Performance Comparison of Large Language Models in TAP Block Ultrasound Interpretation: A Double-Blind Prospective Study
试验速览
- 阶段
- 不适用
- 状态
- 招募中
- 入组人数
- 40
- 试验地点
- 1
- 主要终点
- Anatomical Interpretation Accuracy
研究概览
简要总结
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.
详细描述
This study aims to evaluate how two advanced artificial intelligence (AI) models, Gemini 2.5 Pro and ChatGPT-5.1, interpret ultrasound videos of Transversus Abdominis Plane (TAP) block procedures. TAP blocks are performed as part of routine clinical care by experienced anesthesiologists. The ultrasound videos recorded during these procedures serve as the data source for this study. No additional procedures or patient involvement are required beyond standard care.
Ultrasound Video Processing All ultrasound recordings will be fully de-identified by removing patient names, dates, and any other identifying information.
Gemini 2.5 Pro will receive original video files. ChatGPT-5.1 will receive high-resolution GIF segments generated from the same recordings.
Both models will be given identical structured prompts consisting of eight clinically relevant questions about anatomic structures, needle placement, local anesthetic spread, dermatomal effects, and potential safety concerns.
Expert Participation
研究设计
- 研究类型
- Observational
- 观察模型
- Other
- 时间视角
- Prospective
入排标准
- 年龄范围
- 18 Years 至 85 Years（Adult, Older Adult）
- 性别
- All
- 接受健康志愿者
- 否
入选标准
- •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
- •Able to provide written informed consent
排除标准
- •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
结局指标
主要结局
Anatomical Interpretation Accuracy
时间窗: At the time of video analysis
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.
次要结局
- Block Success Interpretation(At the time of video analysis.)
- Needle Plane Evaluation(At the time of video analysis.)
- Dermatomal Level Prediction(At the time of video analysis.)
- Risk Awareness Assessment(At the time of video analysis.)
- Recommendation Quality(At the time of video analysis.)
- Agreement Between Experts(During expert evaluation phase.)
- AI Response Time(Captured automatically during model output.)
研究者
Engin Ihsan Turan
principal investigator
Kanuni Sultan Suleyman Training and Research Hospital