Development and validation of artificial intelligence-based analysis software to support screening system of cervical intraepithelial neoplasia
Abstract Cervical cancer, the fourth most common cancer among women worldwide, often proves fatal and stems from precursor lesions caused by high-risk human papillomavirus (HR-HPV) infection. Accurate and early diagnosis is crucial for effective treatment. Current screening methods, such as the Pap...
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2024-01-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-024-51880-4 |
| _version_ | 1797276620929630208 |
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| author | Yung-Taek Ouh Tae Jin Kim Woong Ju Sang Wun Kim Seob Jeon Soo-Nyung Kim Kwang Gi Kim Jae-Kwan Lee |
| author_facet | Yung-Taek Ouh Tae Jin Kim Woong Ju Sang Wun Kim Seob Jeon Soo-Nyung Kim Kwang Gi Kim Jae-Kwan Lee |
| author_sort | Yung-Taek Ouh |
| collection | DOAJ |
| description | Abstract Cervical cancer, the fourth most common cancer among women worldwide, often proves fatal and stems from precursor lesions caused by high-risk human papillomavirus (HR-HPV) infection. Accurate and early diagnosis is crucial for effective treatment. Current screening methods, such as the Pap test, liquid-based cytology (LBC), visual inspection with acetic acid (VIA), and HPV DNA testing, have limitations, requiring confirmation through colposcopy. This study introduces CerviCARE AI, an artificial intelligence (AI) analysis software, to address colposcopy challenges. It automatically analyzes Tele-cervicography images, distinguishing between low-grade and high-grade lesions. In a multicenter retrospective study, CerviCARE AI achieved a remarkable sensitivity of 98% for high-risk groups (P2, P3, HSIL or higher, CIN2 or higher) and a specificity of 95.5%. These findings underscore CerviCARE AI's potential as a valuable diagnostic tool for highly accurate identification of cervical precancerous lesions. While further prospective research is needed to validate its clinical utility, this AI system holds promise for improving cervical cancer screening and lessening the burden of this deadly disease. |
| first_indexed | 2024-03-07T15:30:51Z |
| format | Article |
| id | doaj.art-91fed0e76252467c91c8f136224ce5af |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-03-07T15:30:51Z |
| publishDate | 2024-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-91fed0e76252467c91c8f136224ce5af2024-03-05T16:27:26ZengNature PortfolioScientific Reports2045-23222024-01-0114111010.1038/s41598-024-51880-4Development and validation of artificial intelligence-based analysis software to support screening system of cervical intraepithelial neoplasiaYung-Taek Ouh0Tae Jin Kim1Woong Ju2Sang Wun Kim3Seob Jeon4Soo-Nyung Kim5Kwang Gi Kim6Jae-Kwan Lee7Department of Obstetrics and Gynecology, Korea University Ansan HospitalDepartment of Obstetrics and Gynecology, Konkuk University School of MedicineDepartment of Obstetrics and Gynecology, Ewha Womans University Seoul HospitalDepartment of Obstetrics and Gynecology, Institute of Women’s Life Medical Science, Yonsei University College of MedicineDepartment of Obstetrics and Gynecology, College of Medicine, Soonchunhyang University Cheonan HospitalR&D Center, NTL Medical InstituteDepartment of Biomedical Engineering, Gachon University College of Medicine, Gil Medical CenterDepartment of Obstetrics and Gynecology, Korea University Guro HospitalAbstract Cervical cancer, the fourth most common cancer among women worldwide, often proves fatal and stems from precursor lesions caused by high-risk human papillomavirus (HR-HPV) infection. Accurate and early diagnosis is crucial for effective treatment. Current screening methods, such as the Pap test, liquid-based cytology (LBC), visual inspection with acetic acid (VIA), and HPV DNA testing, have limitations, requiring confirmation through colposcopy. This study introduces CerviCARE AI, an artificial intelligence (AI) analysis software, to address colposcopy challenges. It automatically analyzes Tele-cervicography images, distinguishing between low-grade and high-grade lesions. In a multicenter retrospective study, CerviCARE AI achieved a remarkable sensitivity of 98% for high-risk groups (P2, P3, HSIL or higher, CIN2 or higher) and a specificity of 95.5%. These findings underscore CerviCARE AI's potential as a valuable diagnostic tool for highly accurate identification of cervical precancerous lesions. While further prospective research is needed to validate its clinical utility, this AI system holds promise for improving cervical cancer screening and lessening the burden of this deadly disease.https://doi.org/10.1038/s41598-024-51880-4 |
| spellingShingle | Yung-Taek Ouh Tae Jin Kim Woong Ju Sang Wun Kim Seob Jeon Soo-Nyung Kim Kwang Gi Kim Jae-Kwan Lee Development and validation of artificial intelligence-based analysis software to support screening system of cervical intraepithelial neoplasia Scientific Reports |
| title | Development and validation of artificial intelligence-based analysis software to support screening system of cervical intraepithelial neoplasia |
| title_full | Development and validation of artificial intelligence-based analysis software to support screening system of cervical intraepithelial neoplasia |
| title_fullStr | Development and validation of artificial intelligence-based analysis software to support screening system of cervical intraepithelial neoplasia |
| title_full_unstemmed | Development and validation of artificial intelligence-based analysis software to support screening system of cervical intraepithelial neoplasia |
| title_short | Development and validation of artificial intelligence-based analysis software to support screening system of cervical intraepithelial neoplasia |
| title_sort | development and validation of artificial intelligence based analysis software to support screening system of cervical intraepithelial neoplasia |
| url | https://doi.org/10.1038/s41598-024-51880-4 |
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