Evaluation of ChatGPT-4's Success Sonoanatomy

Background and Rationale:

With the increasing use of ultrasound in regional anesthesia, techniques such as peripheral nerve blocks and fascial plane blocks have become more reliable and safer. Ultrasound guidance has significantly improved the success rate of regional anesthesia, reducing complications by enabling accurate visualization of relevant anatomical structures. However, successful ultrasound-guided blocks require thorough knowledge of sonoanatomy. Without this expertise, there is a risk of improper anesthetic placement, potentially leading to block failure or unintended complications.

Experienced anesthesiologists proficient in sonoanatomy can easily interpret ultrasound images, but those with limited experience often face difficulties. This highlights the need for educational tools that can aid in teaching and improving the identification of anatomical landmarks. The development of AI-based systems, such as ChatGPT-4, for this purpose could revolutionize the training of regional anesthesia techniques by providing real-time feedback on ultrasound images.

Primary Aim:

The primary objective of this study is to evaluate the accuracy and effectiveness of ChatGPT-4 in identifying sonoanatomical landmarks from ultrasound images during regional anesthesia procedures.

Secondary Aim:

A secondary goal is to assess whether the AI model can evaluate the accuracy of block applications by analyzing the ultrasound images and determining the success of the block based on sonoanatomical features and block placement.

Expected Benefits:

The study aims to explore whether AI-based systems can be integrated into educational settings to aid anesthesiologists in mastering sonoanatomy for regional anesthesia. By facilitating the accurate identification of anatomical structures, the AI could potentially enhance the learning curve and improve block success rates. The findings of this study may lead to the development of advanced tools for training and performing ultrasound-guided regional anesthesia, benefiting both novice and experienced anesthesiologists.

Potential Risks:

There are no anticipated risks for participants in this study, as no personal data will be collected, and the study involves only the analysis of ultrasound images.

Study Design:

This is a prospective, observational study that will be conducted at Health Sciences University Istanbul Kanuni Sultan Süleyman Education and Research Hospital. The study will include patients aged 18 years and older who are undergoing surgery and receiving regional anesthesia under ultrasound guidance for analgesia or anesthesia purposes. Consent will be obtained from all patients before participating in the study.

Data Collection:

Only ultrasound images from the procedures will be captured, without collecting any personal or identifiable patient data. Each regional anesthesia block will be documented step-by-step through ultrasound images. These images will include key steps such as probe position, orientation, and the anatomical structures visualized during the procedure. The positioning and orientation of the ultrasound probe during the block will also be recorded.

AI Model Configuration:

A customized GPT-4 model will be developed and trained to analyze the ultrasound images. Based on the probe's position, region of placement, and the anatomical plane, the AI will attempt to identify the sonoanatomical structures present in the ultrasound images. The model will also make predictions regarding the type of regional block being performed.

In addition to identifying anatomical landmarks, the AI model will assess the success of the block by analyzing the final images from each procedure. It will provide a prediction of whether the block was successfully applied based on the anatomical structures and positioning of the needle and anesthetic.

Evaluation of AI Predictions:

The accuracy of the AI's predictions regarding anatomical landmarks and block success will be evaluated by an experienced anesthesiologist with expertise in regional anesthesia. This expert will compare the AI's predictions with their own interpretations of the ultrasound images to assess the AI's performance.

Study Outcome:

The primary outcome will be the accuracy of ChatGPT-4 in identifying sonoanatomical structures in ultrasound images. The secondary outcome will be the accuracy of the AI model in evaluating the success of block applications. These results will be compared to the evaluations of the experienced anesthesiologist to determine the AI model's efficacy.

Conclusion:

This study seeks to explore the potential of artificial intelligence, specifically ChatGPT-4, in aiding the identification of anatomical landmarks during ultrasound-guided regional anesthesia. By evaluating the AI's accuracy, the study aims to contribute to the development of innovative training tools that could enhance the education and practice of regional anesthesia techniques.