Real-time Anatomy Recognition Tool Accuracy Research for Ultrasound-guided PENG and Suprainguinal Fascia Iliaca Blocks
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.
- EligibilityCriteria: Inclusion Criteria:  Agreeing to take ultrasound images Healthy - No comorbidities Adult individuals between the ages of 18-65  Exclusion Criteria:  Those who do not accept ultrasound images Individuals under 18 years of age with comorbidities Pregnancy - Gender: All - GenderBased: Yes - GenderDescription: 150 (75 women -75 men) healthy volunteers who agree to have ultrasound images taken will be included. - MinimumAge: 18 Years - MaximumAge: 65 Years - StdAgeList: Adult, Older Adult - StudyPopulation: For the first phase of the study, 150 volunteers, 75 women and 75 men, were determined by the Smart Alfa company, which will produce artificial intelligence.  In the sample size analysis conducted for the second stage, it was determined that there should be 19 participants for each group with an alpha margin of error of 0.05, with a power rate of 80% for comparison of two groups. The effect used for this calculation was calculated as 0.837, and the actual power was calculated as 0.812, based on similar studies. As a result of the analysis, considering the drop out rate, it was planned to recruit 20 participants for each group. (20 women - 20 men) - SamplingMethod: Probability Sample