Automated surgical skill assessment in endoscopic pituitary surgery using real‐time instrument tracking on a high‐fidelity bench‐top phantom
Abstract Improved surgical skill is generally associated with improved patient outcomes, although assessment is subjective, labour intensive, and requires domain‐specific expertise. Automated data‐driven metrics can alleviate these difficulties, as demonstrated by existing machine learning instrumen...
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| Main Authors: | , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2024-12-01
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| Series: | Healthcare Technology Letters |
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| Online Access: | https://doi.org/10.1049/htl2.12101 |
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| author | Adrito Das Bilal Sidiqi Laurent Mennillo Zhehua Mao Mikael Brudfors Miguel Xochicale Danyal Z. Khan Nicola Newall John G. Hanrahan Matthew J. Clarkson Danail Stoyanov Hani J. Marcus Sophia Bano |
| author_facet | Adrito Das Bilal Sidiqi Laurent Mennillo Zhehua Mao Mikael Brudfors Miguel Xochicale Danyal Z. Khan Nicola Newall John G. Hanrahan Matthew J. Clarkson Danail Stoyanov Hani J. Marcus Sophia Bano |
| author_sort | Adrito Das |
| collection | DOAJ |
| description | Abstract Improved surgical skill is generally associated with improved patient outcomes, although assessment is subjective, labour intensive, and requires domain‐specific expertise. Automated data‐driven metrics can alleviate these difficulties, as demonstrated by existing machine learning instrument tracking models. However, these models are tested on limited datasets of laparoscopic surgery, with a focus on isolated tasks and robotic surgery. Here, a new public dataset is introduced: the nasal phase of simulated endoscopic pituitary surgery. Simulated surgery allows for a realistic yet repeatable environment, meaning the insights gained from automated assessment can be used by novice surgeons to hone their skills on the simulator before moving to real surgery. Pituitary Real‐time INstrument Tracking Network (PRINTNet) has been created as a baseline model for this automated assessment. Consisting of DeepLabV3 for classification and segmentation, StrongSORT for tracking, and the NVIDIA Holoscan for real‐time performance, PRINTNet achieved 71.9% multiple object tracking precision running at 22 frames per second. Using this tracking output, a multilayer perceptron achieved 87% accuracy in predicting surgical skill level (novice or expert), with the ‘ratio of total procedure time to instrument visible time’ correlated with higher surgical skill. The new publicly available dataset can be found at https://doi.org/10.5522/04/26511049. |
| format | Article |
| id | doaj-art-75051a1e19924338974d863934da9873 |
| institution | Kabale University |
| issn | 2053-3713 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Wiley |
| record_format | Article |
| series | Healthcare Technology Letters |
| spelling | doaj-art-75051a1e19924338974d863934da98732024-12-23T18:23:22ZengWileyHealthcare Technology Letters2053-37132024-12-0111633634410.1049/htl2.12101Automated surgical skill assessment in endoscopic pituitary surgery using real‐time instrument tracking on a high‐fidelity bench‐top phantomAdrito Das0Bilal Sidiqi1Laurent Mennillo2Zhehua Mao3Mikael Brudfors4Miguel Xochicale5Danyal Z. Khan6Nicola Newall7John G. Hanrahan8Matthew J. Clarkson9Danail Stoyanov10Hani J. Marcus11Sophia Bano12UCL Hawkes Institute University College London London UKUCL Hawkes Institute University College London London UKUCL Hawkes Institute University College London London UKUCL Hawkes Institute University College London London UKNVIDIA London UKUCL Hawkes Institute University College London London UKUCL Hawkes Institute University College London London UKUCL Hawkes Institute University College London London UKUCL Hawkes Institute University College London London UKUCL Hawkes Institute University College London London UKUCL Hawkes Institute University College London London UKUCL Hawkes Institute University College London London UKUCL Hawkes Institute University College London London UKAbstract Improved surgical skill is generally associated with improved patient outcomes, although assessment is subjective, labour intensive, and requires domain‐specific expertise. Automated data‐driven metrics can alleviate these difficulties, as demonstrated by existing machine learning instrument tracking models. However, these models are tested on limited datasets of laparoscopic surgery, with a focus on isolated tasks and robotic surgery. Here, a new public dataset is introduced: the nasal phase of simulated endoscopic pituitary surgery. Simulated surgery allows for a realistic yet repeatable environment, meaning the insights gained from automated assessment can be used by novice surgeons to hone their skills on the simulator before moving to real surgery. Pituitary Real‐time INstrument Tracking Network (PRINTNet) has been created as a baseline model for this automated assessment. Consisting of DeepLabV3 for classification and segmentation, StrongSORT for tracking, and the NVIDIA Holoscan for real‐time performance, PRINTNet achieved 71.9% multiple object tracking precision running at 22 frames per second. Using this tracking output, a multilayer perceptron achieved 87% accuracy in predicting surgical skill level (novice or expert), with the ‘ratio of total procedure time to instrument visible time’ correlated with higher surgical skill. The new publicly available dataset can be found at https://doi.org/10.5522/04/26511049.https://doi.org/10.1049/htl2.12101artificial intelligenceinstrument segmentationmachine learningminimally invasive surgeryneurosurgery |
| spellingShingle | Adrito Das Bilal Sidiqi Laurent Mennillo Zhehua Mao Mikael Brudfors Miguel Xochicale Danyal Z. Khan Nicola Newall John G. Hanrahan Matthew J. Clarkson Danail Stoyanov Hani J. Marcus Sophia Bano Automated surgical skill assessment in endoscopic pituitary surgery using real‐time instrument tracking on a high‐fidelity bench‐top phantom Healthcare Technology Letters artificial intelligence instrument segmentation machine learning minimally invasive surgery neurosurgery |
| title | Automated surgical skill assessment in endoscopic pituitary surgery using real‐time instrument tracking on a high‐fidelity bench‐top phantom |
| title_full | Automated surgical skill assessment in endoscopic pituitary surgery using real‐time instrument tracking on a high‐fidelity bench‐top phantom |
| title_fullStr | Automated surgical skill assessment in endoscopic pituitary surgery using real‐time instrument tracking on a high‐fidelity bench‐top phantom |
| title_full_unstemmed | Automated surgical skill assessment in endoscopic pituitary surgery using real‐time instrument tracking on a high‐fidelity bench‐top phantom |
| title_short | Automated surgical skill assessment in endoscopic pituitary surgery using real‐time instrument tracking on a high‐fidelity bench‐top phantom |
| title_sort | automated surgical skill assessment in endoscopic pituitary surgery using real time instrument tracking on a high fidelity bench top phantom |
| topic | artificial intelligence instrument segmentation machine learning minimally invasive surgery neurosurgery |
| url | https://doi.org/10.1049/htl2.12101 |
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