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.
Study Type
OBSERVATIONAL
Enrollment
10,400
An artificial intelligence model predicts the risk and contours of local recurrence for MR images and triages them before radiologists interpret them.
Sun Yat-Sen University Cancer Center
Guangzhou, Guangdong, China
sensitivity
Time frame: through study completion, an average of 2 years
specificity
Time frame: through study completion, an average of 2 years
positive predictive value
Time frame: through study completion, an average of 2 years
negative predictive value
Time frame: through study completion, an average of 2 years
total time of interpretation for all the MR images
Time frame: through study completion, an average of 2 years
the rate of discussion with a third radiologist
Time frame: through study completion, an average of 2 years
the detection rate of local recurrence in the AI-supported reading group
Time frame: through study completion, an average of 2 years
the sensitivity in the subgroups of different rT-stage
Time frame: through study completion, an average of 2 years
the incidence of cases whose recurrent risks and contours cannot be provided by the AI model
Time frame: through study completion, an average of 2 years
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