Cervical cancer, the fourth most common cancer globally and the fourth leading cause of cancer-related deaths, can be effectively prevented through early screening. Detecting precancerous cervical lesions and halting their progression in a timely manner is crucial. However, accurate screening platforms for early detection of cervical cancer are needed. Therefore, it is urgent to develop an Artificial Intelligence Cervical Cancer Screening (AICS) system for diagnosing cervical cytology grades and cancer.
Study Type
OBSERVATIONAL
Enrollment
16,164
Guangzhou Women and Children's Medical Center
Guangzhou, Guangdong, China
The Third Affiliated Hospital of Guangzhou Medical University
Guangzhou, Guangdong, China
Sun Yat-Sen Memorial Hospital of Sun Yat-sen University
Guangzhou, Guangdong, China
Area under ROC curve (AUC)
Area under the curve
Time frame: Diagnostic evaluation will be performed within 1 week when the smear pictures are obtained
Specificity
The true negative rate (TNR) of the diagnostic platform, which is the ratio between the number of negative individuals correctly categorized by platform and the total number of actual negative individuals (%).
Time frame: Diagnostic evaluation will be performed within 1 week when the smear pictures are obtained
Sensitivity
The true positive rate (TPR) of the diagnostic platform, which is the ratio between the number of positive individuals correctly categorized by platform and the total number of actual positive individuals (%).
Time frame: Diagnostic evaluation will be performed within 1 week when the smear pictures are obtained
Accuracy
The quantity of true positive (TP) plus true negative (TN) over the quantity of (TP) plus true negative (TN) plus false positive (FP) plus false negative (FN).
Time frame: Diagnostic evaluation will be performed within 1 week when the smear pictures are obtained
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