A Novel Ultrasound Probe for Thyroid Imaging and Machine Learning (THYMAL 01)
A Novel Ultrasound Probe for Thyroid Imaging and Machine Learning: A Pilot Study
Study Overview
Status
Status
Conditions
Conditions
Intervention / Treatment
Intervention / Treatment
Detailed Description
Our purpose-built thyroid US platform, integrated with state-of-the-art machine-learning algorithms, will achieve accurate detection and delineation of thyroid nodules and neighboring critical structures, and will produce a trackable three-dimensional nodule model capable of guiding minimally invasive ablation while providing real-time volumetric quantification of treatment progress and residual disease.
STUDY DESIGN This study will consist of two parts. Part A - Prospective Thyroid Imaging Study We will conduct a prospective, paired imaging study (n=30) to determine whether the study US device acquires results/ability to detect thyroid nodules and key structures (common carotid artery, internal jugular vein, trachea, vagus nerve, recurrent laryngeal nerve, and parathyroid glands) that are comparable to the standard-of-care (SOC) US device.
Part B - Machine Learning Model Development US images and cine video acquired from the investigational device and radiologist annotations from Part A will be used to train and internally test an ML model for nodule and critical-structure segmentation. For external validation the ML model will be applied to anonymized thyroid US studies acquired on the standard-of-care (SOC) US and retrieved retrospectively from the provincial PACS (n≈30). Accuracy and precision will be assessed to establish technical validity and generalizability. ML outputs are for research only; no clinical decisions will be based on them.
Study Type
Study Type
Enrollment (Estimated)
Enrollment
Phase
Phase
- Not Applicable
Contacts and Locations
Study Contact
Study Contact
- Name: Deborah Wright, Project Manager, RN
- Phone Number: 902-225-6835
- Email: debbie.wright@nshealth.ca
Study Contact Backup
- Name: Richard Balys, MD, MD
- Phone Number: 902-223-8032
- Email: rbalys@gmail.com
Participation Criteria
Eligibility Criteria
Eligibility Criteria
Ages Eligible for Study
- Adult
- Older Adult
Accepts Healthy Volunteers
Description
Inclusion Criteria:
- Participants must be ≥ 18 years of age
- Able and willing to sign informed consent
- Known thyroid nodule demonstrated on a prior thyroid US and is undergoing active surveillance of the nodule with serial USs
Exclusion Criteria:
- Unable to lay flat for 15 minutes
- Active neck wounds, dressings, or skin conditions that would interfere with neck US (e.g., preclude transducer placement)
- Cervical spine or back disease that would prevent neck extension and would hinder the ability to obtain accurate thyroid US images
- Previous thyroid surgeries, radiation of face and neck
- Known inflammatory thyroid diseases or thyroiditis (e.g., Graves, Hashimoto)
Study Plan
How is the study designed?
Design Details
- Primary Purpose: Diagnostic
- Allocation: N/A
- Interventional Model: Single Group Assignment
- Masking: None (Open Label)
Number of Arms
Arms and Interventions
Participant Group / ArmParticipant Group / Arm |
Intervention / TreatmentIntervention / Treatment |
|---|---|
|
Experimental: Thyroid Ultrasound
Patients who are scheduled for a SOC surveillance Thyroid Nodule Ultrasound will undergo a second ultrasound using the Sound Blade Ultrasound.
|
Patients who are scheduled for a SOC surveillance Thyroid Nodule Ultrasound will undergo a second ultrasound using the Sound Blade Ultrasound.
|
What is the study measuring?
Primary Outcome Measures
Primary Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
|---|---|---|
|
The Ability to Visualization Thyroid Nodules and other key Anatomical Structures
Time Frame: Day 1
|
The primary endpoint is to evaluate the ability to visualize thyroid nodules and other key anatomical structures with the investigational device compared to the SOC US imaging device.
|
Day 1
|
|
Evaluate the visibility/detection performance of a machine-learning model
Time Frame: Day 1
|
A machine-learning model will be created to identify and segment thyroid nodules and key anatomical neck structures.
The first outcome will evaluate performance of visibility and detection using standard classification measures
|
Day 1
|
|
Evaluate the segmentation performance of a machine-learning model
Time Frame: Day 1
|
A machine-learning model will be created to identify and segment thyroid nodules and key anatomical neck structures.
The second outcome will evaluate performance on delineable cases.
|
Day 1
|
Collaborators and Investigators
Sponsor
Sponsor
Investigators
Investigators
- Principal Investigator: Richard Bayls, MD, Nova Scotia Health Authority
Study record dates
Study Major Dates
Study Start (Estimated)
Study Start
Primary Completion (Estimated)
Primary Completion
Study Completion (Estimated)
Study Completion
Study Registration Dates
First Submitted
First Submitted
First Submitted That Met QC Criteria
First Submitted That Met QC Criteria
First Posted (Actual)
First Posted
Study Record Updates
Last Update Posted (Actual)
Last Update Posted
Last Update Submitted That Met QC Criteria
Last Update Submitted That Met QC Criteria
Last Verified
Last Verified
More Information
Terms related to this study
Keywords
Additional Relevant MeSH Terms
Other Study ID Numbers
Other Study ID Numbers
- THYMAL 01
Plan for Individual participant data (IPD)
Plan to Share Individual Participant Data (IPD)?
IPD Plan Description
Drug and device information, study documents
Studies a U.S. FDA-regulated drug product
Studies a U.S. FDA-regulated device product
This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.
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