Diese Seite wurde automatisch übersetzt und die Genauigkeit der Übersetzung wird nicht garantiert. Bitte wende dich an die englische Version für einen Quelltext.

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

6. Mai 2026 aktualisiert von: 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)

Studienübersicht

Status

Noch keine Rekrutierung

Bedingungen

Detaillierte Beschreibung

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.

Studientyp

Beobachtungs

Einschreibung (Geschätzt)

30

Kontakte und Standorte

Dieser Abschnitt enthält die Kontaktdaten derjenigen, die die Studie durchführen, und Informationen darüber, wo diese Studie durchgeführt wird.

Studienkontakt

Studienorte

  • Kanada
    • Ontario
      • Toronto, Ontario, Kanada, M5T 2S8
        • Toronto Western Hospital, University Health Network
        • Kontakt:
        • Hauptermittler:
          • Anahi Perlas, MD, FRCPC

Teilnahmekriterien

Forscher suchen nach Personen, die einer bestimmten Beschreibung entsprechen, die als Auswahlkriterien bezeichnet werden. Einige Beispiele für diese Kriterien sind der allgemeine Gesundheitszustand einer Person oder frühere Behandlungen.

Zulassungskriterien

Studienberechtigtes Alter

  • Erwachsene
  • Älterer Erwachsener

Akzeptiert gesunde Freiwillige

Ja

Probenahmeverfahren

Nicht-Wahrscheinlichkeitsprobe

Studienpopulation

Adult healthy volunteer aged ≥18 years

Beschreibung

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.

Studienplan

Dieser Abschnitt enthält Einzelheiten zum Studienplan, einschließlich des Studiendesigns und der Messung der Studieninhalte.

Wie ist die Studie aufgebaut?

Designdetails

Was misst die Studie?

Primäre Ergebnismessungen

Ergebnis Maßnahme
Maßnahmenbeschreibung
Zeitfenster
To see the overall accuracy of the AI model
Zeitfenster: 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

Sekundäre Ergebnismessungen

Ergebnis Maßnahme
Maßnahmenbeschreibung
Zeitfenster
To measure the accuracy of the AI model in differentiating a empty from a full stomach
Zeitfenster: 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
Zeitfenster: 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
Zeitfenster: 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
Zeitfenster: 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.
Zeitfenster: 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).
Zeitfenster: 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
Zeitfenster: 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

Mitarbeiter und Ermittler

Hier finden Sie Personen und Organisationen, die an dieser Studie beteiligt sind.

Sponsor

University Health Network, Toronto

Ermittler

  • Hauptermittler: Anahi Perlas, University Health Network, Toronto

Studienaufzeichnungsdaten

Diese Daten verfolgen den Fortschritt der Übermittlung von Studienaufzeichnungen und zusammenfassenden Ergebnissen an ClinicalTrials.gov. Studienaufzeichnungen und gemeldete Ergebnisse werden von der National Library of Medicine (NLM) überprüft, um sicherzustellen, dass sie bestimmten Qualitätskontrollstandards entsprechen, bevor sie auf der öffentlichen Website veröffentlicht werden.

Haupttermine studieren

Studienbeginn (Geschätzt)

8. Mai 2026

Primärer Abschluss (Geschätzt)

31. Dezember 2027

Studienabschluss (Geschätzt)

31. Dezember 2027

Studienanmeldedaten

Zuerst eingereicht

27. April 2026

Zuerst eingereicht, das die QC-Kriterien erfüllt hat

6. Mai 2026

Zuerst gepostet (Tatsächlich)

12. Mai 2026

Studienaufzeichnungsaktualisierungen

Letztes Update gepostet (Tatsächlich)

12. Mai 2026

Letztes eingereichtes Update, das die QC-Kriterien erfüllt

6. Mai 2026

Zuletzt verifiziert

1. April 2026

Mehr Informationen

Begriffe im Zusammenhang mit dieser Studie

Schlüsselwörter

Andere Studien-ID-Nummern

  • 25-6148

Arzneimittel- und Geräteinformationen, Studienunterlagen

Studiert ein von der US-amerikanischen FDA reguliertes Arzneimittelprodukt

Nein

Studiert ein von der US-amerikanischen FDA reguliertes Geräteprodukt

Nein

Diese Informationen wurden ohne Änderungen direkt von der Website clinicaltrials.gov abgerufen. Wenn Sie Ihre Studiendaten ändern, entfernen oder aktualisieren möchten, wenden Sie sich bitte an register@clinicaltrials.gov. Sobald eine Änderung auf clinicaltrials.gov implementiert wird, wird diese automatisch auch auf unserer Website aktualisiert .

Klinische Studien zur Point-of-Care-Ultraschall

Suchen Sie nach ähnlichen Studien

Sponsoren und Mitarbeiter

Krankheiten

Drogeninterventionen

CROs by country

CROs in Venezuela

Bedingungen

Seltene Krankheiten

Drogeninterventionen

Nahrungsergänzungsmittel

Sponsor / Mitarbeiter

Standorte

Abonnieren