Development of an Artificial Intelligence Algorithm to Enhance the Gastric Point-of-care Ultrasound. A Proof-of-concept Study.
试验速览
- 阶段
- 不适用
- 状态
- 招募中
- 入组人数
- 30
- 试验地点
- 2
研究概览
简要总结
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)
详细描述
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.
研究设计
- 研究类型
- Observational
- 观察模型
- Cohort
- 时间视角
- Prospective
入排标准
- 年龄范围
- 18 Years 至 —（Adult, Older Adult）
- 性别
- All
- 接受健康志愿者
- 是
入选标准
- •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
- •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
排除标准
- •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.