Can large language models interpret parasternal block ultrasound images
Official title: Diagnostic Accuracy of Multimodal Large Language Models in Interpreting Ultrasound Images of Parasternal Intercostal Plane Blocks: A Prospective Observational Study
Run by Kanuni Sultan Suleyman Training and Research Hospital · NCT07915206 · Data updated 2026-11-12
This study tests whether multimodal language models can read ultrasound clips from parasternal intercostal plane blocks and match expert anesthesia assessments in adults undergoing elective surgery.
Quick facts
| Study type | Observational |
|---|---|
| Ages | 18 Years and up |
| Sex | All |
| Enrollment | 32 (estimated) |
| Sponsor | Kanuni Sultan Suleyman Training and Research Hospital Academic / other |
| Locations | 1 site (Istanbul) |
| Status | Recruiting |
| Started | November 2, 2026 |
| Main results expected | April 2, 2027 |
| Study ends | April 3, 2027 |
| Registry record updated | November 6, 2026 |
| Trial ID | NCT07915206 on ClinicalTrials.gov |
Is this trial a fit for you?
✓Likely a good fit
Adults aged 18–85 years with ASA I–III undergoing elective surgery and receiving ultrasound-guided parasternal intercostal plane blocks, with complete ultrasound video recording.
✕Probably not a fit
Patients with poor-quality ultrasound recordings or those whose blocks are not interpretable by the AI may not receive any added benefit.
A plain-English reading of the study's rules. Only the study team can confirm whether you qualify.
What this trial studies
This prospective observational study will compare multimodal large language models’ interpretations of ultrasound recordings from superficial and deep parasternal intercostal plane blocks with expert anesthesiologist assessments. Ultrasound videos of the injection and local anesthetic spread will be analyzed by models such as ChatGPT and Gemini using standardized prompts. The goal is to see if these models can correctly identify anatomical structures, assess injectate spread, determine block success, and flag potential risks. By enrolling 32 adults (ASA I–III) undergoing elective surgery, the study will evaluate diagnostic concordance between AI and human experts in a real clinical setting. No additional research procedures will be performed beyond routine anesthesia practice.
Why it matters
Potential benefit: If successful, AI-assisted ultrasound interpretation could support anesthesiologists by providing rapid, supplementary assessments during regional anesthesia.
How similar studies have gone: Previous work has explored AI-assisted image interpretation in anesthesia and ultrasound, with mixed but promising results for workflow support.
Who can join: the full rules
Show full inclusion / exclusion criteria
Inclusion Criteria: * Patients aged 18-85 years. * American Society of Anesthesiologists (ASA) physical status I-III. * Undergoing elective surgery with an ultrasound-guided superficial or deep parasternal intercostal plane block. * Complete ultrasound video recording of local anesthetic injection. * Written informed consent for study participation. Exclusion Criteria: * Technical failure during block performance. * Inadequate ultrasound recording quality preventing clear visualization of the needle tip or local anesthetic spread. * Missing demographic data. * Withdrawal of informed consent at any stage of the study.
Where it's running
-
Health Science University İstanbul Kanuni Sultan Süleyman Education and Training Hospital
Istanbul, Turkey (Türkiye) Recruiting
Study contacts
- Study coordinator: Engin ihsan Turan, principal investigator
- Email: enginihsan@hotmail.com
- Phone: +9005382431114
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How to take part
- Review the eligibility criteria above with your treating physician.
- Check the official listing on ClinicalTrials.gov for the latest contact details and recruitment status.
- Contact the study coordinator or principal investigator to ask for pre-screening. Pre-screening is free and never obligates you to enroll.