An observational study in Robot Surgery, sponsored by Aalborg University. Completed at 1 site in Denmark. Open to participants aged 18 Years and older, including healthy volunteers. Per ClinicalTrials.gov, last updated 2024-09-26.
Sponsored by Aalborg University · Observational
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:
Aalborg University is the lead sponsor of 178 studies on the registry; 37 are open to participants now.
Counted across the registry records on this site, refreshed daily.
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.
Inclusion Criteria:
Robot surgeons with 100 or more performed robot surgical cases.
Other: observational study
Robot surgeons with less than 100 performed robot surgical cases.
Other: observational study
This was an observational study with no intervention.
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.
Time frame: 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.
Time frame: 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.
Time frame: 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.
Time frame: 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.
Time frame: From the start to the end of the clinical procedures.
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.
Time frame: From the start to the end of the clinical procedures.
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.
Time frame: From start to end of a the robot surgical procedure that is being assessed in terms of action recognition.
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.
Time frame: 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.
Time frame: 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.
Time frame: From the start to the end of the clinical procedures.
Plan to share: Yes — 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.
Supporting information: Study protocol, Analytic code
This study is completed, as verified in Sep 2024. You cannot join it, but the record below documents what was studied.
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Aalborg University