Point-of-care Ultrasound
Conditions
Keywords
Artificial Intelligence, POCUS (Point-of-care ultrasound), Ultrasound
Brief summary
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)
Detailed description
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.
Interventions
None listed
Sponsors
University Health Network, Toronto
Study design
Observational model
COHORT
Time perspective
PROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
18 Years to No maximum
Healthy volunteers
Yes
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.
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| To see the overall accuracy of the AI model | 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. |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| To measure the accuracy of the AI model in differentiating a empty from a full stomach | 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). |
| To measure the balanced accuracy of the AI model | Through study completion, an average of 2 years | Balanced accuracy accounts for uneven distributions of detected objects (e.g., small vs. large anatomical structures) |
| To measure the precision of the AI model | 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. |
| To measure the recall of the AI model | 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. |
| To evaluate the model's classification performance across different confidence thresholds. | 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. |
| To evaluate the model's performance in detecting different anatomical structures (e.g., organs, vessels). | 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. |
| To measure the latency and average inference time per image | 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. |
Countries
Canada
Contacts
CONTACTJayanta Chowdhury, MBBS,MD
PRINCIPAL_INVESTIGATORAnahi Perlas
University Health Network, Toronto
Outcome results
None listed