An artificial intelligence-assisted system is trained and validated by collecting nasopharyngolaryngoscopy images from patients.
To address the clinical pain points of traditional nasopharyngolaryngoscopy, such as incomplete visualization, inaccurate identification, and unclear imaging, this study will retrospectively collect nasopharyngolaryngoscopy images and baseline information (including gender and age) of patients who underwent nasopharyngolaryngoscopy at participating centers for model training and validation. Deep learning algorithms will be applied to construct the model. The final clinical performance evaluation of the model will be conducted using an independent, prospectively collected test cohort.
Study Type
OBSERVATIONAL
Enrollment
500
The deep learning model is trained using the training dataset and tested with the internal validation set.
The prospective dataset is used for the comparative testing of the model and physicians.
Ruijin Hospital, Shanghai Jiao Tong University School of Medicine
Shanghai, China
RECRUITINGperformance of lesion detection
The area under the receiver operating characteristic curve (ROC-AUC) of the model for abnormal lesion detection
Time frame: Within 3 months after the completion of prospective data collection
performance of anatomic site recognition
The average precision (AP) of the model for recognizing nasopharyngeal and laryngeal anatomic sites
Time frame: Within 3 months after the completion of prospective data collection
Comparison of diagnostic performance between the model and physicians
Differences in sensitivity, specificity, and overall accuracy between the AI model and endoscopists with different years of experience
Time frame: Within 3 months after the completion of prospective data collection
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