Evaluation of ChatGPT-4's Success Sonoanatomy

NCT06642389 | Completed

• 1 other identifier: SONOANATOMY-AI
• Study Type: observational
• Target: 147
• Locations: 1 country
• Sites: 1

Brief Summary

Aim and Importance: Regional anesthesia techniques have advanced significantly with the advent of ultrasound guidance. Peripheral nerve blocks and fascial plane blocks can now be performed safely and effectively under ultrasound visualization. Research has shown that ultrasound use significantly improves block success rates. However, accurate application requires in-depth knowledge of sonoanatomy, as failure to identify critical structures may result in incorrect anesthetic placement or failed blocks. While experienced anesthesiologists can easily identify these anatomical landmarks, those less familiar with sonoanatomy may find it challenging. This study aims to evaluate the effectiveness of ChatGPT-4 in identifying sonoanatomical structures in ultrasound images. A secondary objective is to assess whether artificial intelligence can evaluate the accuracy of regional anesthesia applications. Expected Benefits and Risks: The primary benefit is to explore the potential of AI-based systems in improving the learning and application of sonoanatomy, which may help anesthesiologists perform more accurate and successful blocks. We believe that the findings could contribute to regional anesthesia training. The study poses no risks to participants. Study Type, Scope, and Design: This prospective, observational study will be conducted at Health Sciences University Istanbul Kanuni Sultan Süleyman Education and Research Hospital. Ultrasound images from patients aged 18 and older undergoing regional anesthesia under ultrasound guidance will be photographed, without collecting personal data. Detailed images of the ultrasound-guided block steps will be captured. The position and orientation of the ultrasound probe will be documented for the AI model. A customized GPT-4 model will be developed to evaluate the sonoanatomical structures in the provided ultrasound images based on the probe's position and orientation. Additionally, the AI model will predict which block is being performed and assess the success of the block by analyzing the images. An experienced anesthesiologist will evaluate the accuracy of the AI's predictions.

Conditions

Regional Anesthesia

Keywords

artificial intelligence; sonoanatomy

Outcome Measures

Primary Outcomes (1)

• accuracy of ChatGPT-4

  Accuracy will be defined as the AI model's ability to correctly identify key anatomical landmarks (e.g., nerves, muscles, blood vessels, and fascial planes) that are crucial for successful block performance, compared to the gold standard interpretations provided by an experienced anesthesiologist.

  immediately after procedure

Secondary Outcomes (2)

• ChatGPT-4's accuracy in predicting the specific block being performed

  immediately after procedure

• ChatGPT-4's accuracy in assessing the success of the block

  immediately after procedure

Interventions

Sonoanatomical Structure Identification OTHER

ChatGPT-4 will analyze the ultrasound images to identify key anatomical landmarks such as nerves, muscles, blood vessels, and fascial planes that are critical for successful regional anesthesia. These structures will be labeled and compared to the expert anesthesiologist's assessment to determine accuracy.

Block Type Prediction OTHER

Based on the ultrasound images and the position of the probe, ChatGPT-4 will predict the type of regional anesthesia block being performed (e.g., supraclavicular block, femoral nerve block). These predictions will be compared to the actual block performed to evaluate the AI's accuracy.

Block Success Assessment OTHER

After analyzing the ultrasound images from the block application, ChatGPT-4 will assess whether the block was successfully administered. This assessment will be based on the correct placement of the needle, the spread of the anesthetic, and proximity to target structures. The AI's evaluation of block success will be compared to the expert anesthesiologist's judgment.

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Probability Sample

Study Population

The study will include adult patients aged 18 years or older who are undergoing surgery at Health Sciences University Istanbul Kanuni Sultan Süleyman Education and Research Hospital. These patients will receive regional anesthesia under ultrasound guidance, and those who sign an informed consent form will be included. The study will exclude patients under 18 years old, those without a surgical history, patients who did not receive regional anesthesia under ultrasound guidance, and those who do not sign the informed consent form. This population provides a representative sample of adult surgical patients undergoing regional anesthesia, making it suitable for evaluating the accuracy of AI in identifying sonoanatomical landmarks in ultrasound images.

You may qualify if:

• Patients aged 18 years or older.
• Patients undergoing surgery.
• Patients receiving any regional anesthesia technique under ultrasound guidance.
• Patients who have signed an informed consent form.

You may not qualify if:

• Patients under the age of 18.
• Patients without a history of surgery.
• Patients who have not received any regional anesthesia technique under ultrasound guidance.
• Patients who have not signed the required informed consent documents will not be included in the study.

Sponsors & Collaborators

• Kanuni Sultan Suleyman Training and Research Hospital: lead

Study Sites (1)

Health Science University İstanbul Kanuni Sultan Süleyman Education and Training Hospital

Istanbul, 34303, Turkey (Türkiye)

Location

Study Officials

• Engin ihsan Turan, Specialist

  Health Science University İstanbul Kanuni Sultan Süleyman Education and Training Hospital

  PRINCIPAL INVESTIGATOR

Study Design

• Study Type: observational
• Observational Model: CASE ONLY
• Time Perspective: PROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: anesthesiology and reanimation specialist

Study Record Dates

• First Submitted: October 12, 2024
• First Posted: October 15, 2024
• Study Start: October 17, 2024
• Primary Completion: February 15, 2025
• Study Completion: February 16, 2025
• Last Updated: February 20, 2025

Record last verified: 2025-02

Locations

TU (1)