scholarly journals Point-of-care cervical cancer screening using deep learning-based microholography

Theranostics ◽  
2019 ◽  
Vol 9 (26) ◽  
pp. 8438-8447
Author(s):  
Divya Pathania ◽  
Christian Landeros ◽  
Lucas Rohrer ◽  
Victoria D'Agostino ◽  
Seonki Hong ◽  
...  
Keyword(s):  
Cervical Cancer ◽  
Deep Learning ◽  
Cancer Screening ◽  
Cervical Cancer Screening ◽  
Point Of Care

scholarly journals Clinical Performance Validation of 4 Point-of-Care Cervical Cancer Screening Tests in HIV-Infected Women in Zambia

2016 ◽  
Vol 20 (3) ◽  
pp. 218-223 ◽  
Author(s):  
Carla J. Chibwesha ◽  
Brigitte Frett ◽  
Katundu Katundu ◽  
Allen C. Bateman ◽  
Aaron Shibemba ◽  
...  
Keyword(s):  
Cervical Cancer ◽  
Cancer Screening ◽  
Cervical Cancer Screening ◽  
Clinical Performance ◽  
Point Of Care ◽  
Screening Tests ◽  
Cancer Screening Tests ◽  
Performance Validation

scholarly journals Accuracy and Efficiency of Deep-Learning–Based Automation of Dual Stain Cytology in Cervical Cancer Screening

2020 ◽  
Vol 113 (1) ◽  
pp. 72-79 ◽  
Author(s):  
Nicolas Wentzensen ◽  
Bernd Lahrmann ◽  
Megan A Clarke ◽  
Walter Kinney ◽  
Diane Tokugawa ◽  
...  
Keyword(s):  
Cervical Cancer ◽  
Deep Learning ◽  
Cancer Screening ◽  
Cervical Cancer Screening ◽  
Statistical Tests ◽  
Northern California ◽  
Visual Interpretation ◽  
Kaiser Permanente ◽  
Ki 67 ◽  
Subjective Analysis

Abstract Background With the advent of primary human papillomavirus testing followed by cytology for cervical cancer screening, visual interpretation of cytology slides remains the last subjective analysis step and suffers from low sensitivity and reproducibility. Methods We developed a cloud-based whole-slide imaging platform with a deep-learning classifier for p16/Ki-67 dual-stained (DS) slides trained on biopsy-based gold standards. We compared it with conventional Pap and manual DS in 3 epidemiological studies of cervical and anal precancers from Kaiser Permanente Northern California and the University of Oklahoma comprising 4253 patients. All statistical tests were 2-sided. Results In independent validation at Kaiser Permanente Northern California, artificial intelligence (AI)-based DS had lower positivity than cytology (P < .001) and manual DS (P < .001) with equal sensitivity and substantially higher specificity compared with both Pap (P < .001) and manual DS (P < .001), respectively. Compared with Pap, AI-based DS reduced referral to colposcopy by one-third (41.9% vs 60.1%, P < .001). At a higher cutoff, AI-based DS had similar performance to high-grade squamous intraepithelial lesions cytology, indicating a risk high enough to allow for immediate treatment. The classifier was robust, showing comparable performance in 2 cytology systems and in anal cytology. Conclusions Automated DS evaluation removes the remaining subjective component from cervical cancer screening and delivers consistent quality for providers and patients. Moving from Pap to automated DS substantially reduces the number of colposcopies and also achieves excellent performance in a simulated fully vaccinated population. Through cloud-based implementation, this approach is globally accessible. Our results demonstrate that AI not only provides automation and objectivity but also delivers a substantial benefit for women by reduction of unnecessary colposcopies.

CytoBrain: Cervical Cancer Screening System Based on Deep Learning Technology

2021 ◽  
Vol 36 (2) ◽  
pp. 347-360
Author(s):  
Hua Chen ◽  
Juan Liu ◽  
Qing-Man Wen ◽  
Zhi-Qun Zuo ◽  
Jia-Sheng Liu ◽  
...  
Keyword(s):  
Cervical Cancer ◽  
Deep Learning ◽  
Cancer Screening ◽  
Cervical Cancer Screening ◽  
Learning Technology ◽  
Screening System
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