Human-AI Collaboration for Ultrasound Diagnosis of Thyroid Nodules - a Clinical Trial

NCT ID: NCT06306599

Last Updated: 2024-11-25

Basic Information

• Recruitment Status: COMPLETED
• Clinical Phase: NA
• Total Enrollment: 20 participants
• Study Classification: INTERVENTIONAL
• Study Start Date: 2023-09-01
• Study Completion Date: 2023-11-04

Brief Summary

This is an experimental study wherein groups of medical students and physicians of varying degrees of experience in head-and-neck ultrasound were asked to scan the same five patients each with a thyroid nodule.

The study participants did their own ultrasound assessment of the thyroid nodules, as well as using an AI-based ultrasound diagnostics system.

The researchers intended to study two primary outcomes: 1) how varying degrees of experience in ultrasound by the operator might affect the diagnostic performance of the AI-based system, and 2) how the AI-based system influenced the diagnostic performance of the ultrasound operator.

Detailed Description

This is a prospective clinical study aiming to test how the experience of the ultrasound operator influences the performance of AI-based (artificial intelligence-based) diagnostics when analysing thyroid nodules on ultrasound scans. The investigators set up an experiment with five stations, each with a patient with a thyroid nodule and an ultrasound machine with the deep learning based system S-Detect for Thyroid installed. 20 study participants where recruited: 8 medical students of novice ultrasound skill, 3 junior ENT (ear-nose-throat) registrars of intermediate ultrasound skill, and 9 senior ENT registrars experienced in ultrasound. The participants scanned all the patients and recorded their analyses of the nodules using the EUTIRADS (European thyroid imagining reporting and data system) system in three different ways: a analysis of their own, S-Detect's analysis, and an analysis combining the two previous.

The hypothesis was that the AI system would perform equally well when between the participant groups. In addition, it was expected that the experienced participants would perform better than the students without AI help, and that the doctors would gain little from AI input, but that the students would have their performance improved by AI input.

Conditions

Thyroid Nodule

Study Design

• Allocation Method: NA
• Intervention Model: SINGLE_GROUP
• Primary Study Purpose: DIAGNOSTIC
• Blinding Strategy: NONE

Study Groups

Experiment

20 participants ultrasound scan five patients with thyroid nodules, and assess these nodules themselves, then with the AI-program, and at last they give a combined assessment.

Group Type: EXPERIMENTAL

S-Detect for Thyroid

Intervention Type: DIAGNOSTIC_TEST

Deep learning based program on Samsung ultrasound machines designed to do real-time semi-automated analysis of thyroid nodules. The ultrasound operator freezes a transverse image of the patient's thyroid nodule and activates S-Detect. The operator selects the nodule on the screen, and the program automatically draws a region of interest. Then S-Detect gives a dichotomous diagnosis of either "Possibly benign" and "Possibly malignant". In addition, it measures the nodule and characterises it with a lexicon based on EUTIRADS.

Interventions

S-Detect for Thyroid

Deep learning based program on Samsung ultrasound machines designed to do real-time semi-automated analysis of thyroid nodules. The ultrasound operator freezes a transverse image of the patient's thyroid nodule and activates S-Detect. The operator selects the nodule on the screen, and the program automatically draws a region of interest. Then S-Detect gives a dichotomous diagnosis of either "Possibly benign" and "Possibly malignant". In addition, it measures the nodule and characterises it with a lexicon based on EUTIRADS.

Intervention Type: DIAGNOSTIC_TEST

Eligibility Criteria

Inclusion Criteria

• Last year student

• Doctor enrolled in introductory training as ENT physician.

Senior ENT registrar doctors

• Doctor enrolled in ENT training.

Exclusion Criteria

• Experience with ultrasound beyond that which is taught at the University of Copenhagen

Junior ENT registrar doctors

• Eligible Sex: ALL
• Accepts Healthy Volunteers: Yes

Sponsors

Rigshospitalet, Denmark

OTHER

Sponsor Role: lead

Responsible Party

Axel Bukhave Edström

Principal Investigator

Responsibility Role: PRINCIPAL_INVESTIGATOR

Principal Investigators

Tobias Todsen, Ph.d

Role: STUDY_DIRECTOR

Rigshospitalet, Denmark

Locations

Rigshospitalet

Copenhagen, , Denmark

Countries

Denmark

Other Identifiers

Thyroid AI US operator exp

Identifier Type: -

Identifier Source: org_study_id