An online cross-section survey exploring the training needs of healthcare professionals learning to perform transvaginal ultrasound for endometriosis: Does artificial intelligence have a role to play? - 01/07/26
, Jodie Avery a, Rebecca O’Hara b, Hsiang-Ting Chen c, Mathew Leonardi a, d, M. Louise Hull a, 1, George Condous a, e, 1
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Highlights |
• | The most effective training for developing skills in diagnosing endometriosis sonographically is working with skilled mentors or participating in formal training programs. |
• | Due to limited access to these options, most clinicians rely on self-directed learning. |
• | While the majority of respondents had not used AI in their ultrasound workflow, most were open to using it as a learning aid for endometriosis ultrasound. |
• | End-users indicated that the most helpful AI tools would be those integrated into ultrasound machines and designed to assist with recognising disease on scan. |
Abstract |
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 to help expedite eTVUS training. This study aimed to outline the range of eTVUS training currently being undertaken worldwide, identify barriers and enablers in performing eTVUS, and understand how AI-assisted tools could enhance self-learning of eTVUS.
Method |
An online cross-sectional survey was disseminated to healthcare professionals who perform TVUS. A combination of multiple choice and free-text questions were asked regarding demographic information, experience performing eTVUS, training undertaken for eTVUS, experience with AI in ultrasound and how end-users believed AI could help with learning and performing eTVUS. Statistical and thematic analyses were performed.
Results |
A total of 407 response, from 33 countries were included in the final analysis. Online self-directed learning (53.3%) was the most undertaken training method for eTVUS. Working with a skilled mentor was rated as the most helpful training method (mean 4.49 [range,3–5]), which was also the most desired, yet most difficult-to-access training method reported (42.3%). Inadequate training/education in the eTVUS technique (37.1%) and lack of confidence in recognising the appearance of endometriosis on ultrasound (37.1%) were the most common barriers. Training/education in eTVUS techniques was reported as the strongest facilitator to enable performing eTVUS (58.7%).
Most respondents (64.7%) stated they had not used AI in their ultrasound workflow but 64.3% stated they would use an AI tool to assist with learning eTVUS, if available. Overwhelmingly, respondents wanted a teaching tool to help recognise disease (68.3%), which would ideally be built into an ultrasound machine (56.6%).
Conclusion |
Self-directed online learning and reading was the most common form of training undertaken for eTVUS with difficulties reporting accessing skilled mentors as a significant barrier to eTVUS implementation. However, most health professionals would use AI tools for learning if available.
Le texte complet de cet article est disponible en PDF.Keywords : Endometriosis, Training, Transvaginal ultrasound, Artifical intelligence
Plan
Vol 16
Article 100174- décembre 2026 Retour au numéro
Article précédent
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