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A deep learning–based system for mediastinum station localization in linear EUS (with video)
BACKGROUND AND OBJECTIVES: EUS is a crucial diagnostic and therapeutic method for many anatomical regions, especially in the evaluation of mediastinal diseases and related pathologies. Rapidly finding the standard stations is the key to achieving efficient and complete mediastinal EUS imaging. Howev...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Lippincott Williams & Wilkins
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10631614/ https://www.ncbi.nlm.nih.gov/pubmed/37969169 http://dx.doi.org/10.1097/eus.0000000000000011 |
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author | Yao, Liwen Zhang, Chenxia Xu, Bo Yi, Shanshan Li, Juan Ding, Xiangwu Yu, Honggang |
author_facet | Yao, Liwen Zhang, Chenxia Xu, Bo Yi, Shanshan Li, Juan Ding, Xiangwu Yu, Honggang |
author_sort | Yao, Liwen |
collection | PubMed |
description | BACKGROUND AND OBJECTIVES: EUS is a crucial diagnostic and therapeutic method for many anatomical regions, especially in the evaluation of mediastinal diseases and related pathologies. Rapidly finding the standard stations is the key to achieving efficient and complete mediastinal EUS imaging. However, it requires substantial technical skills and extensive knowledge of mediastinal anatomy. We constructed a system, named EUS-MPS (EUS–mediastinal position system), for real-time mediastinal EUS station recognition. METHODS: The standard scanning of mediastinum EUS was divided into 7 stations. There were 33 010 images in mediastinum EUS examination collected to construct a station classification model. Then, we used 151 videos clips for video validation and used 1212 EUS images from 2 other hospitals for external validation. An independent data set containing 230 EUS images was applied for the man-machine contest. We conducted a crossover study to evaluate the effectiveness of this system in reducing the difficulty of mediastinal ultrasound image interpretation. RESULTS: For station classification, the model achieved an accuracy of 90.49% in image validation and 83.80% in video validation. At external validation, the models achieved 89.85% accuracy. In the man-machine contest, the model achieved an accuracy of 84.78%, which was comparable to that of expert (83.91%). The accuracy of the trainees' station recognition was significantly improved in the crossover study, with an increase of 13.26% (95% confidence interval, 11.04%–15.48%; P < 0.05). CONCLUSIONS: This deep learning–based system shows great performance in mediastinum station localization, having the potential to play an important role in shortening the learning curve and establishing standard mediastinal scanning in the future. |
format | Online Article Text |
id | pubmed-10631614 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Lippincott Williams & Wilkins |
record_format | MEDLINE/PubMed |
spelling | pubmed-106316142023-11-15 A deep learning–based system for mediastinum station localization in linear EUS (with video) Yao, Liwen Zhang, Chenxia Xu, Bo Yi, Shanshan Li, Juan Ding, Xiangwu Yu, Honggang Endosc Ultrasound Original Research BACKGROUND AND OBJECTIVES: EUS is a crucial diagnostic and therapeutic method for many anatomical regions, especially in the evaluation of mediastinal diseases and related pathologies. Rapidly finding the standard stations is the key to achieving efficient and complete mediastinal EUS imaging. However, it requires substantial technical skills and extensive knowledge of mediastinal anatomy. We constructed a system, named EUS-MPS (EUS–mediastinal position system), for real-time mediastinal EUS station recognition. METHODS: The standard scanning of mediastinum EUS was divided into 7 stations. There were 33 010 images in mediastinum EUS examination collected to construct a station classification model. Then, we used 151 videos clips for video validation and used 1212 EUS images from 2 other hospitals for external validation. An independent data set containing 230 EUS images was applied for the man-machine contest. We conducted a crossover study to evaluate the effectiveness of this system in reducing the difficulty of mediastinal ultrasound image interpretation. RESULTS: For station classification, the model achieved an accuracy of 90.49% in image validation and 83.80% in video validation. At external validation, the models achieved 89.85% accuracy. In the man-machine contest, the model achieved an accuracy of 84.78%, which was comparable to that of expert (83.91%). The accuracy of the trainees' station recognition was significantly improved in the crossover study, with an increase of 13.26% (95% confidence interval, 11.04%–15.48%; P < 0.05). CONCLUSIONS: This deep learning–based system shows great performance in mediastinum station localization, having the potential to play an important role in shortening the learning curve and establishing standard mediastinal scanning in the future. Lippincott Williams & Wilkins 2023 2023-10-16 /pmc/articles/PMC10631614/ /pubmed/37969169 http://dx.doi.org/10.1097/eus.0000000000000011 Text en Copyright © 2023 The Author(s). Published by Wolters Kluwer Health, Inc on behalf of Scholar Media Publishing. https://creativecommons.org/licenses/by-nc-sa/4.0/This is an open access article distributed under the Creative Commons Attribution-NonCommercial-ShareAlike License 4.0 (CC BY-NC-SA) (https://creativecommons.org/licenses/by-nc-sa/4.0/) which allows others to remix, tweak, and build upon the work non-commercially, as long as the author is credited and the new creations are licensed under the identical terms. |
spellingShingle | Original Research Yao, Liwen Zhang, Chenxia Xu, Bo Yi, Shanshan Li, Juan Ding, Xiangwu Yu, Honggang A deep learning–based system for mediastinum station localization in linear EUS (with video) |
title | A deep learning–based system for mediastinum station localization in linear EUS (with video) |
title_full | A deep learning–based system for mediastinum station localization in linear EUS (with video) |
title_fullStr | A deep learning–based system for mediastinum station localization in linear EUS (with video) |
title_full_unstemmed | A deep learning–based system for mediastinum station localization in linear EUS (with video) |
title_short | A deep learning–based system for mediastinum station localization in linear EUS (with video) |
title_sort | deep learning–based system for mediastinum station localization in linear eus (with video) |
topic | Original Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10631614/ https://www.ncbi.nlm.nih.gov/pubmed/37969169 http://dx.doi.org/10.1097/eus.0000000000000011 |
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