A Volunteer Study to Collect Imaging Data for the Development and Validation of Intelligent Ultrasound ScanNav Anatomy PNB
试验速览
- 阶段
- 不适用
- 状态
- 已完成
- 发起方
- 入组人数
- 100
- 试验地点
- 1
- 主要终点
- Change in per-class Mean Intersection over Union (IoU) scores of deep-learning segmentation models compared to baseline models
研究概览
简要总结
This is a single-centre, prospective, non-randomised volunteer study to be undertaken in Intelligent Ultrasound's offices at Hodge House, 114-116 St Mary Street, Cardiff, CF10 1DY, UK.
详细描述
This is a single-centre, non-randomised, prospective study involving up to 100 participants. The data collected in this study will augment the data collected in ML2018_AG_01, ML2018_AG_02, IU2019_AG_03, and IU2020_AG_04.
Phase I Collect data from participants for each indicated block area. This set will be used to develop and verify models for the identification and highlighting of target anatomical structures on ultrasound images.
Phase II Validation will proceed by feeding the videos into the ScanNav AnatomyGuide system and recording the output anatomy highlighting, overlaid on the original ultrasound image. The overlaid video will be examined by experts to identify safety and performance issues.
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
- •Potential participants who meet the following criteria will be considered eligible for the study:
- •Male or female, at least 18 years of age;
- •Able to comprehend and sign the Informed Consent prior to enrolment in the study.
排除标准
- •Potential participants who meet the following criteria will NOT be eligible for the study:
- •Aged <18 years of age;
- •Unwilling or unable to provide informed consent;
- •Previous surgery or trauma to the affected area.
结局指标
主要结局
Change in per-class Mean Intersection over Union (IoU) scores of deep-learning segmentation models compared to baseline models
时间窗: 6 months
The mean Intersection over Union will be computed for each class for each model trained with the enlarged dataset, using the existing unseen test data set. These scores will be compared with the scores from the existing baseline models to determine the effect of the enlarged training data
次要结局
未报告次要终点