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临床试验/NCT03647618
NCT03647618已完成不适用

Anatomy Guidance for Regional Anaesthesia

Medaphor Limited1 个研究点 分布在 1 个国家目标入组 151 人开始时间: 2018年12月20日最近更新:
适应症

试验速览

阶段
不适用
状态
已完成
入组人数
151
试验地点
1
主要终点
Phase II Validation Outcome Measures - Validation

研究概览

简要总结

This mutlicentre study at three hospitals in south Wales, UK, will be used to determine if modern machine learning techniques can help the anaesthetist locate the target by highlighting key anatomical features on the ultrasound image in real time.

The study consists of two phases:

The objective of Phase I is to train a computer-aided system to identify target structures in regional anaesthesia when applied in the following categories:

  • Adductor canal
  • Popliteal
  • Fascia Iliaca
  • Rectus sheath
  • Axillary The objective of Phase II is to estimate the success rate and safety of the computer system being developed.

详细描述

Use of regional anaesthesia (RA) and peripheral nerve block (PNB) is growing, although general anaesthesia (GA) is still more common in general surgical practice. Around 65% of all procedures amenable to a regional technique currently use GA, and current UK National Institute of Health and Clinical Excellence (NICE) guidance is that all regional anaesthesia should be performed using ultrasound guidance. However, further increases in regional anaesthesia are expected, as there are significant patient and economic benefits. In particular, the per-procedure costs of regional anaesthesia are considerably less than for general anaesthesia.

Although growing, ultrasound-guided regional anaesthesia is difficult to learn and difficult to perform. There are significant hand-eye-coordination issues as the clinician must simultaneously manipulate both the needle and the ultrasound probe in order to guide the needle to the target. In addition, both the needle and target anatomy can be very difficult to see on the ultrasound image.

The investigators believe that a computer-aided system that highlights key anatomical features on the ultrasound image would make this procedure safer for the patients and simpler for the clinician.

Currently, the leading method for automatic image segmentation uses deep learning, for which many thousands of training images are required. There have been several successes in applying these techniques to medical images, including ultrasound. However, it appears that relatively little attention has been given to automatic segmentation of ultrasound images for regional anaesthesia.

The closest reference to our proposed research describe a method to locate the median nerve in ultrasound images of the forearm. There has also been a Kaggle challenge to segment the brachial plexus nerves in the neck. Multiple anatomical regions can also be segmented at the same time. However, none of these studies are directly applicable to clinical use as they deal only with images captured from healthy volunteers. Neither do they consider how these techniques could be used to aid anaesthetists performing regional anaesthesia in the clinic.

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

入选标准

  • Male or female, at least 18 years of age;
  • Undergoing regional anaesthesia as part of their treatment at the Royal Gwent Hospital, Ystrad Mynach Hospital and St Woolos Hospital, Wales, UK.
  • 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

Phase II Validation Outcome Measures - Validation

时间窗: 3 months

• Validation of the models generated in Phase I using a validation dataset including:- * Estimation of performance and accuracy (e.g. success/failure of highlighting of target structures, average distance of highlighting from target, time spent highlighting correct structure as a proportion of time target visible) * Estimation of safety (e.g. instances where incorrect highlighting deemed unsafe)

Phase 1 Outcome Measures: - Training/Verification •

时间窗: 3 months

Development and verification of models that identify the target structures using a training dataset. Refinement of Phase II endpoint.

次要结局

未报告次要终点

研究者

申办方类型
Industry
责任方
Sponsor

研究点 (1)

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