A Volunteer Study to Collect Imaging Data for the Development of the Medaphor Anatomy Guide.
试验速览
- 阶段
- 不适用
- 状态
- 已完成
- 入组人数
- 103
- 试验地点
- 1
- 主要终点
- Model development
研究概览
简要总结
The aim of this study is to determine if machine learning can be used to automatically highlight key anatomy on the ultrasound image to help anaesthetists perform ultrasound-guided regional anaesthesia.
详细描述
The study will involve adult volunteers who are willing to be scanned by a trained sonographer to collect ultrasound video data for the following categories:
- Adductor canal
- Popliteal fossa
- Fascia Iliaca
- Rectus sheath
- Axillary region Each volunteer will be scanned to collect data for every category in the list. Where applicable, both sides of the body will be scanned.
The videos will be segmented by hand to identify the relevant anatomical regions for each category.
The primary objective for this study is to provide the range of data required to develop robust models in conjunction with additional data from patients undergoing a Peripheral Nerve Block procedure that are able to produce the desired segmentation on the unseen validation images. The models will be scored using the standard "Mean intersection over Union" pixel-level metric for semantic segmentation.
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
- •Male or female, at least 18 years of age;
- •Willing to undergo ultrasound scanning and provide ultrasound video data for the following categories:
- •Adductor canal
- •Popliteal fossa
- •Fascia Iliaca
- •Rectus sheath
- •Axillary region
- •Able to comprehend and sign the Informed Consent prior to enrolment in the study.
排除标准
- •Aged <18 years of age;
- •Unwilling or unable to provide informed consent.
结局指标
主要结局
Model development
时间窗: 6 months
models with a Mean intersection over Union score of 0.95 or better for each region in each category.
次要结局
未报告次要终点