Robot SurgeryNCT06612606
Transfer Learning of a Neural Network for Robotic Surgical Assessment
An observational study of Robot Surgery, run by Aalborg University at 1 site in Denmark.
Trial 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. Read more about this study
- Who is it for?
- Ages 18 years and olderHealthy volunteers can take part
- Conditions the study is for
- Robot Surgery
- What is being tested?
- observational study
- How many people
- 5 participants
- Where
- 1 siteDenmark
- Study dates
- 22 May 2023to 26 May 2023
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- All sexes
- Healthy volunteers accepted
- Adult, Older adult
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.
Who may be able to join
- 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.
Who cannot join
None listed.
Trial locations and contacts
1 site in 1 country.
Denmark1 site
Department of urology, Aalborg University Hospital
Aalborg, North Jutland, 9000
About this study
Official title: Transfer Learning of a Pretrained Preclinical Neural Network for Robotic Surgical Assessment on Limited Clinical Data
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.
Read more
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.
- Conditions
- Robot Surgery
- Being tested
- observational study
- Keywords
- deep learning, transfer learning, robot surgery, surgical assessment
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
observational studyOther
This was an observational study with no intervention.
| Study group | Role | What they receive |
|---|---|---|
| Experienced robot surgeons | Robot surgeons with 100 or more performed robot surgical cases. Receives: Other: observational study | |
| Novice robot surgeons | Robot surgeons with less than 100 performed robot surgical cases. Receives: Other: observational study |
What is being measured
The primary outcome is the main question the study is designed to answer.
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.
Measured: 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.
Measured: 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.
Measured: 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.
Measured: 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.
Measured: 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.
Measured: From the start to the end of the clinical procedures.
Secondary outcomes (4)
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.
Measured: 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.
Measured: 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.
Measured: 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.
Measured: From the start to the end of the clinical procedures.
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted25 Sep 2024
- Study start22 May 2023
- Primary completion26 May 2023
- Study completion26 May 2023
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (1)Papers the sponsor has linked to this study.
- Other trials for Robot SurgeryStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Cross-sectional
- Enrolment
- 5 participants (actual)
Sponsor and investigators
- Lead sponsor
- Aalborg University
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Nasseh Hashemi, Aalborg University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06612606
- Sponsor's protocol ID
- 2021-247
- Status last verified
- Sep 2024
- First submitted
- 22 Sep 2024
- Last updated
- 26 Sep 2024
- Results posted
- No
- Data monitoring committee
- Yes
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- 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.
Publications
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.
PubMed 41276704
Study at a glance
From the registry record, last updated by the sponsor on 26 Sep 2024. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Transfer Learning of a Pretrained Preclinical Neural Network for Robotic Surgical Assessment on Limited Clinical Data
- ClinicalTrials.gov ID
- NCT06612606
- Acronym / sponsor ID
- 2021-247
- Conditions
- Robot Surgery
- Keywords
- deep learning, transfer learning, robot surgery, surgical assessment
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 5 participants (actual)
- Lead sponsor
- Aalborg UniversityUniversity, hospital or other
- Study start
- 22 May 2023 (actual)
- Primary completion
- 26 May 2023 (actual)
- Study completion
- 26 May 2023 (actual)
- First posted
- 25 Sep 2024 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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 design
- Observational model
- Cohort
- Time perspective
- Cross-sectional
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| Experienced robot surgeons | – | Other: observational studyRobot surgeons with 100 or more performed robot surgical cases. |
| Novice robot surgeons | – | Other: observational studyRobot surgeons with less than 100 performed robot surgical cases. |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Adult, Older adult
- 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.
Key inclusion criteria (3 listed)
- 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.
Key exclusion criteria (0 listed)
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| observational studyThis was an observational study with no intervention. | Other | Experienced robot surgeons, Novice robot surgeons |
Primary outcome
Accuracy of action recognition using clinical data from scratch
Time frame: 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.
Accuracy of skills assessment using clinical data from scratch
Time frame: 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
Time frame: 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
Time frame: 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.
Time frame: 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.
K fold accuracies for action recognition and skills assessment for the partial retraining of the pretrained network.
Time frame: 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.
4 secondary outcomes
Weighted recall/sensitivity, precision and F1 score for action recognition of 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
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.
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.
Time frame: From the start to the end of the clinical procedures.
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 26 Sep 2024.
- Derived from this study: PubMed 41276704Hashemi 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.
- The URL for the IPD, also the site where all the datasets and codes will be made available as open source.Link provided by the sponsor.
- Individual participant dataSharing planned: 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.
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
The record lists no contact. Use the official record to reach the sponsor.
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Robot Surgery, run by Aalborg University at 1 site in Denmark.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 3 inclusion and 0 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: finished, as of the sponsor's last update on 26 Sep 2024. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in Denmark. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Accuracy of action recognition using clinical data from scratch (time frame: From start to end of a the robot surgical procedure that is being assessed in terms of action recognition.). The study lists 6 primary outcomes in all. It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT06612606, which also lists 1 linked publication.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 26 Sep 2024.