Artificial Intelligence Supported Image Reviewing
Conditions
Keywords
artificial intelligence, image reviewing, magnetic resonance imaging
Brief summary
The aim of this randomized controlled study is to investigate whether the previously developed artificial intelligence model can triage post-radiotherapy magnetic resonance images of patients with nasopharyngeal carcinoma and assist radiologists in their interpretation.
Interventions
DIAGNOSTIC_TESTAI
An artificial intelligence model predicts the risk and contours of local recurrence for MR images and triages them before radiologists interpret them.
Sponsors
Sun Yat-sen University
Study design
Observational model
COHORT
Time perspective
PROSPECTIVE
Eligibility
Sex/Gender
ALL
Healthy volunteers
No
Inclusion criteria
* Patients with treatment naive nasopharyngeal carcinoma who had finished radiotherapy for 6 months or more * The previous magnetic resonance imaging examination had showed complete remission in the primary site * Images are acquired using a 3T magnetic resonance imaging device, including unenhanced T1-weighted and T2-weighted sequences and contrast-enhanced T1-weighted sequences
Exclusion criteria
* Patients are enrolled in this study for a specific magnetic resonance imaging scan and not for subsequent follow-up magnetic resonance imaging scans.
Design outcomes
Primary
| Measure | Time frame |
|---|---|
| sensitivity | through study completion, an average of 2 years |
Secondary
| Measure | Time frame |
|---|---|
| the sensitivity in the subgroups of different rT-stage | through study completion, an average of 2 years |
| specificity | through study completion, an average of 2 years |
| positive predictive value | through study completion, an average of 2 years |
| the incidence of cases whose recurrent risks and contours cannot be provided by the AI model | through study completion, an average of 2 years |
| total time of interpretation for all the MR images | through study completion, an average of 2 years |
| the rate of discussion with a third radiologist | through study completion, an average of 2 years |
| the detection rate of local recurrence in the AI-supported reading group | through study completion, an average of 2 years |
| negative predictive value | through study completion, an average of 2 years |
Countries
China
Contacts
Primary ContactFang-Yun Xie
Backup ContactPu-Yun OuYang
Outcome results
None listed