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: * Can pretrained AI models accurately assess action recognition and skills assessment in clinical surgeries? * How do different training approaches of transfer learning affect the performance of the AI models? A baseline model developed from scratch using clinical data will be compared to pretrained models that are (1) directly applied to clinical data (2) fine-tuned by training only some layers of the AI model, and (3) fully retrained to see if these approaches improve performance. Participants who are robot surgeons will: * Undergo RAS procedures on patients, with no intervention, where video data will be collected for later action recognition and skills assessment. * Contribute to model training and evaluation through clinical dataset integration.
Study Type
OBSERVATIONAL
Enrollment
5
This was an observational study with no intervention.
Department of urology, Aalborg University Hospital
Aalborg, North Jutland, Denmark
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.
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