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

The objective of the THYMAL 01 pilot study is to evaluate the Sound Blade Imaging System in subjects with benign thyroid nodules undergoing standard-of-care ultrasound imaging.

Study Overview

• Status: Not yet recruiting
• Conditions: Thyroid Nodules; Thyroid Abnormalities
• Intervention / Treatment: Diagnostic test: Thyroid ultrasound

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: Interventional
• Enrollment (Estimated): 30
• Phase: Not Applicable

Contacts and Locations

• 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

• Ages Eligible for Study: Adult; Older Adult
• Accepts Healthy Volunteers: No

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

1

Arms and Interventions

Participant Group / Arm	Intervention / 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.	Diagnostic test: Thyroid 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

Outcome Measure	Measure Description	Time Frame
The Ability to Visualization Thyroid Nodules and other key Anatomical Structures	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	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	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: Sound Blade Medical Inc.
• Investigators: Principal Investigator: Richard Bayls, MD, Nova Scotia Health Authority

Study record dates

Study Major Dates

• Study Start (Estimated): May 31, 2026
• Primary Completion (Estimated): December 31, 2026
• Study Completion (Estimated): September 1, 2027

Study Registration Dates

• First Submitted: February 27, 2026
• First Submitted That Met QC Criteria: March 18, 2026
• First Posted (Actual): March 23, 2026

Study Record Updates

• Last Update Posted (Actual): April 7, 2026
• Last Update Submitted That Met QC Criteria: April 3, 2026
• Last Verified: April 1, 2026

More Information

Terms related to this study

• Keywords: Thyroid Nodule
• Additional Relevant MeSH Terms: Endocrine System Diseases; Neoplasms by Site; Neoplasms; Thyroid Neoplasms; Endocrine Gland Neoplasms; Head and Neck Neoplasms; Thyroid Diseases; Thyroid Nodule
• Other Study ID Numbers: THYMAL 01

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: NO
• IPD Plan Description: This is an internal feasibility study at this point.

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No