Real-time Anatomy Recognition Tool Accuracy Research for Ultrasound-guided PENG and Suprainguinal Fascia Iliaca Blocks

NCT06283485 | Withdrawn

• 1 other identifier: Study Artificial intelligence
• Study Type: observational
• Target: N/A
• Locations: 1 country
• Sites: 1

Brief Summary

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.

Conditions

Artificial Intelligent; Ultrasound Therapy; Complications; Nerve Block

Keywords

Artificial intelligent; peripheric nerve block; pericapsular nerve block; Suprainguinal Fascia Iliaca

Outcome Measures

Primary Outcomes (2)

• 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.

• 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.

Study Arms (2)

Male

Phase 1: Creating artificial intelligence from ultrasound scans of 150 healthy volunteers Sonoanatomical PENG and Suprainguinal Fascia Iliaca Block pictures are taken as follows. 1\) PENG (Pericapsular Nerve Group Block): Linear and convex probes will be used to collect images. No invasive procedures will be conducted on healthy subjects for sonoanatomical data. 150 healthy volunteers (75 women-75 males) will be ultrasounded. 1.2) Suprainguinal Fascia Iliaca Block: Linear probe images. No invasive procedures will be conducted on healthy subjects for sonoanatomical data. 150 healthy individuals (75 female-75 male) will be ultrasounded. Phase 2: Smart Alfa Teknoloji San. and Tic. Inc. will use Nerveblox, an artificial intelligence system built using data from the first stage, in the second part of the study. First-stage artificial intelligence technology validation and accuracy study. The accuracy study will involve 40 healthy volunteers. 20 men and 20 women will be studied.

Device: ultrasound examination

Female

Phase 1: Creating artificial intelligence from ultrasound scans of 150 healthy volunteers Sonoanatomical PENG and Suprainguinal Fascia Iliaca Block pictures are taken as follows. 1\) PENG (Pericapsular Nerve Group Block): Linear and convex probes will be used to collect images. No invasive procedures will be conducted on healthy subjects for sonoanatomical data. 150 healthy volunteers (75 women-75 males) will be ultrasounded. 1.2) Suprainguinal Fascia Iliaca Block: Linear probe images. No invasive procedures will be conducted on healthy subjects for sonoanatomical data. 150 healthy individuals (75 female-75 male) will be ultrasounded.

Device: ultrasound examination

Interventions

ultrasound examination DEVICE

Phase 1 1: Taking ultrasound images from healthy volunteers (150 volunteers) to produce artificial intelligence - How to take PENG and Suprainguinal Fascia Iliaca Block sonoanatomical images is as follows. Phase 2: In the second phase of the study, Smart Alfa Teknoloji San. and Tic. Inc. Artificial intelligence technology called Nerveblox, which was developed with the data received in the first stage with the support of the company, will be used. It is the validation and accuracy study of the artificial intelligence technology developed in the first stage. The accuracy study will be conducted on 40 healthy volunteers. 20 men and 20 women will be included in the study.

Female; Male

Eligibility Criteria

• Age: 18 Years - 65 Years
• Gender Eligibility Details: 150 (75 women -75 men) healthy volunteers who agree to have ultrasound images taken will be included.
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Probability Sample

Study Population

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)

You may qualify if:

• Agreeing to take ultrasound images
• Healthy - No comorbidities
• Adult individuals between the ages of 18-65

You may not qualify if:

• Those who do not accept ultrasound images
• Individuals under 18 years of age
• with comorbidities
• Pregnancy

Sponsors & Collaborators

• Konya City Hospital: lead
• Betül Afşar: collaborator

Study Sites (1)

Yasin Tire

Konya, Meram, 42140, Turkey (Türkiye)

Location

Study Officials

• Yasin Tire

  Konya City Hospital

  PRINCIPAL INVESTIGATOR

• Betül Afşar

  Konya City Hospital

  STUDY DIRECTOR

Study Design

• Study Type: observational
• Observational Model: CASE CONTROL
• Time Perspective: CROSS SECTIONAL
• Target Duration: 2 Months
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Assoc. Prof. Dr. Yasin Tire

Study Record Dates

• First Submitted: February 15, 2024
• First Posted: February 28, 2024
• Study Start: December 15, 2023
• Primary Completion: April 15, 2025
• Study Completion: April 25, 2025
• Last Updated: August 13, 2025

Record last verified: 2025-08

Locations

TU (1)