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The goal of this observational study is to explore how pretrained artificial intelligence (AI) models, trained on preclinical data, can improve the accuracy of action recognition and skills assessment in robot-assisted surgery (RAS) in urological patients by the use of transfer learning. The main questions it aims to answer are:
Participants who are robot surgeons will:
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| Experienced robot surgeons | Robot surgeons with 100 or more performed robot surgical cases. |
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| Novice robot surgeons | Robot surgeons with less than 100 performed robot surgical cases. |
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| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| observational study | Other | This was an observational study with no intervention. |
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| Measure | Description | Time Frame |
|---|---|---|
| Accuracy of action recognition using clinical data from scratch | Accuracy of the deep learning algorithm for action recognition, when training the model from scratch using clinical data from robot surgical procedures. | From start to end of a the robot surgical procedure that is being assessed in terms of action recognition. |
| Accuracy of skills assessment using clinical data from scratch | Accuracy of the deep learning algorithm for skills assessment, when training the model from scratch using clinical data from robot surgical procedures. | From start to end of a the robot surgical procedure that is being assessed in terms of action recognition. |
| Accuracy of action recognition using the pretrained network directly on clinical data | Accuracy of the pretrained deep learning algorithm for action recognition, when using the model directly on clinical data from robot surgical procedures. | From start to end of a the robot surgical procedure that is being assessed in terms of action recognition. |
| Accuracy of skills assessment using the pretrained model directly on clinical data | Accuracy of the pretrained deep learning algorithm for skills assessment, when using the model directly on clinical data from robot surgical procedures. | From start to end of a the robot surgical procedure that is being assessed in terms of skills assessment. |
| K fold accuracies for action recognition and skills assessment for the complete retraining of the pretrained network. | K fold cross-validation accuracies when retraining the complete pretrained model on the clinical data for both action recognition and skills assessment. | From the start to the end of the clinical procedures. |
| Measure | Description | Time Frame |
|---|---|---|
| Weighted recall/sensitivity, precision and F1 score for action recognition of the clinical network trained from scratch | Based on the performance of action recognition from the clinical network trained from scratch. | From start to end of a the robot surgical procedure that is being assessed in terms of action recognition. |
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Inclusion Criteria:
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The study population consisted of robot surgeons who where either experienced or novice (being specialized doctors undergoing surgical fellowship to become robot surgeons).
All procedures where robot-assisted procedures done on patients, who were admitted for treatment at the urological department. The patients also gave their consent regarding data collection. However, the real participant where the robot surgeons.
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| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Department of urology, Aalborg University Hospital | Aalborg | North Jutland | 9000 | Denmark |
| PubMed Identifier | Type | Citation | Retractions |
|---|---|---|---|
| 41276704 | Derived | Hashemi N, Mose M, Ostergaard LR, Bjerrum F, Sogaard-Andersen E, Fabrin K, Tuckus G, Friis ML, Rasmussen S, Tolsgaard MG. Closing the data gap: leveraging pretrained neural networks for robotic surgical assessment on limited clinical data. J Robot Surg. 2025 Nov 24;20(1):39. doi: 10.1007/s11701-025-02994-y. |
| Label | URL |
|---|---|
| The URL for the IPD, also the site where all the datasets and codes will be made available as open source. | View source |
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The IPD will be shared as anonymous and GDPR secure data on an open access website.
The data will be shared as the anonymized footage of the surgical procedures that the participants made.
The IPD and supporting information will be available from the time of submission to the journal, and will be available for an unlimited amount of time.
Everyone who has access to the open source website of GitHub will be able to access the data. And anyone who will have access to the journal will have access to the supporting information.
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| ID | Term |
|---|---|
| D019370 | Observation |
| ID | Term |
|---|---|
| D008722 | Methods |
| D008919 | Investigative Techniques |
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| K fold accuracies for action recognition and skills assessment for the partial retraining of the pretrained network. | K fold cross validation accuracies for action recognition and skills assessment for the retraining of the LSTM and dense layers of the pretrained network using clinical data. | From the start to the end of the clinical procedures. |
| Weighted recall/sensitivity, precision and F1 score for Skills Assessment of the clinical network trained from scratch |
Based on the performance of skills assessment from the clinical network trained from scratch. |
| From start to end of a the robot surgical procedure that is being assessed in terms of skills assessment.. |
| Predictive certainty of the action recognition and skills assessment of the network trained from scratch on the clinical data. | Predictive certainty with overall mean, minimum and maximum and depicted in probability plots for action recognition and skills assessment of the network trained from scratch on clinical data. | From the start to the end of the clinical procedures. |
| Predictive certainty of the action recognition and skills assessment of the network partially retrained network. | Predictive certainty with overall mean, minimum and maximum and depicted in probability plots for action recognition and skills assessment of the partially retrained network, where only the LSTM and deep layers of the network was trained on clinical data. | From the start to the end of the clinical procedures. |