Artificial Intelligence for Ultrasound-Guided Peripheral Nerve and Plane Blocks

NCT05718414 | Completed

• 1 other identifier: E.595466
• Study Type: observational
• Target: 40
• Locations: 1 country
• Sites: 1

Brief Summary

The goal of this observational study is to test the accuracy of an artificial intelligence tool used for identifying ultrasound-guided block regions in healthy volunteer participants. The main question aims to answer is:

• Is the artificial intelligence tool effective for identifying selected ultrasound-guided nerve block regions and their anatomical landmarks? Three anesthesiology trainees perform ultrasound scanning for 8 nerve block regions on participants. Peripheral nerve and plane block regions are;
• Adductor canal block region
• Axillary brachial plexus block region
• ESP (erector spinae plane) block region
• Femoral block region
• PECS (pectoral) block region
• Popliteal block region
• Rectus sheath block region
• Superficial cervical plexus block region

Conditions

Complication Following Peripheral Nerve Block (Diagnosis)

Keywords

Ultrasound-Guided Nerve or Plane Block; Artificial Intelligence

Outcome Measures

Primary Outcomes (1)

• Validation of real-time identification of anatomical landmarks associated with selected peripheral nerve and plane blocks via AI supported ultrasound practice

  In 40 healthy volunteer participants, AI supported ultrasound was used to scan each peripheral nerve and plane block to highlight the block-specific anatomical landmarks (by the three anesthesiology trainees). Then, expert practitioners score/rate the accuracy of color overlays using a 6-point scale (between 0 to 5) for a total of 4,440 anatomical landmarks by assessing raw and highlighted ultrasound images.

  After collecting and saving all scans/images performed by the anesthesiology trainees in one day, rating/scoring of all these saved raw and highlighted ultrasound scans/images by the experts in one day, single point

Secondary Outcomes (1)

• Difference according to BMI and gender

  After saving all ultrasound scans/images in one day, rating/scoring in one day, single point,

Interventions

Ultrasound via artifical intelligence DEVICE

The peripheral nerve and plan block regions (adductor canal, axillary brachial plexus, PECS, popliteal, rectus sheath, ESP, femoral, and superficial cervical plexus regions) and related anatomical landmarks are practised by 3 anesthesiology residents who were in the training program of regional anesthesia and qualified to perform ultrasound guided techniques . Then, scans of 8 block types for all 40 volunteers; when the "scan success" gauge on the AI software was 100% at the time the images were saved. Using this procedure, 960 ultrasound images were acquired in both raw and AI-processed forms for expert assessment .

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: Yes
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Probability Sample

Study Population

Forty healthy volunteers (20 females, 20 males) over the age of 18 were recruited. After informing the participants about the study ultrasound procedure and scan regions, written informed consent was obtained from each volunteer. Demographic properties including age, gender, and body mass index (BMI)-were recorded. Participants were regarded to be normal weight, overweight, and obese according to BMI.

You may qualify if:

• Volunteers over the age 18

You may not qualify if:

• anatomical deformity in the selected regions
• psychiatric or neurological diseases that would impair understanding of the consent form
• inability to lie flat

Sponsors & Collaborators

• Gazi University: lead
• Smart Alfa Teknoloji San. ve Tic. A.S.: collaborator

Study Sites (1)

Gazi University

Ankara, Turkey (Türkiye)

Location

Related Publications (2)

• Gungor I, Gunaydin B, Oktar SO, M Buyukgebiz B, Bagcaz S, Ozdemir MG, Inan G. A real-time anatomy identification via tool based on artificial intelligence for ultrasound-guided peripheral nerve block procedures: an accuracy study. J Anesth. 2021 Aug;35(4):591-594. doi: 10.1007/s00540-021-02947-3. Epub 2021 May 19.

  PMID: 34008072 BACKGROUND

• Bowness J, Varsou O, Turbitt L, Burkett-St Laurent D. Identifying anatomical structures on ultrasound: assistive artificial intelligence in ultrasound-guided regional anesthesia. Clin Anat. 2021 Jul;34(5):802-809. doi: 10.1002/ca.23742. Epub 2021 May 11.

  PMID: 33904628 BACKGROUND

MeSH Terms

Conditions

Disease

Condition Hierarchy (Ancestors)

Pathologic Processes; Pathological Conditions, Signs and Symptoms

Study Officials

• Irfan Gungor, Asoc.prof.

  Gazi University

  PRINCIPAL INVESTIGATOR

Study Design

• Study Type: observational
• Observational Model: CASE ONLY
• Time Perspective: PROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Prof (MD,PhD)

Study Record Dates

• First Submitted: January 26, 2023
• First Posted: February 8, 2023
• Study Start: February 8, 2023
• Primary Completion: February 15, 2023
• Study Completion: February 16, 2023
• Last Updated: February 16, 2023

Record last verified: 2023-02

Locations

TU (1)