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人工智能三维重建辅助规划胸腔镜肺段切除术的应用价值
Background and objective The three-dimensional (3D) can assist in planning lung segmentectomy. 3D reconstruction software based on artificial intelligence algorithm is gradually applied in clinic. The aim of this study was to evaluate the accuracy and safety of 3D reconstruction assisted planning of...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Editorial board of Chinese Journal of Lung Cancer
2023
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10476204/ https://www.ncbi.nlm.nih.gov/pubmed/37653015 http://dx.doi.org/10.3779/j.issn.1009-3419.2023.102.28 |
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author | ZHENG, Zhizhong REN, Meiyu LI, Bin YANG, Jianbao WEI, Xiaoping SONG, Tieniu MENG, Yuqi CHEN, Yuzhen LIU, Qing |
author_facet | ZHENG, Zhizhong REN, Meiyu LI, Bin YANG, Jianbao WEI, Xiaoping SONG, Tieniu MENG, Yuqi CHEN, Yuzhen LIU, Qing |
author_sort | ZHENG, Zhizhong |
collection | PubMed |
description | Background and objective The three-dimensional (3D) can assist in planning lung segmentectomy. 3D reconstruction software based on artificial intelligence algorithm is gradually applied in clinic. The aim of this study was to evaluate the accuracy and safety of 3D reconstruction assisted planning of thoracoscopic segmentectomy. Methods A total of 90 patients admitted to Department of Thoracic Surgery of Lanzhou University Second Hospital were evaluated for thoracoscopic segmentectomy. Before operation, artificial intelligence 3D reconstruction software was used to make 3D lung images and conduct preoperative planning. Surgical videos were saved during the operation and perioperative data were recorded. Video recordings of 38 patients were selected to explore the effectiveness of artificial intelligence 3D reconstruction for surgical planning. The results of artificial intelligence 3D reconstruction and Mimics 21 software reconstruction were compared with the actual results in the operation, and the detection and classification ability of bronchus and blood vessels of the two reconstruction methods were compared. Results All the 90 patients underwent artificial intelligence 3D reconstruction planning, including 57 patients (63.3%) with single lung segmentectomy and 33 patients (36.7%) with combined sub-segmentectomy. The accuracy of artificial intelligence 3D reconstruction for lesion localization was 100.0%, and the accuracy of computed tomography (CT) was 94.4% (85/90). The detection accuracy of artificial intelligence 3D reconstruction and Mimics 21 software was 92.1% (35/38) and 89.5% (34/38), and the anatomic typing accuracy was 89.5% (34/38) and 84.2% (32/38), and the total accuracy was 76.3% (29/38) and 71.1% (27/38). In the comparative observation of 38 surgical videos and reconstructed images, the consistent rates of target segment planning, surgical approach, artery dissection, vein dissection and bronchial dissection for preoperative planning using artificial intelligence 3D reconstruction were 92.1% (35/38), 92.1% (35/38), 89.5% (34/38), 86.8% (33/38) and 94.7% (36/38). The overall planning operational consistency rate was 68.4% (26/38). Conclusion It is accurate and safe to use artificial intelligence 3D reconstruction to assist planning thoracoscopic segmentectomy. |
format | Online Article Text |
id | pubmed-10476204 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Editorial board of Chinese Journal of Lung Cancer |
record_format | MEDLINE/PubMed |
spelling | pubmed-104762042023-09-05 人工智能三维重建辅助规划胸腔镜肺段切除术的应用价值 ZHENG, Zhizhong REN, Meiyu LI, Bin YANG, Jianbao WEI, Xiaoping SONG, Tieniu MENG, Yuqi CHEN, Yuzhen LIU, Qing Zhongguo Fei Ai Za Zhi Clinical Research Background and objective The three-dimensional (3D) can assist in planning lung segmentectomy. 3D reconstruction software based on artificial intelligence algorithm is gradually applied in clinic. The aim of this study was to evaluate the accuracy and safety of 3D reconstruction assisted planning of thoracoscopic segmentectomy. Methods A total of 90 patients admitted to Department of Thoracic Surgery of Lanzhou University Second Hospital were evaluated for thoracoscopic segmentectomy. Before operation, artificial intelligence 3D reconstruction software was used to make 3D lung images and conduct preoperative planning. Surgical videos were saved during the operation and perioperative data were recorded. Video recordings of 38 patients were selected to explore the effectiveness of artificial intelligence 3D reconstruction for surgical planning. The results of artificial intelligence 3D reconstruction and Mimics 21 software reconstruction were compared with the actual results in the operation, and the detection and classification ability of bronchus and blood vessels of the two reconstruction methods were compared. Results All the 90 patients underwent artificial intelligence 3D reconstruction planning, including 57 patients (63.3%) with single lung segmentectomy and 33 patients (36.7%) with combined sub-segmentectomy. The accuracy of artificial intelligence 3D reconstruction for lesion localization was 100.0%, and the accuracy of computed tomography (CT) was 94.4% (85/90). The detection accuracy of artificial intelligence 3D reconstruction and Mimics 21 software was 92.1% (35/38) and 89.5% (34/38), and the anatomic typing accuracy was 89.5% (34/38) and 84.2% (32/38), and the total accuracy was 76.3% (29/38) and 71.1% (27/38). In the comparative observation of 38 surgical videos and reconstructed images, the consistent rates of target segment planning, surgical approach, artery dissection, vein dissection and bronchial dissection for preoperative planning using artificial intelligence 3D reconstruction were 92.1% (35/38), 92.1% (35/38), 89.5% (34/38), 86.8% (33/38) and 94.7% (36/38). The overall planning operational consistency rate was 68.4% (26/38). Conclusion It is accurate and safe to use artificial intelligence 3D reconstruction to assist planning thoracoscopic segmentectomy. Editorial board of Chinese Journal of Lung Cancer 2023-07-20 /pmc/articles/PMC10476204/ /pubmed/37653015 http://dx.doi.org/10.3779/j.issn.1009-3419.2023.102.28 Text en 版权所有 © 2023《中国肺癌杂志》编辑部 https://creativecommons.org/licenses/by/3.0/This is an open access article distributed in accordance with the terms of the Creative Commons Attribution (CC BY 3.0) License. See: https://creativecommons.org/licenses/by/3.0/. |
spellingShingle | Clinical Research ZHENG, Zhizhong REN, Meiyu LI, Bin YANG, Jianbao WEI, Xiaoping SONG, Tieniu MENG, Yuqi CHEN, Yuzhen LIU, Qing 人工智能三维重建辅助规划胸腔镜肺段切除术的应用价值 |
title | 人工智能三维重建辅助规划胸腔镜肺段切除术的应用价值 |
title_full | 人工智能三维重建辅助规划胸腔镜肺段切除术的应用价值 |
title_fullStr | 人工智能三维重建辅助规划胸腔镜肺段切除术的应用价值 |
title_full_unstemmed | 人工智能三维重建辅助规划胸腔镜肺段切除术的应用价值 |
title_short | 人工智能三维重建辅助规划胸腔镜肺段切除术的应用价值 |
title_sort | 人工智能三维重建辅助规划胸腔镜肺段切除术的应用价值 |
topic | Clinical Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10476204/ https://www.ncbi.nlm.nih.gov/pubmed/37653015 http://dx.doi.org/10.3779/j.issn.1009-3419.2023.102.28 |
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