PitRSDNet: Predicting intra‐operative remaining surgery duration in endoscopic pituitary surgery
Abstract Accurate intra‐operative Remaining Surgery Duration (RSD) predictions allow for anaesthetists to more accurately decide when to administer anaesthetic agents and drugs, as well as to notify hospital staff to send in the next patient. Therefore, RSD plays an important role in improved patien...
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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/htl2.12099 |
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| _version_ | 1846110672566353920 |
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| author | Anjana Wijekoon Adrito Das Roxana R. Herrera Danyal Z. Khan John Hanrahan Eleanor Carter Valpuri Luoma Danail Stoyanov Hani J. Marcus Sophia Bano |
| author_facet | Anjana Wijekoon Adrito Das Roxana R. Herrera Danyal Z. Khan John Hanrahan Eleanor Carter Valpuri Luoma Danail Stoyanov Hani J. Marcus Sophia Bano |
| author_sort | Anjana Wijekoon |
| collection | DOAJ |
| description | Abstract Accurate intra‐operative Remaining Surgery Duration (RSD) predictions allow for anaesthetists to more accurately decide when to administer anaesthetic agents and drugs, as well as to notify hospital staff to send in the next patient. Therefore, RSD plays an important role in improved patient care and minimising surgical theatre costs via efficient scheduling. In endoscopic pituitary surgery, it is uniquely challenging due to variable workflow sequences with a selection of optional steps contributing to high variability in surgery duration. This article presents PitRSDNet for predicting RSD during pituitary surgery, a spatio‐temporal neural network model that learns from historical data focusing on workflow sequences. PitRSDNet integrates workflow knowledge into RSD prediction in two forms: (1) multi‐task learning for concurrently predicting step and RSD; and (2) incorporating prior steps as context in temporal learning and inference. PitRSDNet is trained and evaluated on a new endoscopic pituitary surgery dataset with 88 videos to show competitive performance improvements over previous statistical and machine learning methods. The findings also highlight how PitRSDNet improves RSD precision on outlier cases utilising the knowledge of prior steps. |
| format | Article |
| id | doaj-art-46ed004a6abc4f09970e3298d4c70ced |
| institution | Kabale University |
| issn | 2053-3713 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Wiley |
| record_format | Article |
| series | Healthcare Technology Letters |
| spelling | doaj-art-46ed004a6abc4f09970e3298d4c70ced2024-12-23T18:23:22ZengWileyHealthcare Technology Letters2053-37132024-12-0111631832610.1049/htl2.12099PitRSDNet: Predicting intra‐operative remaining surgery duration in endoscopic pituitary surgeryAnjana Wijekoon0Adrito Das1Roxana R. Herrera2Danyal Z. Khan3John Hanrahan4Eleanor Carter5Valpuri Luoma6Danail Stoyanov7Hani J. Marcus8Sophia Bano9UCL 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 UKDepartment of Neurosurgery National Hospital for Neurology and Neurosurgery London UKDepartment of Neurosurgery National Hospital for Neurology and Neurosurgery London UKUCL Hawkes Institute University College London London UKUCL Hawkes Institute University College London London UKUCL Hawkes Institute University College London London UKAbstract Accurate intra‐operative Remaining Surgery Duration (RSD) predictions allow for anaesthetists to more accurately decide when to administer anaesthetic agents and drugs, as well as to notify hospital staff to send in the next patient. Therefore, RSD plays an important role in improved patient care and minimising surgical theatre costs via efficient scheduling. In endoscopic pituitary surgery, it is uniquely challenging due to variable workflow sequences with a selection of optional steps contributing to high variability in surgery duration. This article presents PitRSDNet for predicting RSD during pituitary surgery, a spatio‐temporal neural network model that learns from historical data focusing on workflow sequences. PitRSDNet integrates workflow knowledge into RSD prediction in two forms: (1) multi‐task learning for concurrently predicting step and RSD; and (2) incorporating prior steps as context in temporal learning and inference. PitRSDNet is trained and evaluated on a new endoscopic pituitary surgery dataset with 88 videos to show competitive performance improvements over previous statistical and machine learning methods. The findings also highlight how PitRSDNet improves RSD precision on outlier cases utilising the knowledge of prior steps.https://doi.org/10.1049/htl2.12099computer visiondecision support systems |
| spellingShingle | Anjana Wijekoon Adrito Das Roxana R. Herrera Danyal Z. Khan John Hanrahan Eleanor Carter Valpuri Luoma Danail Stoyanov Hani J. Marcus Sophia Bano PitRSDNet: Predicting intra‐operative remaining surgery duration in endoscopic pituitary surgery Healthcare Technology Letters computer vision decision support systems |
| title | PitRSDNet: Predicting intra‐operative remaining surgery duration in endoscopic pituitary surgery |
| title_full | PitRSDNet: Predicting intra‐operative remaining surgery duration in endoscopic pituitary surgery |
| title_fullStr | PitRSDNet: Predicting intra‐operative remaining surgery duration in endoscopic pituitary surgery |
| title_full_unstemmed | PitRSDNet: Predicting intra‐operative remaining surgery duration in endoscopic pituitary surgery |
| title_short | PitRSDNet: Predicting intra‐operative remaining surgery duration in endoscopic pituitary surgery |
| title_sort | pitrsdnet predicting intra operative remaining surgery duration in endoscopic pituitary surgery |
| topic | computer vision decision support systems |
| url | https://doi.org/10.1049/htl2.12099 |
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