this study, we evaluate a locally-deployed large-language model (LLM) to convert unstructured endometriosis transvaginal ultrasound (eTVUS) scan reports into structured data for imaging informatics workflows. Across 49 eTVUS reports, we …

observational 2026
International Journal of Gynecology & Obstetrics ·doi:10.1002/ijgo.70666

Endometriosis affects 1 in 10 women globally. We aimed to determine health provider involvement and preferred methods of obtaining an endometriosis diagnosis across international jurisdictions. A global cross-sectional survey, distributed t…

2026
·doi:10.64628/aa.pqpsgk7ah
other 2026

An evidence gap map project examining progress against international infertility and endometriosis research priorities identified in 2020, highlighting areas of advancement and remaining evidence gaps to guide future research.

other 2026

An evidence gap map project examining progress against international infertility and endometriosis research priorities identified in 2020, highlighting areas of advancement and remaining evidence gaps to guide future research.

other 2026
Human Reproduction ·doi:10.1093/humrep/deag024

Artificial intelligence (AI) is revolutionizing how we practice medicine. In areas where we have traditionally struggled, such as diagnosing endometriosis, AI has significant potential to improve the breadth and accuracy of diagnostic servi…

other 2026

Objective: To map the extent and type of evidence describing how diagnostic imaging for endometriosis affects quality of life, mental health and everyday functioning in adolescents and young adults aged 14-25 years. Introduction: With the e…

other 2026

Objective: To map the extent and type of evidence describing how diagnostic imaging for endometriosis affects quality of life, mental health and everyday functioning in adolescents and young adults aged 14-25 years. Introduction: With the e…

other 2026
Artificial intelligence in medicine ·doi:10.1016/j.artmed.2026.103503

Endometriosis is a widespread gynecological disorder causing severe pain and infertility, with diagnosis currently relying on slow, costly, and risky laparoscopy. This highlights the critical need for non-invasive imaging diagnostics using …

2026
·doi:10.1016/s3050-5038(26)00267-0
2026
·doi:10.48550/arxiv.2601.09053

In this study, we evaluate a locally-deployed large-language model (LLM) to convert unstructured endometriosis transvaginal ultrasound (eTVUS) scan reports into structured data for imaging informatics workflows. Across 49 eTVUS reports, we …

2026
·doi:10.1145/3772363.3798872

In this study, we evaluate locally deployed large language models (LLMs) for converting unstructured endometriosis transvaginal ultrasound (eTVUS) reports into structured data. Across 49 de-identified reports, we compared three on-premise L…

other 2025
Australasian journal of ultrasound in medicine ·doi:10.1002/ajum.70026

OBJECTIVES: Accurate diagnosis of pathology from ultrasound images is reliant upon images of a suitable diagnostic quality being acquired. This study aimed to create a novel machine learning model to automatically assess transvaginal ultras…

other 2025
·doi:10.17605/osf.io/2xdsh

Objective: The objective of this scoping review is to identify and synthesise data that maps the current knowledge of ultrasound, MRI and AI assisted diagnosis of endometriosis in adolescents. Introduction: Endometriosis often begins in ado…

other 2025

Objective: The objective of this scoping review is to identify and synthesise data that maps the current knowledge of ultrasound, MRI and AI assisted diagnosis of endometriosis in adolescents. Introduction: Endometriosis often begins in ado…

diagnostic-accuracy 2025
Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society ·doi:10.1016/j.compmedimag.2025.102575

Endometriosis is a serious multifocal condition that can involve various pelvic structures, with Pouch of Douglas (POD) obliteration being a significant clinical indicator for diagnosis. To circumvent the need for invasive diagnostic proced…

review 2024
·doi:10.1002/sono.12402

Abstract Diagnosis of endometriosis has traditionally relied on laparoscopic surgery, which was considered the ‘gold standard’ diagnostic tool. This is not ideal as surgery carries risk, is expensive, is difficult to access, and disrupts pa…

other 2024
Australasian journal of ultrasound in medicine ·doi:10.1002/ajum.12368

PURPOSE: The aim of this study was to investigate the current application of artificial intelligence (AI) tools in the teaching of ultrasound skills as they pertain to gynaecological ultrasound. METHODS: A scoping review was performed. Eig…