The artificial intelligence‐assisted cytology diagnostic system in large‐scale cervical cancer screening: A population‐based cohort study of 0.7 million women

Autor: Sun Xiaorong, Jing Wang, Liang Zhou, Yi Zhang, Fengpin Wu, Pang Baochuan, Linhong Wang, Bojana Turic, Heling Bao, Cao Dehua, Hua Li
Jazyk: angličtina
Rok vydání: 2020
Předmět:
0301 basic medicine
Cancer Research
Biopsy
cervical cancer screening
Uterine Cervical Neoplasms
Method
0302 clinical medicine
Cytology
Diagnosis
Computer-Assisted
Early Detection of Cancer
Colposcopy
education.field_of_study
medicine.diagnostic_test
Middle Aged
artificial intelligence
lcsh:Neoplasms. Tumors. Oncology. Including cancer and carcinogens
Oncology
030220 oncology & carcinogenesis
Female
Abnormality
Cancer Prevention
Cohort study
Adult
China
Cytodiagnosis
Population
lcsh:RC254-282
03 medical and health sciences
Young Adult
Deep Learning
cytopathology
Predictive Value of Tests
medicine
Humans
Radiology
Nuclear Medicine and imaging
education
Aged
Vaginal Smears
business.industry
Reproducibility of Results
Odds ratio
Uterine Cervical Dysplasia
030104 developmental biology
Cytopathology
Artificial intelligence
Neoplasm Grading
business
population‐based study
Zdroj: Cancer Medicine, Vol 9, Iss 18, Pp 6896-6906 (2020)
Cancer Medicine
ISSN: 2045-7634
Popis: Background Adequate cytology is limited by insufficient cytologists in a large‐scale cervical cancer screening. We aimed to develop an artificial intelligence (AI)‐assisted cytology system in cervical cancer screening program. Methods We conducted a perspective cohort study within a population‐based cervical cancer screening program for 0.7 million women, using a validated AI‐assisted cytology system. For comparison, cytologists examined all slides classified by AI as abnormal and a randomly selected 10% of normal slides. Each woman with slides classified as abnormal by either AI‐assisted or manual reading was diagnosed by colposcopy and biopsy. The outcomes were histologically confirmed cervical intraepithelial neoplasia grade 2 or worse (CIN2+). Results Finally, we recruited 703 103 women, of whom 98 549 were independently screened by AI and manual reading. The overall agreement rate between AI and manual reading was 94.7% (95% confidential interval [CI], 94.5%‐94.8%), and kappa was 0.92 (0.91‐0.92). The detection rates of CIN2+ increased with the severity of cytology abnormality performed by both AI and manual reading (P trend
This study aims to assess the role of Artificial Intelligence (AI) in the detection of early cervical cancer in a low resource setting. Our results showed that AI‐assisted cytology could identify most of negative cytology, and showed higher positive predictive value for CIN2 or worse when compared with cytologists. This study indicates that AI‐assisted cytology could be very useful tool as a primary screening method in a large‐scale cervical cancer screening program to improve its effectiveness.
Databáze: OpenAIRE
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