Nctid:
NCT06612606
Payload:
{"hasResults"=>false, "derivedSection"=>{"miscInfoModule"=>{"versionHolder"=>"2024-12-20"}}, "protocolSection"=>{"designModule"=>{"studyType"=>"OBSERVATIONAL", "designInfo"=>{"timePerspective"=>"CROSS_SECTIONAL", "observationalModel"=>"COHORT"}, "enrollmentInfo"=>{"type"=>"ACTUAL", "count"=>5}, "patientRegistry"=>false}, "statusModule"=>{"overallStatus"=>"COMPLETED", "startDateStruct"=>{"date"=>"2023-05-22", "type"=>"ACTUAL"}, "expandedAccessInfo"=>{"hasExpandedAccess"=>false}, "statusVerifiedDate"=>"2024-09", "completionDateStruct"=>{"date"=>"2023-05-26", "type"=>"ACTUAL"}, "lastUpdateSubmitDate"=>"2024-09-24", "studyFirstSubmitDate"=>"2024-09-22", "studyFirstSubmitQcDate"=>"2024-09-22", "lastUpdatePostDateStruct"=>{"date"=>"2024-09-26", "type"=>"ACTUAL"}, "studyFirstPostDateStruct"=>{"date"=>"2024-09-25", "type"=>"ACTUAL"}, "primaryCompletionDateStruct"=>{"date"=>"2023-05-26", "type"=>"ACTUAL"}}, "outcomesModule"=>{"primaryOutcomes"=>[{"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.", "description"=>"Accuracy of the deep learning algorithm for action recognition, when training the model from scratch using clinical data from robot surgical procedures."}, {"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.", "description"=>"Accuracy of the deep learning algorithm for skills assessment, when training the model from scratch using clinical data from robot surgical procedures."}, {"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.", "description"=>"Accuracy of the pretrained deep learning algorithm for action recognition, when using the model directly on clinical data from robot surgical procedures."}, {"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.", "description"=>"Accuracy of the pretrained deep learning algorithm for skills assessment, when using the model directly on clinical data from robot surgical procedures."}, {"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.", "description"=>"K fold cross-validation accuracies when retraining the complete pretrained model on the clinical data for both action recognition and skills assessment."}, {"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.", "description"=>"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."}], "secondaryOutcomes"=>[{"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.", "description"=>"Based on the performance of action recognition from the clinical network trained from scratch."}, {"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..", "description"=>"Based on the performance of skills assessment from the clinical network trained from scratch."}, {"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.", "description"=>"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."}, {"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.", "description"=>"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."}]}, "oversightModule"=>{"isUsExport"=>false, "oversightHasDmc"=>true, "isFdaRegulatedDrug"=>false, "isFdaRegulatedDevice"=>false}, "conditionsModule"=>{"keywords"=>["deep learning", "transfer learning", "robot surgery", "surgical assessment"], "conditions"=>["Robot Surgery"]}, "referencesModule"=>{"seeAlsoLinks"=>[{"url"=>"https://github.com/NasHas", "label"=>"The URL for the IPD, also the site where all the datasets and codes will be made available as open source."}]}, "descriptionModule"=>{"briefSummary"=>"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:\n\n* Can pretrained AI models accurately assess action recognition and skills assessment in clinical surgeries?\n* 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.\n\nParticipants who are robot surgeons will:\n\n* Undergo RAS procedures on patients, with no intervention, where video data will be collected for later action recognition and skills assessment.\n* Contribute to model training and evaluation through clinical dataset integration."}, "eligibilityModule"=>{"sex"=>"ALL", "stdAges"=>["ADULT", "OLDER_ADULT"], "minimumAge"=>"18 years", "samplingMethod"=>"NON_PROBABILITY_SAMPLE", "studyPopulation"=>"The study population consisted of robot surgeons who where either experienced or novice (being specialized doctors undergoing surgical fellowship to become robot surgeons).\n\nAll 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.", "healthyVolunteers"=>true, "eligibilityCriteria"=>"Inclusion Criteria:\n\n* Robot surgeons who are experienced with more than 100 cases.\n* Robot surgical fellows with less than 100 cases.\n* Robot surgeons who worked at the urological department of Aalborg University Hospital."}, "identificationModule"=>{"nctId"=>"NCT06612606", "briefTitle"=>"Transfer Learning of a Neural Network for Robotic Surgical Assessment", "organization"=>{"class"=>"OTHER", "fullName"=>"Aalborg University"}, "officialTitle"=>"Transfer Learning of a Pretrained Preclinical Neural Network for Robotic Surgical Assessment on Limited Clinical Data", "orgStudyIdInfo"=>{"id"=>"2021-247"}}, "armsInterventionsModule"=>{"armGroups"=>[{"label"=>"Experienced robot surgeons", "description"=>"Robot surgeons with 100 or more performed robot surgical cases.", "interventionNames"=>["Other: observational study"]}, {"label"=>"Novice robot surgeons", "description"=>"Robot surgeons with less than 100 performed robot surgical cases.", "interventionNames"=>["Other: observational study"]}], "interventions"=>[{"name"=>"observational study", "type"=>"OTHER", "description"=>"This was an observational study with no intervention.", "armGroupLabels"=>["Experienced robot surgeons", "Novice robot surgeons"]}]}, "contactsLocationsModule"=>{"locations"=>[{"zip"=>"9000", "city"=>"Aalborg", "state"=>"North Jutland", "country"=>"Denmark", "facility"=>"Department of urology, Aalborg University Hospital", "geoPoint"=>{"lat"=>57.048, "lon"=>9.9187}}]}, "ipdSharingStatementModule"=>{"infoTypes"=>["STUDY_PROTOCOL", "ANALYTIC_CODE"], "timeFrame"=>"The IPD and supporting information will be available from the time of submission to the journal, and will be available for an unlimited amount of time.", "ipdSharing"=>"YES", "description"=>"The IPD will be shared as anonymous and GDPR secure data on an open access website.\n\nThe data will be shared as the anonymized footage of the surgical procedures that the participants made.", "accessCriteria"=>"Everyone who has access to the open source website of GitHub will be able to access the data. And anyone who will have access to the journal will have access to the supporting information."}, "sponsorCollaboratorsModule"=>{"leadSponsor"=>{"name"=>"Aalborg University", "class"=>"OTHER"}, "responsibleParty"=>{"type"=>"PRINCIPAL_INVESTIGATOR", "investigatorTitle"=>"Principal investigator", "investigatorFullName"=>"Nasseh Hashemi", "investigatorAffiliation"=>"Aalborg University"}}}}
