The goal of this study is to learn how accurately two artificial intelligence (AI) models, Gemini 2.5 Pro and ChatGPT-5.1, can interpret ultrasound videos of the Transversus Abdominis Plane (TAP) block, a regional anesthesia technique used for pain control after surgery.
The main questions this study aims to answer are:
How accurately can each AI model identify anatomical structures on TAP block ultrasound videos? Can the AI models correctly evaluate the spread of local anesthetic and determine whether the block is successful? How closely do the AI models' answers match the evaluations of expert anesthesiologists? No additional procedures will be performed on patients. TAP blocks will be done as part of routine clinical care, and the ultrasound videos will be recorded and de-identified.
Participants will not need to do anything extra for the study. Experienced anesthesiologists will review the videos and provide expert answers. The AI models will be given the same videos and asked the same questions. A second expert, who does not know which answers came from humans or AI, will compare all responses.
The results will help researchers understand whether advanced AI systems can safely support clinicians in interpreting ultrasound-guided regional anesthesia procedures and improve education and decision-making in anesthesia practice.
Adults aged 18-85 years
ASA I-III physical status
Undergoing elective surgery with a lateral TAP block performed as part of routine anesthesia care
Complete ultrasound-guided block procedure recorded on video
Unsuccessful or incomplete TAP block procedure
Poor-quality ultrasound video (needle tip or anesthetic spread not visible)
Missing demographic or clinical data
Withdrawal of consent at any time
This study will include adult surgical patients who receive a lateral Transversus Abdominis Plane (L-TAP) block as part of routine anesthesia practice during elective operations. The population consists only of patients whose block procedures are already clinically indicated and performed by experienced anesthesiologists. No additional procedures are carried out for research purposes. Ultrasound videos obtained during standard block practice are de-identified and analyzed. The study does not involve patient follow-up or any change in clinical management.
Other
Prospective
Gemini 2.5 Pro Evaluation
Other interventionAnalysis of de-identified ultrasound videos by the Gemini 2.5 Pro artificial intelligence model. The model receives standardized prompts and provides anatomical interpretation, block success assessment, and clinical suggestion outputs.
ChatGPT-5.1 Evaluation
Other interventionAnalysis of de-identified ultrasound videos by the ChatGPT-5.1 artificial intelligence model. The model receives the same standardized questions and produces anatomical and clinical interpretations for comparison.
Anatomical Interpretation Accuracy
For each ultrasound video, the ability of both AI models (ChatGPT-5.1 and Gemini 2.5 Pro) to correctly identify key anatomical structures of the lateral TAP block (internal oblique, transversus abdominis, fascial plane, needle tip) will be evaluated. The accuracy of each model will be compared with the expert-defined reference answer.
Block Success Interpretation
Assessment of whether each AI model correctly determines block success based on needle placement and local anesthetic spread, compared with expert reference evaluation.
Needle Plane Evaluation
Determination of whether AI models correctly assess the needle tip location and whether it is within the correct interfascial plane (IO-TA fascia), compared with the expert reference.
Dermatomal Level Prediction
Comparison of each AI model's predicted dermatomal coverage (e.g., T10-T12) with the expert-provided reference dermatomal level.
Risk Awareness Assessment
Evaluation of whether each AI model correctly identifies potential risks on ultrasound images (e.g., peritoneal proximity, vascular structures). 0 = no risk awareness, 1 = partial, 2 = complete and appropriate risk identification.
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