[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"health-study-detail:100633868":3},{"organization":4,"outcomesModule":7,"designInfo":25,"detailedDescription":28,"studyPopulation":29,"armGroups":30,"interventions":36,"overallOfficials":24,"centralContacts":42,"locations":24,"responsibleParty":48,"collaborators":24,"id":51,"slug":24,"hasResults":52,"nctId":53,"briefTitle":54,"officialTitle":55,"acronym":24,"eligibilityCriteria":56,"healthyVolunteers":52,"sex":57,"minAge":58,"maxAge":59,"enrollmentInfo":60,"targetDuration":24,"studyType":63,"phases":24,"briefSummary":64,"conditions":65,"keywords":67,"overallStatus":74,"whyStopped":24,"lastUpdateSubmitDate":75,"lastUpdatePostDateStruct":76,"startDateStruct":79,"completionDateStruct":80,"leadSponsor":82,"locationsCount":24},{"fullName":5,"class":6},"Gaziantep City Hospital","OTHER",{"primaryOutcomes":8,"secondaryOutcomes":13,"otherOutcomes":24},[9],{"measure":10,"description":11,"timeFrame":12},"Agreement in Target Point Identification Between Artificial Intelligence and Anesthesiologists","Agreement between artificial intelligence (AI) and experienced anesthesiologists in identifying target injection points will be evaluated using the intraclass correlation coefficient (ICC). Target points will be defined using coordinate-based measurements on real-time ultrasound images.","During block procedure (ultrasound imaging)",[14,18,21],{"measure":15,"description":16,"timeFrame":17},"Proportion of Measurements Within Clinically Acceptable Agreement","The proportion of target point measurements with a difference of ≤5 mm between AI and anesthesiologists will be calculated. A difference of ≤5 mm will be considered clinically acceptable agreement.","During block procedure",{"measure":19,"description":20,"timeFrame":17},"Agreement in Nerve Identification","Agreement between AI and anesthesiologists in identifying peripheral nerves (median, ulnar, radial, and musculocutaneous) will be evaluated using Cohen's kappa coefficient.",{"measure":22,"description":23,"timeFrame":17},"Spatial Difference Between Target Points","The absolute distance (in millimeters) between AI-generated and anesthesiologist-defined target points will be calculated for each measurement.",null,{"allocation":24,"interventionModel":24,"interventionModelDescription":24,"primaryPurpose":24,"observationalModel":26,"timePerspective":27,"maskingInfo":24},"CASE_ONLY","PROSPECTIVE","Ultrasound-guided axillary brachial plexus block is a widely used regional anesthesia technique for upper extremity surgeries. The success of the procedure largely depends on accurate identification of neural structures and optimal injection points, which are operator-dependent.\n\nArtificial intelligence (AI) has recently emerged as a promising tool for assisting ultrasound interpretation by automatically identifying anatomical structures. However, the level of agreement between AI-based target point identification and expert anesthesiologists has not been sufficiently investigated, particularly in axillary brachial plexus block.\n\nIn this prospective observational study, patients undergoing upper extremity surgery under axillary brachial plexus block will be included. No additional intervention will be performed on patients within the scope of the study. All evaluations will be based on real-time ultrasound imaging obtained as part of routine clinical practice.\n\nDuring routine ultrasound examination prior to block performance, images will be observed in real time. Experienced anesthesiologists will determine anatomical structures and optimal target injection points during the procedure. Simultaneously, the AI-based system will analyze the same real-time ultrasound images and identify target points.\n\nFor each identified nerve (median, ulnar, radial, and musculocutaneous), both AI and anesthesiologists will determine target injection points. The spatial difference between AI-generated and expert-defined target points will be calculated in millimeters.\n\nThe primary objective is to evaluate the agreement between AI and anesthesiologists in target point identification using the intraclass correlation coefficient (ICC). Additionally, a difference of ≤5 mm between measurements will be considered clinically acceptable agreement.\n\nSecondary outcomes will include:\n\nProportion of measurements within ≤5 mm agreement Agreement in nerve identification Procedure-related parameters\n\nAll expert evaluations will be performed independently and blinded to AI outputs.\n\nThis study aims to determine whether AI can reliably assist clinicians in identifying anatomical targets during ultrasound-guided regional anesthesia without introducing any additional risk to patients.","Adult patients aged 18-80 years with American Society of Anesthesiologists (ASA) physical status I-III undergoing upper extremity surgery under ultrasound-guided axillary brachial plexus block in routine clinical practice. Only patients with adequate real-time ultrasound imaging of the axillary region allowing identification of relevant anatomical structures will be included.",[31],{"label":32,"type":24,"description":33,"interventionNames":34},"Patients Undergoing Ultrasound-Guided Axillary Brachial Plexus Block","Patients undergoing upper extremity surgery in whom ultrasound-guided axillary brachial plexus block is performed as part of routine clinical practice. Real-time ultrasound images will be evaluated simultaneously by an artificial intelligence system and experienced anesthesiologists. No additional intervention will be performed on patients.",[35],"Procedure: Ultrasound-Guided Axillary Brachial Plexus Block (Routine Clinical Practice)",[37],{"type":38,"name":39,"description":40,"armGroupLabels":41,"otherNames":24},"PROCEDURE","Ultrasound-Guided Axillary Brachial Plexus Block (Routine Clinical Practice)","Ultrasound-guided axillary brachial plexus block performed as part of routine clinical care. No additional intervention is introduced for the purposes of the study. Real-time ultrasound images obtained during the procedure will be analyzed by an artificial intelligence system and experienced anesthesiologists.",[32],[43],{"name":44,"role":45,"phone":46,"phoneExt":24,"email":47},"Muhammed Gökhan Abay","CONTACT","+905379479745","abay.25@gmail.com",{"type":49,"investigatorFullName":44,"investigatorTitle":50,"investigatorAffiliation":5,"oldNameTitle":24,"oldOrganization":24},"PRINCIPAL_INVESTIGATOR","Specialist Physician, Department of Anesthesiology and Reanimation","100633868",false,"NCT07532278","Agreement Between Artificial Intelligence and Anesthesiologists in Ultrasound-Guided Axillary Brachial Plexus Block","Evaluation of Agreement Between Artificial Intelligence and Experienced Anesthesiologists in Target Point Identification for Ultrasound-Guided Axillary Brachial Plexus Block: A Prospective Observational Study","Inclusion Criteria:\n\n* Age between 18 and 80 years\n* American Society of Anesthesiologists (ASA) physical status I-III\n* Patients scheduled for upper extremity surgery under ultrasound-guided axillary brachial plexus block as part of routine clinical practice\n* Ability to obtain adequate real-time ultrasound imaging of the axillary region prior to block performance\n* Provision of written informed consent\n\nExclusion Criteria:\n\n* Inability to clearly visualize the axillary artery and at least one peripheral nerve (median, ulnar, radial, or musculocutaneous) on ultrasound imaging\n* Presence of significant ultrasound artifacts impairing image interpretation\n* History of previous surgery in the axillary region causing anatomical distortion\n* Anatomical deformities or significant anatomical variations in the axillary region\n* Inadequate ultrasound image quality due to severe obesity or other technical limitations\n* Failure to obtain real-time ultrasound imaging prior to block performance\n* Withdrawal of informed consent","ALL","18 Years","80 Years",{"count":61,"type":62},100,"ESTIMATED","OBSERVATIONAL","This prospective observational study aims to evaluate the agreement between artificial intelligence (AI)-assisted target point identification and experienced anesthesiologists during ultrasound-guided axillary brachial plexus block.\n\nUltrasound guidance is widely used in regional anesthesia to improve block success and safety. However, accurate identification of anatomical structures and optimal injection points remains operator-dependent. Artificial intelligence-based systems have the potential to assist clinicians by identifying anatomical landmarks in real time.\n\nIn this study, AI-generated target points will be compared with those determined by experienced anesthesiologists. The level of agreement between the two methods will be analyzed. Secondary outcomes will include block performance parameters and image quality.\n\nThe findings of this study may contribute to understanding the clinical utility of AI in ultrasound-guided regional anesthesia.",[66],"Ultrasound-Guided Nerve Block and Artificial Intelligence",[68,69,70,71,72,73],"Ultrasound-Guided Regional Anesthesia","Axillary Brachial Plexus Block","Artificial Intelligence","Ultrasound Imaging","Anatomical Structure Recognition","Agreement Analysis","NOT_YET_RECRUITING","2026-04-09",{"date":77,"type":78},"2026-04-15","ACTUAL",{"date":77,"type":62},{"date":81,"type":62},"2026-07-15",{"name":5,"class":6}]