This paper is in the following e-collection/theme issue:

mHealth in the Developing World/LMICs, Underserved Communities, and for Global Health (578) Machine Learning (1501) mHealth for Wellness, Behavior Change and Prevention (2922) mHealth in a Clinical Setting (624) Innovations and Technology for Cancer Prevention and Screening (150)

Published on in Vol 8, No 3 (2020): March

Quantitative Screening of Cervical Cancers for Low-Resource Settings: Pilot Study of Smartphone-Based Endoscopic Visual Inspection After Acetic Acid Using Machine Learning Techniques

Quantitative Screening of Cervical Cancers for Low-Resource Settings: Pilot Study of Smartphone-Based Endoscopic Visual Inspection After Acetic Acid Using Machine Learning Techniques

Quantitative Screening of Cervical Cancers for Low-Resource Settings: Pilot Study of Smartphone-Based Endoscopic Visual Inspection After Acetic Acid Using Machine Learning Techniques

Authors of this article:

Jung Kweon Bae1 Author Orcid Image ;   Hyun-Jin Roh2 Author Orcid Image ;   Joon S You1 Author Orcid Image ;   Kyungbin Kim3 Author Orcid Image ;   Yujin Ahn1 Author Orcid Image ;   Sanzhar Askaruly1 Author Orcid Image ;   Kibeom Park1 Author Orcid Image ;   Hyunmo Yang1 Author Orcid Image ;   Gil-Jin Jang4 Author Orcid Image ;   Kyung Hyun Moon5 Author Orcid Image ;   Woonggyu Jung1 Author Orcid Image
Article Authors Cited by (18) Tweetations (5) Metrics

Journals

  1. ALLANSON E, SCHMELER K. Cervical Cancer Prevention in Low- and Middle-Income Countries. Clinical Obstetrics & Gynecology 2021;64(3):501 View
  2. Sami J, Lemoupa Makajio S, Jeannot E, Kenfack B, Viñals R, Vassilakos P, Petignat P. Smartphone-Based Visual Inspection with Acetic Acid: An Innovative Tool to Improve Cervical Cancer Screening in Low-Resource Setting. Healthcare 2022;10(2):391 View
  3. Kakotkin V, Semina E, Zadorkina T, Agapov M. Prevention Strategies and Early Diagnosis of Cervical Cancer: Current State and Prospects. Diagnostics 2023;13(4):610 View
  4. Coole J, Brenes D, Possati-Resende J, Antoniazzi M, Fonseca B, Maker Y, Kortum A, Vohra I, Schwarz R, Carns J, Borba Souza K, Vidigal Santana I, Kreitchmann R, Salcedo M, Ramanujam N, Schmeler K, Richards-Kortum R. Development of a multimodal mobile colposcope for real-time cervical cancer detection. Biomedical Optics Express 2022;13(10):5116 View
  5. Castor D, Saidu R, Boa R, Mbatani N, Mutsvangwa T, Moodley J, Denny L, Kuhn L. Assessment of the implementation context in preparation for a clinical study of machine-learning algorithms to automate the classification of digital cervical images for cervical cancer screening in resource-constrained settings. Frontiers in Health Services 2022;2 View
  6. Allanson E, Phoolcharoen N, Salcedo M, Fellman B, Schmeler K. Accuracy of Smartphone Images of the Cervix After Acetic Acid Application for Diagnosing Cervical Intraepithelial Neoplasia Grade 2 or Greater in Women With Positive Cervical Screening: A Systematic Review and Meta-Analysis. JCO Global Oncology 2021;(7):1711 View
  7. Yates D, Islam M. Data Mining on Smartphones: An Introduction and Survey. ACM Computing Surveys 2023;55(5):1 View
  8. Cavalcanti T, Lew H, Lee K, Lee S, Park M, Hwang J. Intelligent smartphone-based multimode imaging otoscope for the mobile diagnosis of otitis media. Biomedical Optics Express 2021;12(12):7765 View
  9. Istasy P, Lee W, Iansavichene A, Upshur R, Gyawali B, Burkell J, Sadikovic B, Lazo-Langner A, Chin-Yee B. The Impact of Artificial Intelligence on Health Equity in Oncology: Scoping Review. Journal of Medical Internet Research 2022;24(11):e39748 View
  10. Harsono A, Susiarno H, Suardi D, Owen L, Fauzi H, Kireina J, Wahid R, Carolina J, Mantilidewi K, Hidayat Y. Cervical pre-cancerous lesion detection: development of smartphone-based VIA application using artificial intelligence. BMC Research Notes 2022;15(1) View
  11. Hou H, Mitbander R, Tang Y, Azimuddin A, Carns J, Schwarz R, Richards-Kortum R. Optical imaging technologies for in vivo cancer detection in low-resource settings. Current Opinion in Biomedical Engineering 2023;28:100495 View
  12. Li Z, Zeng C, Dong Y, Cao Y, Yu L, Liu H, Tian X, Tian R, Zhong C, Zhao T, Liu J, Chen Y, Li L, Huang Z, Wang Y, Hu Z, Zhang J, Liang J, Zhou P, Lu Y. A segmentation model to detect cevical lesions based on machine learning of colposcopic images. Heliyon 2023;9(11):e21043 View
  13. Jin E, Noble J, Gomes M. A Review of Computer-Aided Diagnostic Algorithms for Cervical Neoplasia and an Assessment of Their Applicability to Female Genital Schistosomiasis. Mayo Clinic Proceedings: Digital Health 2023;1(3):247 View
  14. Vargas‐Cardona H, Rodriguez‐Lopez M, Arrivillaga M, Vergara‐Sanchez C, García‐Cifuentes J, Bermúdez P, Jaramillo‐Botero A. Artificial intelligence for cervical cancer screening: Scoping review, 2009–2022. International Journal of Gynecology & Obstetrics 2024;165(2):566 View
  15. Chen P, Liu F, Zhang J, Wang B. MFEM-CIN: A Lightweight Architecture Combining CNN and Transformer for the Classification of Pre-Cancerous Lesions of the Cervix. IEEE Open Journal of Engineering in Medicine and Biology 2024;5:216 View
  16. Le L, Nguyen A, Phan L, Ngo H, Wang X, Cunningham B, Valera E, Bashir R, Taylor-Robinson A, Do C. Current smartphone-assisted point-of-care cancer detection: Towards supporting personalized cancer monitoring. TrAC Trends in Analytical Chemistry 2024;174:117681 View