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AI for Gastric POCUS ( Point-of-care Ultrasound) (POCUS)

2026년 7월 29일 업데이트: University Health Network, Toronto

Development of an Artificial Intelligence Algorithm to Enhance the Gastric Point-of-care Ultrasound. A Proof-of-concept Study.

The goal of this observational study is to train and test an AI (Artificial Intelligence)-based program to assist anesthesiologists in the interpretation of stomach ultrasound images and differentiate a "full" from an "empty" stomach.

It is a healthy-volunteer study, where the participants will undergo ultrasound examination of their stomach at three different time points to visualize the stomach contents. These are at fasting state, after taking some solid food and after taking some water. Here, the participants will be randomized to receive one of five different types solid foods and one of five different volumes of water. The stomach ultrasound images will then be used to train and test the accuracy of the model to diagnose the type of stomach content (nothing vs. clear fluid vs. solid food)

연구 개요

상태

모병

정황

상세 설명

Gastric (stomach) Point-of-care ultrasound (POCUS) is an ultrasound examination done at bedside to assess the stomach. It is a validated non-invasive way to find out what is the content in the stomach and its volume. Gastric POCUS is increasingly used before surgery to determine the risk of gastric contents going into the lungs (possibly causing a lung infection and breathing problems) and guide anesthetic management whenever the doctors are not certain about the stomach content based on clinical information.

Gastric POCUS is a relatively new skill for anesthesiologists. While, obtaining the required images is relatively straightforward, the interpretation of such images, however, requires advanced training. Preliminary data have suggested that Artificial Intelligence (AI)-based programs and devices can help in image capturing and its interpretation for other ultrasound applications. This study will be the first to the researcher's knowledge to develop an AI algorithm to enhance anesthesiologists' ability to recognize a full stomach using gastric POCUS. The goal of this observational study is to train and test an AI (Artificial Intelligence)-based program to assist anesthesiologists in the interpretation of stomach ultrasound images and differentiate a "full" from an "empty" stomach.

This is an observational prospective cohort study that follows the CONSORT (Consolidated Standards of Reporting Trials)-AI extension reporting guidelines.

The researchers expect to enroll 30 healthy volunteers for the study.

Following a period of fasting for solids for at least 8 hours and clear fluids for at least 2 hours from the time of study visit. An anesthesiologist or sonographer with a minimum previous experience of 50 gastric ultrasound examinations will perform a standardized gastric ultrasound exam.

A baseline ultrasound examination will be conducted first with the participant lying on their back with the head elevated at 30 degrees (supine position) and then again with the participant lying on their right side (right lateral decubitus position(RLD)).

The same procedure will be repeated twice after ingestion of

  1. various volumes of water (100-500) determined at random
  2. 1 of 5 different solid or thick fluid foods also determined at random (a banana, an apple, a cup of yogurt, a croissant or a muffin).

Each one of the 30 participants will be randomized to 1 of 5 different volumes of water (100ml, 200ml, 300ml, 400ml, 500ml). Then ultrasound images will be obtained. Subsequently, each participant will also be randomized to 1 of 5 solids (a banana, an apple, a cup of yogurt, a croissant or a muffin) in a 1:1:1:1:1 ratio. A computer-generated list of random numbers for each participant will be created.

The investigators plan to collect 90 10-second clips in total, and each clip can be deconstructed into 10 frozen frames per second, for a total of 100 frozen frames per clip. The investigators expect to generate 9,000 individual images, 80% of which will be used to train the model, 10% to fine-tune and 10% to test the model accuracy. The three de-identified clips from each participant will be normalized and annotated by consultant anesthesiologists to indicate orientation (medial or lateral, cephalad or caudad) and identify relevant structures, as well as the type of content and antral CSA in the right lateral decubitus in case of fluid.

All the collected images will then be fed to an AI to generate computational data.

연구 유형

관찰

등록 (추정된)

30

연락처 및 위치

이 섹션에서는 연구를 수행하는 사람들의 연락처 정보와 이 연구가 수행되는 장소에 대한 정보를 제공합니다.

연구 연락처

연구 장소

  • 캐나다
    • Ontario
      • Toronto, Ontario, 캐나다, M5T 2S8
        • 모병
        • Toronto Western Hospital, University Health Network
        • 연락하다:
        • 수석 연구원:
          • Anahi Perlas, MD, FRCPC

참여기준

연구원은 적격성 기준이라는 특정 설명에 맞는 사람을 찾습니다. 이러한 기준의 몇 가지 예는 개인의 일반적인 건강 상태 또는 이전 치료입니다.

자격 기준

공부할 수 있는 나이

  • 성인
  • 고령자

건강한 자원 봉사자를 받아들입니다

예

샘플링 방법

비확률 샘플

연구 인구

Adult healthy volunteer aged ≥18 years

설명

Inclusion Criteria:

A. Inclusion Criteria at the level of the participants

  • Participants must meet all the following inclusion criteria to be eligible for the study:

    • Aged ≥18 years
    • Any sex
    • Be healthy

B. Inclusion Criteria at the level of the input data • Transverse ultrasound images (10 sec clips) of the gastric antrum in the epigastric area that contain all these structures:

  • The edge of the left lobe of the liver
  • The gastric antrum
  • The pancreas
  • The aorta

Exclusion Criteria:

A. Exclusion Criteria at the level of the participants

  • Participants meeting any of the following exclusion criteria are ineligible for the study:

    • Previous gastro-esophageal surgery (e.g., gastric by-pass, sleeve gastrectomy, fundoplication, partial gastrectomy)
    • Allergy to any of the food that will be provided.

B. Exclusion Criteria at the level of the input data

• Ultrasound images (10 sec clips) where the gastric antrum cannot be positively identified.

공부 계획

이 섹션에서는 연구 설계 방법과 연구가 측정하는 내용을 포함하여 연구 계획에 대한 세부 정보를 제공합니다.

연구는 어떻게 설계됩니까?

디자인 세부사항

연구는 무엇을 측정합니까?

주요 결과 측정

결과 측정
측정값 설명
기간
To see the overall accuracy of the AI model
기간: Through study completion, an average of 2 years
To see the overall accuracy of the AI-enhanced ultrasound model to differentiate no content and clear fluid from solid.
Through study completion, an average of 2 years

2차 결과 측정

결과 측정
측정값 설명
기간
To measure the accuracy of the AI model in differentiating a empty from a full stomach
기간: Through study completion, an average of 2 years
To see the accuracy of the AI-enhanced ultrasound model to differentiate an "empty" (no content or clear fluid with an antral CSA (Cross-sectional Area)< 10 cm2 in the RLD) from a "full" stomach (solid content or clear fluid with an antral CSA > 10cm2 in the RLD).
Through study completion, an average of 2 years
To measure the balanced accuracy of the AI model
기간: Through study completion, an average of 2 years
Balanced accuracy accounts for uneven distributions of detected objects (e.g., small vs. large anatomical structures)
Through study completion, an average of 2 years
To measure the precision of the AI model
기간: Through study completion, an average of 2 years
Precision evaluates the proportion of true positives among detected objects, addressing false positives that can lead to unnecessary interventions in clinical settings.
Through study completion, an average of 2 years
To measure the recall of the AI model
기간: Through study completion, an average of 2 years
(b) Recall (sensitivity) quantifies the model's ability to detect all relevant objects (true positives), critical for avoiding missed detections (false negatives) in important medical diagnoses.
Through study completion, an average of 2 years
To evaluate the model's classification performance across different confidence thresholds.
기간: Through study completion, an average of 2 years
Receiver Operating Characteristic (ROC) curves and Area Under Curve (AUC) will be computed to evaluate the model's classification performance across different confidence thresholds.
Through study completion, an average of 2 years
To evaluate the model's performance in detecting different anatomical structures (e.g., organs, vessels).
기간: Through study completion, an average of 2 years
Class-specific mean Average Precision (mAP) will be calculated to evaluate the model's performance in detecting different anatomical structures (e.g., organs, vessels). mAP is the standard metric for object detection tasks, summarizing precision and recall across multiple confidence thresholds.
Through study completion, an average of 2 years
To measure the latency and average inference time per image
기간: Through study completion, an average of 2 years
Given the clinical need for real-time feedback during ultrasound procedures, the average inference time per image and latency will be measured for each model.
Through study completion, an average of 2 years

공동 작업자 및 조사자

여기에서 이 연구와 관련된 사람과 조직을 찾을 수 있습니다.

스폰서

University Health Network, Toronto

수사관

  • 수석 연구원: Anahi Perlas, University Health Network, Toronto

연구 기록 날짜

이 날짜는 ClinicalTrials.gov에 대한 연구 기록 및 요약 결과 제출의 진행 상황을 추적합니다. 연구 기록 및 보고된 결과는 공개 웹사이트에 게시되기 전에 특정 품질 관리 기준을 충족하는지 확인하기 위해 국립 의학 도서관(NLM)에서 검토합니다.

연구 주요 날짜

연구 시작 (실제)

2026년 5월 5일

기본 완료 (추정된)

2027년 12월 31일

연구 완료 (추정된)

2027년 12월 31일

연구 등록 날짜

최초 제출

2026년 4월 27일

QC 기준을 충족하는 최초 제출

2026년 5월 6일

처음 게시됨 (실제)

2026년 5월 12일

연구 기록 업데이트

마지막 업데이트 게시됨 (실제)

2026년 7월 31일

QC 기준을 충족하는 마지막 업데이트 제출

2026년 7월 29일

마지막으로 확인됨

2026년 4월 1일

추가 정보

이 연구와 관련된 용어

키워드

기타 연구 ID 번호

  • 25-6148

약물 및 장치 정보, 연구 문서

미국 FDA 규제 의약품 연구

아니

미국 FDA 규제 기기 제품 연구

아니

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