Clinical trial NCT06612606: Transfer Learning of a Neural Network for Robotic Surgical Assessment
Official title: Transfer Learning of a Pretrained Preclinical Neural Network for Robotic Surgical Assessment on Limited Clinical Data
Study type: OBSERVATIONAL
Overall status: COMPLETED
Conditions: Robot Surgery
Keywords: deep learning
transfer learning
robot surgery
surgical assessment
Lead sponsor: Aalborg University
Sponsor class: OTHER
First posted: 2024-09-25
Enrollment: 5
Sex: ALL
Minimum age (years): 18
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: yes
Observational model: COHORT
Time perspective: CROSS_SECTIONAL
Sampling method: NON_PROBABILITY_SAMPLE
Study population: 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.

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.


Eligibility criteria: 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.

Primary outcomes:
measure: Accuracy of action recognition using clinical data from scratch
timeFrame: From start to end of a the robot surgical procedure that is being assessed in terms of action recognition.
measure: Accuracy of skills assessment using clinical data from scratch
timeFrame: From start to end of a the robot surgical procedure that is being assessed in terms of action recognition.
measure: Accuracy of action recognition using the pretrained network directly on clinical data
timeFrame: From start to end of a the robot surgical procedure that is being assessed in terms of action recognition.
measure: Accuracy of skills assessment using the pretrained model directly on clinical data
timeFrame: From start to end of a the robot surgical procedure that is being assessed in terms of skills assessment.
measure: K fold accuracies for action recognition and skills assessment for the complete retraining of the pretrained network.
timeFrame: From the start to the end of the clinical procedures.
measure: K fold accuracies for action recognition and skills assessment for the partial retraining of the pretrained network.
timeFrame: From the start to the end of the clinical procedures.
Secondary outcomes:
measure: Weighted recall/sensitivity, precision and F1 score for action recognition of the clinical network trained from scratch
timeFrame: From start to end of a the robot surgical procedure that is being assessed in terms of action recognition.
measure: Weighted recall/sensitivity, precision and F1 score for Skills Assessment of the clinical network trained from scratch
timeFrame: From start to end of a the robot surgical procedure that is being assessed in terms of skills assessment..
measure: Predictive certainty of the action recognition and skills assessment of the network trained from scratch on the clinical data.
timeFrame: From the start to the end of the clinical procedures.
measure: Predictive certainty of the action recognition and skills assessment of the network partially retrained network.
timeFrame: From the start to the end of the clinical procedures.
Other outcomes:
Locations:
city: Aalborg
country: Denmark
facility: Department of urology, Aalborg University Hospital
state: North Jutland
Study officials:
References: