protocolSection identificationModule nctId: NCT06283485, orgStudyIdInfo id: Study Artificial intelligence, briefTitle: Real-time Anatomy Recognition Tool Accuracy Research for Ultrasound-guided PENG and Suprainguinal Fascia Iliaca Blocks, statusModule overallStatus: RECRUITING, startDateStruct date: 2023-12-15, primaryCompletionDateStruct date: 2024-04-01, completionDateStruct date: 2024-05-25, studyFirstPostDateStruct date: 2024-02-28, lastUpdatePostDateStruct date: 2024-02-28, sponsorCollaboratorsModule leadSponsor name: Konya City Hospital, class: OTHER, collaborators name: Betül Afşar, descriptionModule briefSummary: Background and rationale: Ultrasound-guided regional anesthesia is a widely used pain control method today. A critical aspect of the procedure is accurate visualization of anatomical structures on ultrasound to precisely define target areas. Distinguishing surrounding tissues with an imaging model that automatically recognizes sonoanatomy in ultrasound images will reduce unintended intraneural injections or injury to other anatomical structures in close proximity and increase patient safety.Research question; How can we improve the ultrasound images we frequently use in regional blocks by integrating them with artificial intelligence to reduce complications and improve applications? And what is the accuracy of the developed artificial intelligence support during imaging?Research purpose; This work; We aim to further increase the safety of different regional block positions, minimize the risk of complications, and improve ultrasound visualization by developing an artificial intelligence model (AI Model-Artificial Intelligence) that automatically identifies and segments anatomical landmarks, provides visual guidance for inexperienced colleagues, and improves the performance of the developed model during application. aims to demonstrate its accuracy.Hypothesis; Numerous studies have shown that the use of ultrasound and neurostimulators in practice increases the success, onset and quality of nerve blocks, but due to the low incidence of major complications and the absence of comparable randomized studies, no definitive statement can be made as to whether ultrasound reduces the overall rate of nerve damage. An imaging model that automatically marks sonoanatomy with artificial intelligence in ultrasound images can reduce unintended intraneural injections or injury to other anatomical structures in close proximity and improve patient safety., conditionsModule conditions: Artificial Intelligent, conditions: Ultrasound Therapy; Complications, conditions: Nerve Block, designModule studyType: OBSERVATIONAL, designInfo observationalModel: CASE_CONTROL, timePerspective: CROSS_SECTIONAL, enrollmentInfo count: 150, type: ESTIMATED, armsInterventionsModule interventions name: ultrasound examination, outcomesModule primaryOutcomes measure: Artificial intelligence Program size, primaryOutcomes measure: Score of assessment the pictures, eligibilityModule sex: ALL, minimumAge: 18 Years, maximumAge: 65 Years, stdAges: ADULT, stdAges: OLDER_ADULT, contactsLocationsModule locations facility: Yasin Tire, status: RECRUITING, city: Konya, state: Meram, zip: 42140, country: Turkey, contacts name: Yasin Tire, role: CONTACT, phone: +905055367970, email: dryasintire@hotmail.com, contacts name: Betül Afşar, role: CONTACT, geoPoint lat: 37.87135, lon: 32.48464, hasResults: False