Robot Surgery
Conditions
Keywords
deep learning, transfer learning, robot surgery, surgical assessment
Brief summary
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.
Interventions
OTHERobservational study
This was an observational study with no intervention.
Sponsors
Aalborg University
Study design
Observational model
COHORT
Time perspective
CROSS_SECTIONAL
Eligibility
Sex/Gender
ALL
Age
18 Years to No maximum
Healthy volunteers
Yes
Inclusion criteria
* Robot surgeons who are experienced with more than 100 cases. * Robot surgical fellows with less than 100 cases. * Robot surgeons who worked at the urological department of Aalborg University Hospital.
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| K fold accuracies for action recognition and skills assessment for the partial retraining of the pretrained network. | From the start to the end of the clinical procedures. | 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. |
| Accuracy of skills assessment using clinical data from scratch | From start to end of a the robot surgical procedure that is being assessed in terms of action recognition. | Accuracy of the deep learning algorithm for skills assessment, when training the model from scratch using clinical data from robot surgical procedures. |
| Accuracy of action recognition using the pretrained network directly on clinical data | From start to end of a the robot surgical procedure that is being assessed in terms of action recognition. | Accuracy of the pretrained deep learning algorithm for action recognition, when using the model directly on clinical data from robot surgical procedures. |
| Accuracy of skills assessment using the pretrained model directly on clinical data | From start to end of a the robot surgical procedure that is being assessed in terms of skills assessment. | Accuracy of the pretrained deep learning algorithm for skills assessment, when using the model directly on clinical data from robot surgical procedures. |
| K fold accuracies for action recognition and skills assessment for the complete retraining of the pretrained network. | From the start to the end of the clinical procedures. | K fold cross-validation accuracies when retraining the complete pretrained model on the clinical data for both action recognition and skills assessment. |
| Accuracy of action recognition using clinical data from scratch | From start to end of a the robot surgical procedure that is being assessed in terms of action recognition. | Accuracy of the deep learning algorithm for action recognition, when training the model from scratch using clinical data from robot surgical procedures. |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Weighted recall/sensitivity, precision and F1 score for Skills Assessment of 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.. | Based on the performance of skills assessment from the clinical network trained from scratch. |
| Predictive certainty of the action recognition and skills assessment of the network trained from scratch on the clinical data. | From the start to the end of the clinical procedures. | 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. |
| Predictive certainty of the action recognition and skills assessment of the network partially retrained network. | From the start to the end of the clinical procedures. | 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. |
| Weighted recall/sensitivity, precision and F1 score for action recognition of 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. | Based on the performance of action recognition from the clinical network trained from scratch. |
Countries
Denmark
Outcome results
None listed