A Review of Computer-Aided Diagnostic Algorithms for Cervical Neoplasia and an Assessment of Their Applicability to Female Genital Schistosomiasis
Female genital schistosomiasis (FGS) affects an estimated 56 million women and girls in Africa. Nevertheless, this neglected tropical disease remains largely understudied and underdiagnosed. In this literature review, we examine the effectiveness of published computer-aided diagnostic (CAD) algorith...
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| Published in | Mayo Clinic Proceedings. Digital health Vol. 1; no. 3; pp. 247 - 257 |
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| Main Authors | , , |
| Format | Journal Article |
| Language | English |
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Elsevier Inc
01.09.2023
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| Online Access | Get full text |
| ISSN | 2949-7612 2949-7612 |
| DOI | 10.1016/j.mcpdig.2023.04.007 |
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| Abstract | Female genital schistosomiasis (FGS) affects an estimated 56 million women and girls in Africa. Nevertheless, this neglected tropical disease remains largely understudied and underdiagnosed. In this literature review, we examine the effectiveness of published computer-aided diagnostic (CAD) algorithms for cervical cancer that use colposcopy images and assess their applicability to the design of an automated image diagnostic algorithm for FGS. We searched 2 databases (Embase and MEDLINE) from database inception to June 10, 2022. We identified 393 studies, of which 13 were relevant for FGS diagnosis. These 13 studies were analyzed for their key image analysis model components and compared with the features that would be beneficial in an FGS diagnostic image analysis system. |
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| AbstractList | Female genital schistosomiasis (FGS) affects an estimated 56 million women and girls in Africa. Nevertheless, this neglected tropical disease remains largely understudied and underdiagnosed. In this literature review, we examine the effectiveness of published computer-aided diagnostic (CAD) algorithms for cervical cancer that use colposcopy images and assess their applicability to the design of an automated image diagnostic algorithm for FGS. We searched 2 databases (Embase and MEDLINE) from database inception to June 10, 2022. We identified 393 studies, of which 13 were relevant for FGS diagnosis. These 13 studies were analyzed for their key image analysis model components and compared with the features that would be beneficial in an FGS diagnostic image analysis system. Female genital schistosomiasis (FGS) affects an estimated 56 million women and girls in Africa. Nevertheless, this neglected tropical disease remains largely understudied and underdiagnosed. In this literature review, we examine the effectiveness of published computer-aided diagnostic (CAD) algorithms for cervical cancer that use colposcopy images and assess their applicability to the design of an automated image diagnostic algorithm for FGS. We searched 2 databases (Embase and MEDLINE) from database inception to June 10, 2022. We identified 393 studies, of which 13 were relevant for FGS diagnosis. These 13 studies were analyzed for their key image analysis model components and compared with the features that would be beneficial in an FGS diagnostic image analysis system.Female genital schistosomiasis (FGS) affects an estimated 56 million women and girls in Africa. Nevertheless, this neglected tropical disease remains largely understudied and underdiagnosed. In this literature review, we examine the effectiveness of published computer-aided diagnostic (CAD) algorithms for cervical cancer that use colposcopy images and assess their applicability to the design of an automated image diagnostic algorithm for FGS. We searched 2 databases (Embase and MEDLINE) from database inception to June 10, 2022. We identified 393 studies, of which 13 were relevant for FGS diagnosis. These 13 studies were analyzed for their key image analysis model components and compared with the features that would be beneficial in an FGS diagnostic image analysis system. |
| Author | Jin, Emily Gomes, Mireille Noble, J. Alison |
| Author_xml | – sequence: 1 givenname: Emily surname: Jin fullname: Jin, Emily email: emily.jin@cs.ox.ac.uk organization: Department of Computer Science, University of Oxford, United Kingdom – sequence: 2 givenname: J. Alison surname: Noble fullname: Noble, J. Alison organization: Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, United Kingdom – sequence: 3 givenname: Mireille surname: Gomes fullname: Gomes, Mireille organization: Global Health Institute of Merck, Ares Trading S.A., an affiliate of Merck KGaA, Darmstadt, Germany |
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| Cites_doi | 10.1016/j.bspc.2019.101785 10.1016/j.pt.2011.10.008 10.1093/jnci/djy225 10.1002/ijc.33029 10.1016/S0090-8258(03)00191-4 10.1002/ijc.23489 10.1371/journal.pntd.0010276 10.1109/JBHI.2021.3094311 10.1007/s10278-018-0083-x 10.1371/journal.pone.0047570 10.2196/16467 10.1016/j.bspc.2019.101566 10.4269/ajtmh.2009.09-0081 10.21037/atm-21-885 10.1186/s12879-021-06380-5 10.1109/CVPR.2018.00745 10.1002/ijgo.13538 10.1371/journal.pntd.0008337 10.1371/journal.pntd.0004628 10.1093/infdis/jiv035 10.31557/APJCP.2018.19.12.3571 10.1016/j.medengphy.2014.12.007 10.1016/j.media.2021.102006 10.1038/nrc2462 10.1016/j.patcog.2016.09.027 |
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| Title | A Review of Computer-Aided Diagnostic Algorithms for Cervical Neoplasia and an Assessment of Their Applicability to Female Genital Schistosomiasis |
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