Clinical trial NCT06306599: Human-AI Collaboration for Ultrasound Diagnosis of Thyroid Nodules - a Clinical Trial
Official title: Human-AI Collaboration for Ultrasound Diagnosis of Thyroid Nodules - a Clinical Trial
Study type: INTERVENTIONAL
Overall status: COMPLETED
Phase: NA
Conditions: Thyroid Nodule
Keywords: Ultrasound
AI
Thyroid cancer
Lead sponsor: Rigshospitalet, Denmark
Sponsor class: OTHER
First posted: 2024-03-12
Enrollment: 20
Sex: ALL
Standard ages: CHILD
ADULT
OLDER_ADULT
Accepts healthy volunteers: yes
Allocation: NA
Intervention model: SINGLE_GROUP
Intervention model description: 20 participants scan five patients over the course of fours hour and collect their diagnostics analyses on paper forms.
Primary purpose: DIAGNOSTIC

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.

Eligibility criteria: Medical students

Inclusion Criteria:

* Last year student

Exclusion Criteria:

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

Junior ENT registrar doctors

Inclusion Criteria:

* Doctor enrolled in introductory training as ENT physician.

Senior ENT registrar doctors

Inclusion Criteria:

* Doctor enrolled in ENT training.

Primary outcomes:
measure: Accuracy of S-Detect diagnosis
timeFrame: 1 day (day of experiment)
Secondary outcomes:
measure: Accuracy of biopsy recommendation
timeFrame: 1 day (day of experiment)
measure: Nodule measurement
timeFrame: 1 day (day of experiment)
measure: OSAUS score
timeFrame: 1 day (day of experiment)
Other outcomes:
Locations:
city: Copenhagen
country: Denmark
facility: Rigshospitalet
Study officials:
affiliation: Rigshospitalet, Denmark
name: Tobias Todsen, Ph.d
role: STUDY_DIRECTOR
References: