Ignite Creation Date:
2024-05-06 @ 8:11 PM
Last Modification Date:
2024-10-26 @ 3:22 PM
Study NCT ID:
NCT06283485
Status:
RECRUITING
Last Update Posted:
2024-02-28
First Post:
2024-02-15
Brief Title:
Real-time Anatomy Recognition Tool Accuracy Research for Ultrasound-guided PENG and Suprainguinal Fascia Iliaca Blocks
Sponsor:
Konya City Hospital
Organization:
Konya City Hospital
Study Overview
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
Status:
RECRUITING
Status Verified Date:
2024-10
Last Known Status:
None
Delayed Posting:
No
If Stopped, Why?:
Not Stopped
Has Expanded Access:
False
If Expanded Access, NCT#:
N/A
Has Expanded Access, NCT# Status:
N/A
Acronym:
None
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
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
Detailed Description:
Research processes The study will consist of two stages Phase 1 PENG collection of sonoanatomical images of Supraingiuinal Fascia Iliaca block and development of artificial intelligence software in healthy volunteers without invasive procedures Stage 2 Conducting validation study with the developed artificial intelligence
Stage 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
11 PENG Pericapsular Nerve Group Block Images will be taken with both linear and convex probes Sonoanatomical information will be collected from healthy volunteers and no invasive procedures will be performed 150 75 women -75 men healthy volunteers who agree to have ultrasound images taken will be included
12 Suprainguinal Fascia Iliaca Block Images will be taken with a linear probe Sonoanatomical information will be collected from healthy volunteers and no invasive procedures will be performed 150 75 female-75 male healthy volunteers who agree to have ultrasound images taken will be included
In the first phase of this study thanks to the PENG and Suprainguinal Fascia Iliaca block images collected from volunteers the artificial intelligence technology Smart Alfa Teknoloji San recognizes and marks the anatomical structures of this region and Tic Inc It will be developed by and added to Nerveblox software After PENG and Suprainguinal Fascia Iliaca blocks are included in the software Nerveblox software will be used during validation in the second phase of the study
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
Thanks to the Nerveblox artificial intelligence technology developed at this stage the accuracy of the anatomical structures marked and colored by the regional-specific artificial intelligence It will be evaluated by 6 experienced anesthesiologists based on ultrasound image scans made by 2nd 3rd and 4th year assistants two assistants from each year The second phase is the validation phase and the validators will be experienced anesthesiologists at least five years of specialized experience Validators will score the accuracy of representation of each predefined anatomical landmark using a 5-point scale 1 Very Poor 2 Poor 3 Good 4 Very Good 5 Excellent Accuracy is defined as expert opinion on how software-generated landmark labels represent true anatomy in raw ultrasonography images
Stage 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
11 PENG Pericapsular Nerve Group Block Images will be taken with both linear and convex probes Sonoanatomical information will be collected from healthy volunteers and no invasive procedures will be performed 150 75 women -75 men healthy volunteers who agree to have ultrasound images taken will be included
12 Suprainguinal Fascia Iliaca Block Images will be taken with a linear probe Sonoanatomical information will be collected from healthy volunteers and no invasive procedures will be performed 150 75 female-75 male healthy volunteers who agree to have ultrasound images taken will be included
In the first phase of this study thanks to the PENG and Suprainguinal Fascia Iliaca block images collected from volunteers the artificial intelligence technology Smart Alfa Teknoloji San recognizes and marks the anatomical structures of this region and Tic Inc It will be developed by and added to Nerveblox software After PENG and Suprainguinal Fascia Iliaca blocks are included in the software Nerveblox software will be used during validation in the second phase of the study
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
Thanks to the Nerveblox artificial intelligence technology developed at this stage the accuracy of the anatomical structures marked and colored by the regional-specific artificial intelligence It will be evaluated by 6 experienced anesthesiologists based on ultrasound image scans made by 2nd 3rd and 4th year assistants two assistants from each year The second phase is the validation phase and the validators will be experienced anesthesiologists at least five years of specialized experience Validators will score the accuracy of representation of each predefined anatomical landmark using a 5-point scale 1 Very Poor 2 Poor 3 Good 4 Very Good 5 Excellent Accuracy is defined as expert opinion on how software-generated landmark labels represent true anatomy in raw ultrasonography images
Study Oversight
Has Oversight DMC:
None
Is a FDA Regulated Drug?:
False
Is a FDA Regulated Device?:
False
Is an Unapproved Device?:
None
Is a PPSD?:
None
Is a US Export?:
None
Is an FDA AA801 Violation?:
None