AI-assisted system improves the work efficiency of cytologists via excluding cytology-negative slides and accelerating the slide interpretation
Given the shortage of cytologists, women in low-resource regions had inequitable access to cervical cytology which plays an pivotal role in cervical cancer screening. Emerging studies indicated the potential of AI-assisted system in promoting the implementation of cytology in resource-limited settin...
| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2023-11-01
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| Series: | Frontiers in Oncology |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fonc.2023.1290112/full |
| _version_ | 1797501457359962112 |
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| author | Hui Du Hui Du Hui Du Wenkui Dai Wenkui Dai Wenkui Dai Qian Zhou Changzhong Li Shuai Cheng Li Chun Wang Chun Wang Chun Wang Jinlong Tang Jinlong Tang Jinlong Tang Xiangchen Wu Ruifang Wu Ruifang Wu Ruifang Wu |
| author_facet | Hui Du Hui Du Hui Du Wenkui Dai Wenkui Dai Wenkui Dai Qian Zhou Changzhong Li Shuai Cheng Li Chun Wang Chun Wang Chun Wang Jinlong Tang Jinlong Tang Jinlong Tang Xiangchen Wu Ruifang Wu Ruifang Wu Ruifang Wu |
| author_sort | Hui Du |
| collection | DOAJ |
| description | Given the shortage of cytologists, women in low-resource regions had inequitable access to cervical cytology which plays an pivotal role in cervical cancer screening. Emerging studies indicated the potential of AI-assisted system in promoting the implementation of cytology in resource-limited settings. However, there is a deficiency in evaluating the aid of AI in the improvement of cytologists’ work efficiency. This study aimed to evaluate the feasibility of AI in excluding cytology-negative slides and improve the efficiency of slide interpretation. Well-annotated slides were included to develop the classification model that was applied to classify slides in the validation group. Nearly 70% of validation slides were reported as negative by the AI system, and none of these slides were diagnosed as high-grade lesions by expert cytologists. With the aid of AI system, the average of interpretation time for each slide decreased from 3 minutes to 30 seconds. These findings suggested the potential of AI-assisted system in accelerating slide interpretation in the large-scale cervical cancer screening. |
| first_indexed | 2024-03-10T03:18:41Z |
| format | Article |
| id | doaj.art-b1d9a6eacd0a460b8545ff77be67e817 |
| institution | Directory Open Access Journal |
| issn | 2234-943X |
| language | English |
| last_indexed | 2024-03-10T03:18:41Z |
| publishDate | 2023-11-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Oncology |
| spelling | doaj.art-b1d9a6eacd0a460b8545ff77be67e8172023-11-23T10:08:43ZengFrontiers Media S.A.Frontiers in Oncology2234-943X2023-11-011310.3389/fonc.2023.12901121290112AI-assisted system improves the work efficiency of cytologists via excluding cytology-negative slides and accelerating the slide interpretationHui Du0Hui Du1Hui Du2Wenkui Dai3Wenkui Dai4Wenkui Dai5Qian Zhou6Changzhong Li7Shuai Cheng Li8Chun Wang9Chun Wang10Chun Wang11Jinlong Tang12Jinlong Tang13Jinlong Tang14Xiangchen Wu15Ruifang Wu16Ruifang Wu17Ruifang Wu18Department of Obstetrics and Gynecology, Peking University Shenzhen Hospital, Shenzhen, ChinaInstitute of Obstetrics and Gynecology, Shenzhen Peking University-The Hong Kong University of Science and Technology (PKU-HKUST) Medical Center, Shenzhen, ChinaShenzhen Key Laboratory on Technology for Early Diagnosis of Major Gynecologic Diseases, Shenzhen, ChinaDepartment of Obstetrics and Gynecology, Peking University Shenzhen Hospital, Shenzhen, ChinaInstitute of Obstetrics and Gynecology, Shenzhen Peking University-The Hong Kong University of Science and Technology (PKU-HKUST) Medical Center, Shenzhen, ChinaShenzhen Key Laboratory on Technology for Early Diagnosis of Major Gynecologic Diseases, Shenzhen, ChinaDepartment of Computer Science, City University of Hong Kong, Hong Kong, Hong Kong SAR, ChinaSuzhou Ruiqian Technology Company Ltd., Suzhou, ChinaDepartment of Computer Science, City University of Hong Kong, Hong Kong, Hong Kong SAR, ChinaDepartment of Obstetrics and Gynecology, Peking University Shenzhen Hospital, Shenzhen, ChinaInstitute of Obstetrics and Gynecology, Shenzhen Peking University-The Hong Kong University of Science and Technology (PKU-HKUST) Medical Center, Shenzhen, ChinaShenzhen Key Laboratory on Technology for Early Diagnosis of Major Gynecologic Diseases, Shenzhen, ChinaDepartment of Obstetrics and Gynecology, Peking University Shenzhen Hospital, Shenzhen, ChinaInstitute of Obstetrics and Gynecology, Shenzhen Peking University-The Hong Kong University of Science and Technology (PKU-HKUST) Medical Center, Shenzhen, ChinaShenzhen Key Laboratory on Technology for Early Diagnosis of Major Gynecologic Diseases, Shenzhen, ChinaSuzhou Ruiqian Technology Company Ltd., Suzhou, ChinaDepartment of Obstetrics and Gynecology, Peking University Shenzhen Hospital, Shenzhen, ChinaInstitute of Obstetrics and Gynecology, Shenzhen Peking University-The Hong Kong University of Science and Technology (PKU-HKUST) Medical Center, Shenzhen, ChinaShenzhen Key Laboratory on Technology for Early Diagnosis of Major Gynecologic Diseases, Shenzhen, ChinaGiven the shortage of cytologists, women in low-resource regions had inequitable access to cervical cytology which plays an pivotal role in cervical cancer screening. Emerging studies indicated the potential of AI-assisted system in promoting the implementation of cytology in resource-limited settings. However, there is a deficiency in evaluating the aid of AI in the improvement of cytologists’ work efficiency. This study aimed to evaluate the feasibility of AI in excluding cytology-negative slides and improve the efficiency of slide interpretation. Well-annotated slides were included to develop the classification model that was applied to classify slides in the validation group. Nearly 70% of validation slides were reported as negative by the AI system, and none of these slides were diagnosed as high-grade lesions by expert cytologists. With the aid of AI system, the average of interpretation time for each slide decreased from 3 minutes to 30 seconds. These findings suggested the potential of AI-assisted system in accelerating slide interpretation in the large-scale cervical cancer screening.https://www.frontiersin.org/articles/10.3389/fonc.2023.1290112/fullHPVcervical cancer screeningartificial intelligenceslide interpretationlow-resource areas |
| spellingShingle | Hui Du Hui Du Hui Du Wenkui Dai Wenkui Dai Wenkui Dai Qian Zhou Changzhong Li Shuai Cheng Li Chun Wang Chun Wang Chun Wang Jinlong Tang Jinlong Tang Jinlong Tang Xiangchen Wu Ruifang Wu Ruifang Wu Ruifang Wu AI-assisted system improves the work efficiency of cytologists via excluding cytology-negative slides and accelerating the slide interpretation Frontiers in Oncology HPV cervical cancer screening artificial intelligence slide interpretation low-resource areas |
| title | AI-assisted system improves the work efficiency of cytologists via excluding cytology-negative slides and accelerating the slide interpretation |
| title_full | AI-assisted system improves the work efficiency of cytologists via excluding cytology-negative slides and accelerating the slide interpretation |
| title_fullStr | AI-assisted system improves the work efficiency of cytologists via excluding cytology-negative slides and accelerating the slide interpretation |
| title_full_unstemmed | AI-assisted system improves the work efficiency of cytologists via excluding cytology-negative slides and accelerating the slide interpretation |
| title_short | AI-assisted system improves the work efficiency of cytologists via excluding cytology-negative slides and accelerating the slide interpretation |
| title_sort | ai assisted system improves the work efficiency of cytologists via excluding cytology negative slides and accelerating the slide interpretation |
| topic | HPV cervical cancer screening artificial intelligence slide interpretation low-resource areas |
| url | https://www.frontiersin.org/articles/10.3389/fonc.2023.1290112/full |
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