A Rectal Cancer Surgery Dataset: Use of artificial intelligence to aid automation of error identification
Abstract Minimally invasive surgery is complex and prone to variation not routinely objectively measured. We established an association between skills and patient outcomes. The evolving application of artificial intelligence techniques could assist intraoperative analysis. In this study, we analysed...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-01-01
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| Series: | Scientific Data |
| Online Access: | https://doi.org/10.1038/s41597-024-04152-9 |
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| _version_ | 1832572025302941696 |
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| author | Walaa Ghamrawi Nathan Curtis Jialang Xu Matt Boal Evangelos Mazomenos Eddie Edwards Danail Stoyanov Nader Francis |
| author_facet | Walaa Ghamrawi Nathan Curtis Jialang Xu Matt Boal Evangelos Mazomenos Eddie Edwards Danail Stoyanov Nader Francis |
| author_sort | Walaa Ghamrawi |
| collection | DOAJ |
| description | Abstract Minimally invasive surgery is complex and prone to variation not routinely objectively measured. We established an association between skills and patient outcomes. The evolving application of artificial intelligence techniques could assist intraoperative analysis. In this study, we analysed 77 rectal cancer operations’ videos from a multicentre RCT that were recorded unedited and underwent blinded manual analysis using a validated, bespoke performance assessment tool (LapTMEpt) and the Objective Clinical Human Reliability Analysis (OCHRA). The OCHRA methodology involved segmentation of the 77 operations and manually annotating each case for the enacted errors and near misses. We provide a detailed description of the errors and near misses of over 380 hours of video analysis, containing 1377 errors. This dataset can inform machine learning to assist progress toward a fully automated, objective assessment of surgical skills. |
| format | Article |
| id | doaj-art-b01ca08b67474c3692cdd448ee83530c |
| institution | Kabale University |
| issn | 2052-4463 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Data |
| spelling | doaj-art-b01ca08b67474c3692cdd448ee83530c2025-02-02T12:08:01ZengNature PortfolioScientific Data2052-44632025-01-011211710.1038/s41597-024-04152-9A Rectal Cancer Surgery Dataset: Use of artificial intelligence to aid automation of error identificationWalaa Ghamrawi0Nathan Curtis1Jialang Xu2Matt Boal3Evangelos Mazomenos4Eddie Edwards5Danail Stoyanov6Nader Francis7The Griffin Institute, Northwick Park and St Mark’s Hospital, Watford RoadDorset County Hospital Foundation NHS Trust, Williams AveWellcome/EPSRC Centre for Interventional and Surgical Sciences (WEISS), University College LondonThe Griffin Institute, Northwick Park and St Mark’s Hospital, Watford RoadWellcome/EPSRC Centre for Interventional and Surgical Sciences (WEISS), University College LondonWellcome/EPSRC Centre for Interventional and Surgical Sciences (WEISS), University College LondonWellcome/EPSRC Centre for Interventional and Surgical Sciences (WEISS), University College LondonThe Griffin Institute, Northwick Park and St Mark’s Hospital, Watford RoadAbstract Minimally invasive surgery is complex and prone to variation not routinely objectively measured. We established an association between skills and patient outcomes. The evolving application of artificial intelligence techniques could assist intraoperative analysis. In this study, we analysed 77 rectal cancer operations’ videos from a multicentre RCT that were recorded unedited and underwent blinded manual analysis using a validated, bespoke performance assessment tool (LapTMEpt) and the Objective Clinical Human Reliability Analysis (OCHRA). The OCHRA methodology involved segmentation of the 77 operations and manually annotating each case for the enacted errors and near misses. We provide a detailed description of the errors and near misses of over 380 hours of video analysis, containing 1377 errors. This dataset can inform machine learning to assist progress toward a fully automated, objective assessment of surgical skills.https://doi.org/10.1038/s41597-024-04152-9 |
| spellingShingle | Walaa Ghamrawi Nathan Curtis Jialang Xu Matt Boal Evangelos Mazomenos Eddie Edwards Danail Stoyanov Nader Francis A Rectal Cancer Surgery Dataset: Use of artificial intelligence to aid automation of error identification Scientific Data |
| title | A Rectal Cancer Surgery Dataset: Use of artificial intelligence to aid automation of error identification |
| title_full | A Rectal Cancer Surgery Dataset: Use of artificial intelligence to aid automation of error identification |
| title_fullStr | A Rectal Cancer Surgery Dataset: Use of artificial intelligence to aid automation of error identification |
| title_full_unstemmed | A Rectal Cancer Surgery Dataset: Use of artificial intelligence to aid automation of error identification |
| title_short | A Rectal Cancer Surgery Dataset: Use of artificial intelligence to aid automation of error identification |
| title_sort | rectal cancer surgery dataset use of artificial intelligence to aid automation of error identification |
| url | https://doi.org/10.1038/s41597-024-04152-9 |
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