Nerve Block Clinical Trial
Official title:
Accuracy Study of an AI-based, Real-time Anatomy Identification Tool for Use in Ultrasound-guided PENG (Pericapsular Nerve Block) 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.
Status | Recruiting |
Enrollment | 150 |
Est. completion date | May 25, 2024 |
Est. primary completion date | April 1, 2024 |
Accepts healthy volunteers | |
Gender | All |
Age group | 18 Years to 65 Years |
Eligibility | 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 |
Country | Name | City | State |
---|---|---|---|
Turkey | Yasin Tire | Konya | Meram |
Lead Sponsor | Collaborator |
---|---|
Konya City Hospital | Betül Afsar |
Turkey,
Type | Measure | Description | Time frame | Safety issue |
---|---|---|---|---|
Primary | Artificial intelligence Program size | Thanks to the PENG and Suprainguinal Fascia Iliaca block images collected from volunteers in the first phase of this study, the artificial intelligence technology Smart Alfa company recognizes and marks the anatomical structures of these four regions. It will be developed by and added to Nerveblox software. | Based, during the ultrasonography. | |
Primary | Score of assessment the pictures | In the second phase, thanks to the Nerveblox artificial intelligence technology developed, the accuracy of the anatomical structures marked and colored by the regional-specific artificial intelligence; It will be evaluated based on ultrasound image scans made by 2nd, 3rd and 4th year assistants and by anesthesiologists with at least 5 years of experience. | After the ultrasonography. |
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