Performance Comparison of Large Language Models in TAP Block Ultrasound Interpretation

NCT ID: NCT07261436

Last Updated: 2026-02-04

Study Results

Results pending

The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.

Basic Information

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Recruitment Status

RECRUITING

Total Enrollment

40 participants

Study Classification

OBSERVATIONAL

Study Start Date

2026-01-15

Study Completion Date

2026-05-01

Brief Summary

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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.

Detailed Description

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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

Two anesthesiology experts will participate independently:

Expert A will review each ultrasound video and answer the same set of eight clinical questions. These answers will serve as the primary human clinical reference.

Expert B will independently evaluate all responses, those from Expert A, Gemini, and ChatGPT-5.1, after they have been anonymized and randomly ordered. Expert B will not know whether a response originated from an AI model or a human expert. Expert B will assess anatomical accuracy, clarity, clinical appropriateness, and overall content quality for each answer.

If Expert A and Expert B disagree on the interpretation or quality assessment of any response, a third expert (Expert C), who is also experienced in ultrasound-guided regional anesthesia, will independently review the relevant responses. Expert C's evaluation will be used to resolve discrepancies and establish the final consensus.

Data Collected

For each TAP block video, the following information will be recorded:

Ultrasound and procedural details. Patient demographic descriptors (age, sex, BMI, ASA classification), used only for general characterization of the sample.

AI-related performance features such as response completeness, relevance, confidence level, and response time.

Conditions

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Artificial Intelegence

Study Design

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Observational Model Type

OTHER

Study Time Perspective

PROSPECTIVE

Interventions

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Gemini 2.5 Pro Evaluation

Analysis 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.

Intervention Type OTHER

ChatGPT-5.1 Evaluation

Analysis 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.

Intervention Type OTHER

Eligibility Criteria

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Inclusion Criteria

* 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

Exclusion Criteria

* 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
Minimum Eligible Age

18 Years

Maximum Eligible Age

85 Years

Eligible Sex

ALL

Accepts Healthy Volunteers

No

Sponsors

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Kanuni Sultan Suleyman Training and Research Hospital

OTHER

Sponsor Role lead

Responsible Party

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Engin Ihsan Turan

principal investigator

Responsibility Role PRINCIPAL_INVESTIGATOR

Locations

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Health Science University İstanbul Kanuni Sultan Süleyman Education and Training Hospital

Istanbul, Istanbul, Turkey (Türkiye)

Site Status RECRUITING

Countries

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Turkey (Türkiye)

Central Contacts

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Engin ihsan Turan, principal investigator

Role: CONTACT

+905382431114

Facility Contacts

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Engin ihsan Turan

Role: primary

05382431114

Other Identifiers

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SUCCESS OF LLMs in TAP BLOCK

Identifier Type: -

Identifier Source: org_study_id

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