Jodie Avery

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 …

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 …

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 …

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…

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 …

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…

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.

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.

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…

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…

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…

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…

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 …

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…

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…

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…

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…

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…

Objective.Endometriosis, affecting about 10% of individuals assigned female at birth, is challenging to diagnose and manage. Diagnosis typically involves the identification of various signs of the disease using either laparoscopic surgery o…

Endometriosis, affecting about 10% of individuals assigned female at birth, is challenging to diagnose and manage. Diagnosis typically involves the identification of various signs of the disease using either laparoscopic surgery or the anal…

Abstract Study question Can we improve the diagnostic accuracy of the detection of POD obliteration in endometriosis magnetic resonance imaging, by leveraging results from unpaired eTVUS data sets? Summary answer We illustrate effective mul…

Background and Aims: Currently, it takes an average of 6.4 years to obtain a diagnosis for endometriosis. To address this delay, the IMAGENDO project combines ultrasounds (eTVUS) and Magnetic Resonance Images (eMRI) using Artificial Intelli…