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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.
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.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| Experiment | Experimental | 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. |
|
| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| S-Detect for Thyroid | 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. |
| Measure | Description | Time Frame |
|---|---|---|
| Accuracy of S-Detect diagnosis | Number of correct thyroid nodule malignancy diagnoses out of total malignancy diagnoses by the AI-based ultrasound diagnostic system "S-Detect" on the five patients' thyroid nodules. Gold standard is cytology and histology of the nodules. | 1 day (day of experiment) |
| Measure | Description | Time Frame |
|---|---|---|
| Accuracy of biopsy recommendation | Number of correct biopsy recommendations on the five patients' thyroid nodules. Recommendation is derived from EUTIRADS analyses done by participants with and without AI assistance. Gold standard is biopsy recommendation derived from expert consensus EUTIRADS assessment of the nodules. | 1 day (day of experiment) |
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Medical students
Inclusion Criteria:
Exclusion Criteria:
Junior ENT registrar doctors
Inclusion Criteria:
Senior ENT registrar doctors
Inclusion Criteria:
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| Name | Affiliation | Role |
|---|---|---|
| Tobias Todsen, Ph.d | Rigshospitalet, Denmark | Study Director |
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Rigshospitalet | Copenhagen | 2100 | Denmark |
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| ID | Term |
|---|---|
| D016606 | Thyroid Nodule |
| D013964 | Thyroid Neoplasms |
| ID | Term |
|---|---|
| D004701 | Endocrine Gland Neoplasms |
| D009371 | Neoplasms by Site |
| D009369 | Neoplasms |
| D006258 | Head and Neck Neoplasms |
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20 participants scan five patients over the course of fours hour and collect their diagnostics analyses on paper forms.
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|
| Nodule measurement | Measurement of the five patients' thyroid nodules done by participants and S-Detect. Gold standard are measurements from expert consensus assessment analysis of the nodules. | 1 day (day of experiment) |
| OSAUS score | Mean OSAUS (objective structured assessment of ultrasound skills) scores of participants as assessed from their ultrasound scans of the five patients. Assessment are independently by two head-and-neck ultrasound experts. | 1 day (day of experiment) |
| D004700 |
| Endocrine System Diseases |
| D013959 | Thyroid Diseases |