[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"health-study-detail:100658894":3},{"organization":4,"outcomesModule":7,"designInfo":27,"detailedDescription":30,"studyPopulation":31,"armGroups":32,"interventions":26,"overallOfficials":26,"centralContacts":36,"locations":42,"responsibleParty":60,"collaborators":26,"id":62,"slug":63,"hasResults":64,"nctId":65,"briefTitle":66,"officialTitle":67,"acronym":68,"eligibilityCriteria":69,"healthyVolunteers":64,"sex":70,"minAge":71,"maxAge":26,"enrollmentInfo":72,"targetDuration":26,"studyType":75,"phases":26,"briefSummary":76,"conditions":77,"keywords":79,"overallStatus":45,"whyStopped":26,"lastUpdateSubmitDate":88,"lastUpdatePostDateStruct":89,"startDateStruct":92,"completionDateStruct":94,"leadSponsor":96,"locationsCount":97},{"fullName":5,"class":6},"University of Colorado, Denver","OTHER",{"primaryOutcomes":8,"secondaryOutcomes":13,"otherOutcomes":26},[9],{"measure":10,"description":11,"timeFrame":12},"Sensitivity of the AI System for Retained Foreign Object Detection","Sensitivity will be calculated as the proportion of reference-standard positive examinations containing a retained foreign object that are correctly identified as positive by the AI system. The reference standard will be established using available clinical information, including finalized radiology reports, operative documentation, follow-up imaging when available, and expert adjudication of discordant cases when needed.","Through completion of the 12-month prospective study period",[14,17,20,23],{"measure":15,"description":16,"timeFrame":12},"Specificity of the AI System for Retained Foreign Object Detection","Specificity will be calculated as the proportion of reference-standard negative examinations without a retained foreign object that are correctly identified as negative by the AI system.",{"measure":18,"description":19,"timeFrame":12},"Area Under the Receiver Operating Characteristic Curve for RFO Detection","The area under the receiver operating characteristic curve (AUROC) will be calculated using AI-generated detection scores and the reference-standard classification of examinations as positive or negative for a retained foreign object. AUROC ranges from 0 to 1, with higher values indicating greater discrimination between positive and negative examinations.",{"measure":21,"description":22,"timeFrame":12},"System Latency","System latency will be measured as the elapsed time between availability of an eligible radiographic examination for processing and availability of the corresponding AI output. Latency will be summarized across prospectively processed examinations to evaluate operational feasibility in the real-world clinical workflow.",{"measure":24,"description":25,"timeFrame":12},"Diagnostic Accuracy of the AI System for Retained Foreign Object Detection","Diagnostic accuracy will be calculated as the proportion of all prospectively evaluated examinations that are correctly classified by the AI system as positive or negative for a retained foreign object compared with the reference standard.",null,{"allocation":26,"interventionModel":26,"interventionModelDescription":26,"primaryPurpose":26,"observationalModel":28,"timePerspective":29,"maskingInfo":26},"COHORT","PROSPECTIVE","This is a prospective, non-interventional, silent observational study evaluating the real-world performance and operational feasibility of an artificial intelligence system for detection of retained foreign objects (RFOs) on intraoperative radiographs.\n\nConsecutive eligible OR count radiographs obtained during routine clinical care will be identified using institutional imaging protocol labels and automatically routed from the clinical imaging environment to a secure local research inference system within the institutional firewall. The AI system will process eligible images in real time and generate research outputs, including detection probabilities, localization bounding boxes, and processing timestamps.\n\nThe study is conducted in a silent fashion. AI-generated results will not be displayed to interpreting radiologists, surgeons, or other members of the clinical care team and will not be used for clinical decision-making or patient management. No additional imaging, procedure, intervention, or patient contact is required as part of the prospective study.\n\nReference standards for evaluation of AI performance will be established using available clinical information, including finalized radiology reports, operative documentation, follow-up imaging when available, and expert adjudication of discordant cases when needed.\n\nThe study will evaluate standalone AI diagnostic performance using measures including sensitivity, specificity, and area under the receiver operating characteristic curve. Additional measures will evaluate localization performance and operational feasibility, including system latency from image acquisition to availability of the AI output. Exploratory analyses may evaluate performance according to factors such as retained object type, surgical context, and image characteristics.\n\nThe prospective evaluation is planned to include approximately 12 months of consecutive eligible examinations and is intended to characterize the performance of the AI system under real-world clinical workflow conditions without influencing clinical care.","The study population consists of consecutive adult patients undergoing intraoperative OR count radiography as part of routine clinical care at University of Colorado\u002FUCHealth. Eligible examinations are prospectively identified using institutional OR count imaging protocol labels and processed by the AI system in real time. AI outputs are collected for research purposes only and are not shown to the clinical care team or used to guide patient management.",[33],{"label":34,"type":26,"description":35,"interventionNames":26},"Silent AI Analysis of OR Count Radiographs","Eligible OR count radiographs obtained during routine clinical care are automatically processed by an artificial intelligence system designed to detect and localize retained foreign objects. The system generates research outputs including detection probabilities, localization bounding boxes, and processing timestamps. During the prospective silent evaluation, these outputs are not displayed to clinicians and are not used for clinical decision-making or patient management.",[37],{"name":38,"role":39,"phone":40,"phoneExt":26,"email":41},"Harrison Bai, MD","CONTACT","720-516-0637","harrison.bai@cuanschutz.edu",[43],{"facility":44,"status":45,"city":46,"state":47,"zip":48,"country":49,"countryCode":50,"cosmosGeoPoint":51,"geoPoint":56,"contacts":57},"University of Colorado Hospital","RECRUITING","Aurora","Colorado","80045","United States","US",{"type":52,"coordinates":53},"Point",[54,55],-104.83192,39.72943,{"lat":55,"lon":54},[58],{"name":59,"role":39,"phone":40,"phoneExt":26,"email":41},"Harrison Bai",{"type":61,"investigatorFullName":26,"investigatorTitle":26,"investigatorAffiliation":26,"oldNameTitle":26,"oldOrganization":26},"SPONSOR","100658894","prospective-silent-evaluation-of-ai-for-retained-foreign-object-detection-100658894",false,"NCT07858162","Prospective Silent Evaluation of AI for Retained Foreign Object Detection","Prospective Silent Evaluation of a Real-Time Artificial Intelligence System for Detection of Retained Foreign Objects on Intraoperative Radiographs","RFO-AI","Inclusion Criteria:\n\n* Age 18 years or older.\n* Undergoing intraoperative radiography during routine clinical care for evaluation of a possible retained foreign object.\n* Radiographic examination identified using the institutional OR count imaging protocol, including \"XR OR COUNT PROTOCOL W\u002FWO POSITIVE CONTROL\" or an equivalent institutional protocol label used for OR count imaging during the study period.\n\nExclusion Criteria:\n\n* Radiographic examination lacking usable imaging data.\n* Insufficient clinical or imaging information to establish an adequate reference standard for analysis.","ALL","18 Years",{"count":73,"type":74},120,"ESTIMATED","OBSERVATIONAL","Retained foreign objects, such as surgical sponges, needles, or instruments, are rare but potentially serious complications of surgery. When there is concern that an object may have been left behind, an intraoperative radiograph may be obtained while the patient is still in the operating room. These radiographs can be difficult to interpret because of overlapping surgical equipment, patient positioning, and the need for rapid interpretation.\n\nThis prospective observational study evaluates an artificial intelligence (AI) system designed to detect retained foreign objects on intraoperative radiographs. Eligible radiographs obtained during routine clinical care will be automatically processed by the AI system in real time for approximately one year. The AI results will not be shown to radiologists, surgeons, or other members of the clinical care team and will not affect patient care.\n\nThe study will evaluate how accurately the AI system detects retained foreign objects and how reliably and quickly the system operates in a real-world clinical environment.",[78],"Retained Foreign Object",[78,80,81,82,83,84,85,86,87],"Retained Surgical Item","Retained Foreign Body","Artificial Intelligence","Deep Learning","Object Detection","Intraoperative Radiography","OR Count Radiograph","Surgical Safety","2026-09-29",{"date":90,"type":91},"2026-10-05","ACTUAL",{"date":93,"type":91},"2026-02-01",{"date":95,"type":74},"2027-02-01",{"name":5,"class":6},1]