Ultrasound-Guided Nerve Block and Artificial IntelligenceNCT07532278
Agreement Between Artificial Intelligence and Anesthesiologists in Ultrasound-Guided Axillary Brachial Plexus Block
An observational study of Ultrasound-Guided Nerve Block and Artificial Intelligence, run by Gaziantep City Hospital.
Trial summary
This prospective observational study aims to evaluate the agreement between artificial intelligence (AI)-assisted target point identification and experienced anesthesiologists during ultrasound-guided axillary brachial plexus block. Read more about this study
- Who is it for?
- Ages 18 to 80 years
- Conditions the study is for
- Ultrasound-Guided Nerve Block and Artificial Intelligence
- What is being tested?
- Ultrasound-Guided Axillary Brachial Plexus Block (Routine Clinical Practice)
- Type of treatment
- Surgery or procedure
- How many people
- 100 participants
- Where
- No sites listed yet
- Study dates
- 15 Apr 2026to 15 Jul 2026
- Phase
- Observational
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 to 80 years
- All sexes
- No healthy volunteers
- Adult, Older adult
Study population: Adult patients aged 18-80 years with American Society of Anesthesiologists (ASA) physical status I-III undergoing upper extremity surgery under ultrasound-guided axillary brachial plexus block in routine clinical practice. Only patients with adequate real-time ultrasound imaging of the axillary region allowing identification of relevant anatomical structures will be included.
Who may be able to join
- Age between 18 and 80 years
- American Society of Anesthesiologists (ASA) physical status I-III
- Patients scheduled for upper extremity surgery under ultrasound-guided axillary brachial plexus block as part of routine clinical practice
- Ability to obtain adequate real-time ultrasound imaging of the axillary region prior to block performance
- Provision of written informed consent
Who cannot join
- Inability to clearly visualize the axillary artery and at least one peripheral nerve (median, ulnar, radial, or musculocutaneous) on ultrasound imaging
- Presence of significant ultrasound artifacts impairing image interpretation
- History of previous surgery in the axillary region causing anatomical distortion
- Anatomical deformities or significant anatomical variations in the axillary region
- Inadequate ultrasound image quality due to severe obesity or other technical limitations
- Failure to obtain real-time ultrasound imaging prior to block performance
- Withdrawal of informed consent
Trial locations and contacts
No sites are listed yet. Contact the study team or a site near you to ask about taking part.
About this study
Official title: Evaluation of Agreement Between Artificial Intelligence and Experienced Anesthesiologists in Target Point Identification for Ultrasound-Guided Axillary Brachial Plexus Block: A Prospective Observational Study
This prospective observational study aims to evaluate the agreement between artificial intelligence (AI)-assisted target point identification and experienced anesthesiologists during ultrasound-guided axillary brachial plexus block.
Ultrasound guidance is widely used in regional anesthesia to improve block success and safety. However, accurate identification of anatomical structures and optimal injection points remains operator-dependent. Artificial intelligence-based systems have the potential to assist clinicians by identifying anatomical landmarks in real time.
Read more
In this study, AI-generated target points will be compared with those determined by experienced anesthesiologists. The level of agreement between the two methods will be analyzed. Secondary outcomes will include block performance parameters and image quality.
The findings of this study may contribute to understanding the clinical utility of AI in ultrasound-guided regional anesthesia.
Read the detailed description
Ultrasound-guided axillary brachial plexus block is a widely used regional anesthesia technique for upper extremity surgeries. The success of the procedure largely depends on accurate identification of neural structures and optimal injection points, which are operator-dependent.
Artificial intelligence (AI) has recently emerged as a promising tool for assisting ultrasound interpretation by automatically identifying anatomical structures. However, the level of agreement between AI-based target point identification and expert anesthesiologists has not been sufficiently investigated, particularly in axillary brachial plexus block.
In this prospective observational study, patients undergoing upper extremity surgery under axillary brachial plexus block will be included. No additional intervention will be performed on patients within the scope of the study. All evaluations will be based on real-time ultrasound imaging obtained as part of routine clinical practice.
During routine ultrasound examination prior to block performance, images will be observed in real time. Experienced anesthesiologists will determine anatomical structures and optimal target injection points during the procedure. Simultaneously, the AI-based system will analyze the same real-time ultrasound images and identify target points.
For each identified nerve (median, ulnar, radial, and musculocutaneous), both AI and anesthesiologists will determine target injection points. The spatial difference between AI-generated and expert-defined target points will be calculated in millimeters.
The primary objective is to evaluate the agreement between AI and anesthesiologists in target point identification using the intraclass correlation coefficient (ICC). Additionally, a difference of ≤5 mm between measurements will be considered clinically acceptable agreement.
Secondary outcomes will include:
Proportion of measurements within ≤5 mm agreement Agreement in nerve identification Procedure-related parameters
All expert evaluations will be performed independently and blinded to AI outputs.
This study aims to determine whether AI can reliably assist clinicians in identifying anatomical targets during ultrasound-guided regional anesthesia without introducing any additional risk to patients.
- Being tested
- Ultrasound-Guided Axillary Brachial Plexus Block (Routine Clinical Practice)
- Keywords
- Ultrasound-Guided Regional Anesthesia, Axillary Brachial Plexus Block, Artificial Intelligence, Ultrasound Imaging, Anatomical Structure Recognition, Agreement Analysis
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Ultrasound-Guided Axillary Brachial Plexus Block (Routine Clinical Practice)Procedure or surgery
Ultrasound-guided axillary brachial plexus block performed as part of routine clinical care. No additional intervention is introduced for the purposes of the study. Real-time ultrasound images obtained during the procedure will be analyzed by an artificial intelligence system and experienced anesthesiologists.
| Study group | Role | What they receive |
|---|---|---|
| Patients Undergoing Ultrasound-Guided Axillary Brachial Plexus Block | Patients undergoing upper extremity surgery in whom ultrasound-guided axillary brachial plexus block is performed as part of routine clinical practice. Real-time ultrasound images will be evaluated simultaneously by an artificial intelligence system and experienced anesthesiologists. No additional intervention will be performed on patients. Receives: Procedure: Ultrasound-Guided Axillary Brachial Plexus Block (Routine Clinical Practice) |
What is being measured
The primary outcome is the main question the study is designed to answer.
Agreement in Target Point Identification Between Artificial Intelligence and Anesthesiologists
Agreement between artificial intelligence (AI) and experienced anesthesiologists in identifying target injection points will be evaluated using the intraclass correlation coefficient (ICC). Target points will be defined using coordinate-based measurements on real-time ultrasound images.
Measured: During block procedure (ultrasound imaging)
Secondary outcomes (3)
Proportion of Measurements Within Clinically Acceptable Agreement
The proportion of target point measurements with a difference of ≤5 mm between AI and anesthesiologists will be calculated. A difference of ≤5 mm will be considered clinically acceptable agreement.
Measured: During block procedure
Agreement in Nerve Identification
Agreement between AI and anesthesiologists in identifying peripheral nerves (median, ulnar, radial, and musculocutaneous) will be evaluated using Cohen's kappa coefficient.
Measured: During block procedure
Spatial Difference Between Target Points
The absolute distance (in millimeters) between AI-generated and anesthesiologist-defined target points will be calculated for each measurement.
Measured: During block procedure
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted15 Apr 2026
- Study start15 Apr 2026 (estimated)
- Primary completion15 Jul 2026 (estimated)
- Study completion15 Jul 2026 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Ultrasound-Guided Nerve Block and Artificial IntelligenceStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Case only
- Time perspective
- Prospective
- Enrolment
- 100 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Gaziantep City Hospital
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Muhammed Gökhan Abay, Gaziantep City Hospital
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07532278
- Sponsor's protocol ID
- 121
- Status last verified
- Apr 2026
- First submitted
- 9 Apr 2026
- Last updated
- 15 Apr 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No: Individual participant data will not be shared due to institutional policies and lack of infrastructure for data sharing.
Study at a glance
From the registry record, last updated by the sponsor on 15 Apr 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Evaluation of Agreement Between Artificial Intelligence and Experienced Anesthesiologists in Target Point Identification for Ultrasound-Guided Axillary Brachial Plexus Block: A Prospective Observational Study
- ClinicalTrials.gov ID
- NCT07532278
- Acronym / sponsor ID
- 121
- Keywords
- Ultrasound-Guided Regional Anesthesia, Axillary Brachial Plexus Block, Artificial Intelligence, Ultrasound Imaging, Anatomical Structure Recognition, Agreement Analysis
- Study type
- Observational
- Recruitment status
- Not yet recruiting
- Enrolment
- 100 participants (estimated)
- Lead sponsor
- Gaziantep City HospitalUniversity, hospital or other
- Study start
- 15 Apr 2026 (estimated)
- Primary completion
- 15 Jul 2026 (estimated)
- Study completion
- 15 Jul 2026 (estimated)
- First posted
- 15 Apr 2026 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
This prospective observational study aims to evaluate the agreement between artificial intelligence (AI)-assisted target point identification and experienced anesthesiologists during ultrasound-guided axillary brachial plexus block.
Ultrasound guidance is widely used in regional anesthesia to improve block success and safety. However, accurate identification of anatomical structures and optimal injection points remains operator-dependent. Artificial intelligence-based systems have the potential to assist clinicians by identifying anatomical landmarks in real time.
In this study, AI-generated target points will be compared with those determined by experienced anesthesiologists. The level of agreement between the two methods will be analyzed. Secondary outcomes will include block performance parameters and image quality.
The findings of this study may contribute to understanding the clinical utility of AI in ultrasound-guided regional anesthesia.
Detailed description
Ultrasound-guided axillary brachial plexus block is a widely used regional anesthesia technique for upper extremity surgeries. The success of the procedure largely depends on accurate identification of neural structures and optimal injection points, which are operator-dependent.
Artificial intelligence (AI) has recently emerged as a promising tool for assisting ultrasound interpretation by automatically identifying anatomical structures. However, the level of agreement between AI-based target point identification and expert anesthesiologists has not been sufficiently investigated, particularly in axillary brachial plexus block.
In this prospective observational study, patients undergoing upper extremity surgery under axillary brachial plexus block will be included. No additional intervention will be performed on patients within the scope of the study. All evaluations will be based on real-time ultrasound imaging obtained as part of routine clinical practice.
During routine ultrasound examination prior to block performance, images will be observed in real time. Experienced anesthesiologists will determine anatomical structures and optimal target injection points during the procedure. Simultaneously, the AI-based system will analyze the same real-time ultrasound images and identify target points.
For each identified nerve (median, ulnar, radial, and musculocutaneous), both AI and anesthesiologists will determine target injection points. The spatial difference between AI-generated and expert-defined target points will be calculated in millimeters.
The primary objective is to evaluate the agreement between AI and anesthesiologists in target point identification using the intraclass correlation coefficient (ICC). Additionally, a difference of ≤5 mm between measurements will be considered clinically acceptable agreement.
Secondary outcomes will include:
Proportion of measurements within ≤5 mm agreement Agreement in nerve identification Procedure-related parameters
All expert evaluations will be performed independently and blinded to AI outputs.
This study aims to determine whether AI can reliably assist clinicians in identifying anatomical targets during ultrasound-guided regional anesthesia without introducing any additional risk to patients.
Study design
- Observational model
- Case only
- Time perspective
- Prospective
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| Patients Undergoing Ultrasound-Guided Axillary Brachial Plexus Block | – | Procedure: Ultrasound-Guided Axillary Brachial Plexus Block (Routine Clinical Practice)Patients undergoing upper extremity surgery in whom ultrasound-guided axillary brachial plexus block is performed as part of routine clinical practice. Real-time ultrasound images will be evaluated simultaneously by an artificial intelligence system and experienced anesthesiologists. No additional intervention will be performed on patients. |
Who may qualify
- Ages
- 18 to 80 years
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- Adult patients aged 18-80 years with American Society of Anesthesiologists (ASA) physical status I-III undergoing upper extremity surgery under ultrasound-guided axillary brachial plexus block in routine clinical practice. Only patients with adequate real-time ultrasound imaging of the axillary region allowing identification of relevant anatomical structures will be included.
Key inclusion criteria (5 listed)
- Age between 18 and 80 years
- American Society of Anesthesiologists (ASA) physical status I-III
- Patients scheduled for upper extremity surgery under ultrasound-guided axillary brachial plexus block as part of routine clinical practice
- Ability to obtain adequate real-time ultrasound imaging of the axillary region prior to block performance
- Provision of written informed consent
Key exclusion criteria (7 listed)
- Inability to clearly visualize the axillary artery and at least one peripheral nerve (median, ulnar, radial, or musculocutaneous) on ultrasound imaging
- Presence of significant ultrasound artifacts impairing image interpretation
- History of previous surgery in the axillary region causing anatomical distortion
- Anatomical deformities or significant anatomical variations in the axillary region
- Inadequate ultrasound image quality due to severe obesity or other technical limitations
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Ultrasound-Guided Axillary Brachial Plexus Block (Routine Clinical Practice)Ultrasound-guided axillary brachial plexus block performed as part of routine clinical care. No additional intervention is introduced for the purposes of the study. Real-time ultrasound images obtained during the procedure will be analyzed by an artificial intelligence system and experienced anesthesiologists. | Procedure or surgery | Patients Undergoing Ultrasound-Guided Axillary Brachial Plexus Block |
Primary outcome
Agreement in Target Point Identification Between Artificial Intelligence and Anesthesiologists
Time frame: During block procedure (ultrasound imaging)
Agreement between artificial intelligence (AI) and experienced anesthesiologists in identifying target injection points will be evaluated using the intraclass correlation coefficient (ICC). Target points will be defined using coordinate-based measurements on real-time ultrasound images.
3 secondary outcomes
Proportion of Measurements Within Clinically Acceptable Agreement
Time frame: During block procedure
Agreement in Nerve Identification
Time frame: During block procedure
Spatial Difference Between Target Points
Time frame: During block procedure
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 15 Apr 2026.
- Individual participant dataSharing planned: No. Individual participant data will not be shared due to institutional policies and lack of infrastructure for data sharing.
Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
Questions about this trial
What is this clinical trial studying?
An observational study of Ultrasound-Guided Nerve Block and Artificial Intelligence, run by Gaziantep City Hospital.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 5 inclusion and 7 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: opening soon, as of the sponsor's last update on 15 Apr 2026. Status can change between updates, so confirm with the study contact before making plans.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact Muhammed Gökhan Abay (+905379479745, abay.25@gmail.com), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Agreement in Target Point Identification Between Artificial Intelligence and Anesthesiologists (time frame: During block procedure (ultrasound imaging)). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 15 Apr 2026.