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Leveraging AI in Postgraduate Medical Education for Rapid Skill Acquisition in Ultrasound-Guided Procedural Techniques

Ultrasound-guided techniques are increasingly prevalent and represent a gold standard of care. Skills such as needle visualisation, optimising the target image and directing the needle require deliberate practice. However, training opportunities remain limited by patient case load and safety conside...

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Autores principales: Xu, Flora Wen Xin, Choo, Amanda Min Hui, Ting, Pamela Li Ming, Ong, Shao Jin, Khoo, Deborah
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10607244/
https://www.ncbi.nlm.nih.gov/pubmed/37888332
http://dx.doi.org/10.3390/jimaging9100225
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author Xu, Flora Wen Xin
Choo, Amanda Min Hui
Ting, Pamela Li Ming
Ong, Shao Jin
Khoo, Deborah
author_facet Xu, Flora Wen Xin
Choo, Amanda Min Hui
Ting, Pamela Li Ming
Ong, Shao Jin
Khoo, Deborah
author_sort Xu, Flora Wen Xin
collection PubMed
description Ultrasound-guided techniques are increasingly prevalent and represent a gold standard of care. Skills such as needle visualisation, optimising the target image and directing the needle require deliberate practice. However, training opportunities remain limited by patient case load and safety considerations. Hence, there is a genuine and urgent need for trainees to attain accelerated skill acquisition in a time- and cost-efficient manner that minimises risk to patients. We propose a two-step solution: First, we have created an agar phantom model that simulates human tissue and structures like vessels and nerve bundles. Moreover, we have adopted deep learning techniques to provide trainees with live visualisation of target structures and automate assessment of their user speed and accuracy. Key structures like the needle tip, needle body, target blood vessels, and nerve bundles, are delineated in colour on the processed image, providing an opportunity for real-time guidance of needle positioning and target structure penetration. Quantitative feedback on user speed (time taken for target penetration), accuracy (penetration of correct target), and efficacy in needle positioning (percentage of frames where the full needle is visualised in a longitudinal plane) are also assessable using our model. Our program was able to demonstrate a sensitivity of 99.31%, specificity of 69.23%, accuracy of 91.33%, precision of 89.94%, recall of 99.31%, and F1 score of 0.94 in automated image labelling.
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spelling pubmed-106072442023-10-28 Leveraging AI in Postgraduate Medical Education for Rapid Skill Acquisition in Ultrasound-Guided Procedural Techniques Xu, Flora Wen Xin Choo, Amanda Min Hui Ting, Pamela Li Ming Ong, Shao Jin Khoo, Deborah J Imaging Article Ultrasound-guided techniques are increasingly prevalent and represent a gold standard of care. Skills such as needle visualisation, optimising the target image and directing the needle require deliberate practice. However, training opportunities remain limited by patient case load and safety considerations. Hence, there is a genuine and urgent need for trainees to attain accelerated skill acquisition in a time- and cost-efficient manner that minimises risk to patients. We propose a two-step solution: First, we have created an agar phantom model that simulates human tissue and structures like vessels and nerve bundles. Moreover, we have adopted deep learning techniques to provide trainees with live visualisation of target structures and automate assessment of their user speed and accuracy. Key structures like the needle tip, needle body, target blood vessels, and nerve bundles, are delineated in colour on the processed image, providing an opportunity for real-time guidance of needle positioning and target structure penetration. Quantitative feedback on user speed (time taken for target penetration), accuracy (penetration of correct target), and efficacy in needle positioning (percentage of frames where the full needle is visualised in a longitudinal plane) are also assessable using our model. Our program was able to demonstrate a sensitivity of 99.31%, specificity of 69.23%, accuracy of 91.33%, precision of 89.94%, recall of 99.31%, and F1 score of 0.94 in automated image labelling. MDPI 2023-10-16 /pmc/articles/PMC10607244/ /pubmed/37888332 http://dx.doi.org/10.3390/jimaging9100225 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Xu, Flora Wen Xin
Choo, Amanda Min Hui
Ting, Pamela Li Ming
Ong, Shao Jin
Khoo, Deborah
Leveraging AI in Postgraduate Medical Education for Rapid Skill Acquisition in Ultrasound-Guided Procedural Techniques
title Leveraging AI in Postgraduate Medical Education for Rapid Skill Acquisition in Ultrasound-Guided Procedural Techniques
title_full Leveraging AI in Postgraduate Medical Education for Rapid Skill Acquisition in Ultrasound-Guided Procedural Techniques
title_fullStr Leveraging AI in Postgraduate Medical Education for Rapid Skill Acquisition in Ultrasound-Guided Procedural Techniques
title_full_unstemmed Leveraging AI in Postgraduate Medical Education for Rapid Skill Acquisition in Ultrasound-Guided Procedural Techniques
title_short Leveraging AI in Postgraduate Medical Education for Rapid Skill Acquisition in Ultrasound-Guided Procedural Techniques
title_sort leveraging ai in postgraduate medical education for rapid skill acquisition in ultrasound-guided procedural techniques
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10607244/
https://www.ncbi.nlm.nih.gov/pubmed/37888332
http://dx.doi.org/10.3390/jimaging9100225
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