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Clinical Trial Details — Status: Completed

Administrative data

NCT number NCT04040179
Other study ID # IU2019_AG_03
Secondary ID
Status Completed
Phase
First received
Last updated
Start date August 5, 2019
Est. completion date September 30, 2019

Study information

Verified date July 2019
Source Medaphor Limited
Contact n/a
Is FDA regulated No
Health authority
Study type Observational

Clinical Trial Summary

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.


Description:

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

- ESP

- PECS

- Interscalene

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.


Recruitment information / eligibility

Status Completed
Enrollment 30
Est. completion date September 30, 2019
Est. primary completion date September 30, 2019
Accepts healthy volunteers Accepts Healthy Volunteers
Gender All
Age group 18 Years and older
Eligibility Inclusion Criteria:

1. Male or female, at least 18 years of age;

2. Willing to undergo ultrasound scanning and provide ultrasound video data for the following categories:

- Adductor canal

- Popliteal fossa

- Fascia Iliaca

- Rectus sheath

- Axillary

- ESP

- PECS

- Interscalene

3. Able to comprehend and sign the Informed Consent prior to enrolment in the study.

Exclusion Criteria:

1. Aged <18 years of age;

2. Unwilling or unable to provide informed consent.

Study Design


Related Conditions & MeSH terms

  • Ultrasound Imaging of Anatomical Structures

Locations

Country Name City State
United Kingdom Medicentre Cardiff

Sponsors (2)

Lead Sponsor Collaborator
Medaphor Limited IntelligentUltrasound Limited

Country where clinical trial is conducted

United Kingdom, 

Outcome

Type Measure Description Time frame Safety issue
Primary Model development Models with a Mean intersection over Union score of 0.95 or better for each region in each category. 6 months
See also
  Status Clinical Trial Phase
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Active, not recruiting NCT04983771 - Validity of Experts and ScanNav Anatomy PNB When Identifying Sono-anatomical Structures for Ultrasound-Guided Regional Anaesthesia
Completed NCT05583032 - An Evaluation of the Long-Term Impact of Assistive AI on Anaesthetists' Ultrasound Scanning for UGRA
Completed NCT04918693 - A Study to Evaluate the Performance and Potential Benefits of an Assistive Artificial Intelligence Device ScanNav Anatomy Peripheral Nerve Block - US V1.0 for Ultrasound-guided Regional Anesthesia
Completed NCT04906018 - A Study to Collect Imaging Data for the Validation of the Intelligent Ultrasound's ScanNav Anatomy Peripheral Nerve Block (PNB) - US v1.0
Completed NCT04830891 - A Volunteer Study to Collect Imaging Data for the Development and Validation of ScanNav Anatomy PNB
Completed NCT04277169 - A Volunteer Study to Collect Imaging Data for the Development of the IntelligentUltrasound Anatomy Guide
Completed NCT05156099 - Clinical Performance Study: Evaluate Scanning & Interpretation Performance With & Without ScanNav Anatomy PNB
Completed NCT06375603 - Nerve and Plane Block Data Collection Study for the Development of Artificial Intelligence-assisted Software
Completed NCT06025279 - Musculoskeletal System Ultrasound Examination Data Collection Study for the Development of an Artificial Intelligence Software