Lung Cancer
Conditions
Brief summary
This study investigates ways of improving radiologists performance of the classification of CT-scans as cancerous or non-cancerous. Participants interact with an AI to classify CT-scans under three different conditions.
Detailed description
The three conditions are as follows: probabilistic classification, where the radiologist diagnoses scans using an AI cancer likelihood score; classification plus detection, where the radiologist see detecting lung nodules in addition to the AI's probabilistic classification score before making her own examination of the CT-scan; and classification with delayed detection, where the radiologist identifies regions of interest independently of the AI and then sees the AI's detected ROIs.
Interventions
BEHAVIORALAI-human interaction
Exploring what kinds of AI-human interaction improve radiologists detection accuracy.
Sponsors
Ensemble Group Holdings, LLC
Study design
Allocation
RANDOMIZED
Intervention model
CROSSOVER
Primary purpose
DIAGNOSTIC
Masking
SINGLE (Subject)
Eligibility
Sex/Gender
ALL
Healthy volunteers
Yes
Inclusion criteria
* The participant performs radiology screenings professionally
Exclusion criteria
\-
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Classification accuracy | up to 4 months after initiation of evaluation of the test set | This compares radiologists' classifications with the ground truth in the tested cases. |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| detection concordance | up to 4 months after initiation of evaluation of the test set | Evaluation of concordance between radiologists in the tested cases in detection of lung nodules \> 4 mm |
Countries
Hong Kong
Outcome results
None listed