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| Name | Class |
|---|---|
| Guangzhou Women and Children's Medical Center | OTHER |
| The Third Affiliated Hospital of Guangzhou Medical University | OTHER |
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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.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| Training dataset | 11,468 eligible individuals' slides for the cervical cytology screening collected from the Sun Yat-sen Memorial Hospital (SYSMH, Guangzhou, China) between January 2016 and January 2020, were randomly assigned to the training dataset (n = 9,316) and the internal validation dataset (n = 2,152) in order to train and validate the Artificial Intelligence Cervical Cancer Screening (AICS). | ||
| SYSMH internal validation dataset | 11,468 eligible individuals' slides for the cervical cytology screening collected from the Sun Yat-sen Memorial Hospital (SYSMH, Guangzhou, China) between January 2016 and January 2020, were randomly assigned to the training dataset (n = 9,316) and the internal validation dataset (n = 2,152) in order to train and validate the Artificial Intelligence Cervical Cancer Screening (AICS). | ||
| TAHGMU external validation dataset | 600 slides from 600 eligible individuals were obtained in the Third Affiliated Hospital of Guangzhou Medical University (TAHGMU, Guangzhou, China) between January 2016 and January 2020, which was used to validate the Artificial Intelligence Cervical Cancer Screening (AICS). | ||
| GWCMC external validation dataset | 600 slides from 600 eligible individuals were obtained in Guangzhou Women and Children Medical Center (GWCMC, Guangzhou, China) between January 2016 and January 2020, which was used to validate the Artificial Intelligence Cervical Cancer Screening (AICS). | ||
| Prospective validation dataset |
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| Measure | Description | Time Frame |
|---|---|---|
| Area under ROC curve (AUC) | Area under the curve | Diagnostic evaluation will be performed within 1 week when the smear pictures are obtained |
| Measure | Description | Time Frame |
|---|---|---|
| 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 (%). | Diagnostic evaluation will be performed within 1 week when the smear pictures are obtained |
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Inclusion Criteria:
Exclusion Criteria:
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Female patients who were 18 years or older with clear diagnostic results of cervical liquid-based cytological examination were included. All cases were collected from Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University.
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| Name | Affiliation | Role |
|---|---|---|
| Herui Yao, PhD | Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University | Principal Investigator |
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Guangzhou Women and Children's Medical Center | Guangzhou | Guangdong | 510000 | China | ||
| The Third Affiliated Hospital of Guangzhou Medical University |
Requests for the data collected and analyzed in this study will be considered if the application is in line with public benefits and the applicant is willing to sign a data access agreement. Contact can be through the corresponding author.
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Samples Without DNA: Samples retained, with no potential for DNA extraction from any retained samples (e.g., fixed tissue, plasma)
A prospective validation dataset was conducted to distinguish the diagnostic performance of the cytopathologists, AICS, and AICS-assisted cytopathologists, in which 2,780 eligible slides from 2,780 individuals were obtained and prospectively labeled between August 28, 2020 and October 16, 2020 at SYSMH.
| Randomized controlled trial | A prospective randomized controlled trial was conducted to compare the performance of the cytopathologists, AICS, and AICS-assisted cytopathologists in SYSMH. Here, 618 slides were collected between August 13, 2020, and December 14, 2020, to build the SYSMH randomized controlled trial. The remaining 608 slides after quality control were randomly assigned (1:1:1) to the AICS group (n = 201), the cytopathologists group (n = 203), and the AICS-assisted cytopathologists group (n = 204). |
| 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 (%). |
| 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). | Diagnostic evaluation will be performed within 1 week when the smear pictures are obtained |
| Guangzhou |
| Guangdong |
| 510000 |
| China |
| Sun Yat-Sen Memorial Hospital of Sun Yat-sen University | Guangzhou | Guangdong | 510120 | China |
| ID | Term |
|---|---|
| D002583 | Uterine Cervical Neoplasms |
| D009369 | Neoplasms |
| ID | Term |
|---|---|
| D014594 | Uterine Neoplasms |
| D005833 | Genital Neoplasms, Female |
| D014565 | Urogenital Neoplasms |
| D009371 | Neoplasms by Site |
| D002577 | Uterine Cervical Diseases |
| D014591 | Uterine Diseases |
| D005831 | Genital Diseases, Female |
| D052776 | Female Urogenital Diseases |
| D005261 | Female Urogenital Diseases and Pregnancy Complications |
| D000091642 | Urogenital Diseases |
| D000091662 | Genital Diseases |
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