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Computer-aided anatomy recognition in intrathoracic and -abdominal surgery: a systematic review
BACKGROUND: Minimally invasive surgery is complex and associated with substantial learning curves. Computer-aided anatomy recognition, such as artificial intelligence-based algorithms, may improve anatomical orientation, prevent tissue injury, and improve learning curves. The study objective was to...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer US
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9652273/ https://www.ncbi.nlm.nih.gov/pubmed/35927354 http://dx.doi.org/10.1007/s00464-022-09421-5 |
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author | den Boer, R. B. de Jongh, C. Huijbers, W. T. E. Jaspers, T. J. M. Pluim, J. P. W. van Hillegersberg, R. Van Eijnatten, M. Ruurda, J. P. |
author_facet | den Boer, R. B. de Jongh, C. Huijbers, W. T. E. Jaspers, T. J. M. Pluim, J. P. W. van Hillegersberg, R. Van Eijnatten, M. Ruurda, J. P. |
author_sort | den Boer, R. B. |
collection | PubMed |
description | BACKGROUND: Minimally invasive surgery is complex and associated with substantial learning curves. Computer-aided anatomy recognition, such as artificial intelligence-based algorithms, may improve anatomical orientation, prevent tissue injury, and improve learning curves. The study objective was to provide a comprehensive overview of current literature on the accuracy of anatomy recognition algorithms in intrathoracic and -abdominal surgery. METHODS: This systematic review is reported according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guideline. Pubmed, Embase, and IEEE Xplore were searched for original studies up until January 2022 on computer-aided anatomy recognition, without requiring intraoperative imaging or calibration equipment. Extracted features included surgical procedure, study population and design, algorithm type, pre-training methods, pre- and post-processing methods, data augmentation, anatomy annotation, training data, testing data, model validation strategy, goal of the algorithm, target anatomical structure, accuracy, and inference time. RESULTS: After full-text screening, 23 out of 7124 articles were included. Included studies showed a wide diversity, with six possible recognition tasks in 15 different surgical procedures, and 14 different accuracy measures used. Risk of bias in the included studies was high, especially regarding patient selection and annotation of the reference standard. Dice and intersection over union (IoU) scores of the algorithms ranged from 0.50 to 0.98 and from 74 to 98%, respectively, for various anatomy recognition tasks. High-accuracy algorithms were typically trained using larger datasets annotated by expert surgeons and focused on less-complex anatomy. Some of the high-accuracy algorithms were developed using pre-training and data augmentation. CONCLUSIONS: The accuracy of included anatomy recognition algorithms varied substantially, ranging from moderate to good. Solid comparison between algorithms was complicated by the wide variety of applied methodology, target anatomical structures, and reported accuracy measures. Computer-aided intraoperative anatomy recognition is an upcoming research discipline, but still at its infancy. Larger datasets and methodological guidelines are required to improve accuracy and clinical applicability in future research. Trial registration: PROSPERO registration number: CRD42021264226 SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s00464-022-09421-5. |
format | Online Article Text |
id | pubmed-9652273 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Springer US |
record_format | MEDLINE/PubMed |
spelling | pubmed-96522732022-11-15 Computer-aided anatomy recognition in intrathoracic and -abdominal surgery: a systematic review den Boer, R. B. de Jongh, C. Huijbers, W. T. E. Jaspers, T. J. M. Pluim, J. P. W. van Hillegersberg, R. Van Eijnatten, M. Ruurda, J. P. Surg Endosc Review Article BACKGROUND: Minimally invasive surgery is complex and associated with substantial learning curves. Computer-aided anatomy recognition, such as artificial intelligence-based algorithms, may improve anatomical orientation, prevent tissue injury, and improve learning curves. The study objective was to provide a comprehensive overview of current literature on the accuracy of anatomy recognition algorithms in intrathoracic and -abdominal surgery. METHODS: This systematic review is reported according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guideline. Pubmed, Embase, and IEEE Xplore were searched for original studies up until January 2022 on computer-aided anatomy recognition, without requiring intraoperative imaging or calibration equipment. Extracted features included surgical procedure, study population and design, algorithm type, pre-training methods, pre- and post-processing methods, data augmentation, anatomy annotation, training data, testing data, model validation strategy, goal of the algorithm, target anatomical structure, accuracy, and inference time. RESULTS: After full-text screening, 23 out of 7124 articles were included. Included studies showed a wide diversity, with six possible recognition tasks in 15 different surgical procedures, and 14 different accuracy measures used. Risk of bias in the included studies was high, especially regarding patient selection and annotation of the reference standard. Dice and intersection over union (IoU) scores of the algorithms ranged from 0.50 to 0.98 and from 74 to 98%, respectively, for various anatomy recognition tasks. High-accuracy algorithms were typically trained using larger datasets annotated by expert surgeons and focused on less-complex anatomy. Some of the high-accuracy algorithms were developed using pre-training and data augmentation. CONCLUSIONS: The accuracy of included anatomy recognition algorithms varied substantially, ranging from moderate to good. Solid comparison between algorithms was complicated by the wide variety of applied methodology, target anatomical structures, and reported accuracy measures. Computer-aided intraoperative anatomy recognition is an upcoming research discipline, but still at its infancy. Larger datasets and methodological guidelines are required to improve accuracy and clinical applicability in future research. Trial registration: PROSPERO registration number: CRD42021264226 SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s00464-022-09421-5. Springer US 2022-08-04 2022 /pmc/articles/PMC9652273/ /pubmed/35927354 http://dx.doi.org/10.1007/s00464-022-09421-5 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Review Article den Boer, R. B. de Jongh, C. Huijbers, W. T. E. Jaspers, T. J. M. Pluim, J. P. W. van Hillegersberg, R. Van Eijnatten, M. Ruurda, J. P. Computer-aided anatomy recognition in intrathoracic and -abdominal surgery: a systematic review |
title | Computer-aided anatomy recognition in intrathoracic and -abdominal surgery: a systematic review |
title_full | Computer-aided anatomy recognition in intrathoracic and -abdominal surgery: a systematic review |
title_fullStr | Computer-aided anatomy recognition in intrathoracic and -abdominal surgery: a systematic review |
title_full_unstemmed | Computer-aided anatomy recognition in intrathoracic and -abdominal surgery: a systematic review |
title_short | Computer-aided anatomy recognition in intrathoracic and -abdominal surgery: a systematic review |
title_sort | computer-aided anatomy recognition in intrathoracic and -abdominal surgery: a systematic review |
topic | Review Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9652273/ https://www.ncbi.nlm.nih.gov/pubmed/35927354 http://dx.doi.org/10.1007/s00464-022-09421-5 |
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