Meta AnalysisID 899
基于机器学习的醋酸肉眼观察宫颈图像筛查宫颈癌：系统评价
CRD42021270745
Is machine-learning based screening during visual inspection with acetic acid for cervical cancer in women as effective as histology in identifying women with precancerous and cancerous lesions (CIN2+)?
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Record Fields
Scalar fields from the final meta_analysis record.
- Meta Analysis Id
- 899
- Evidence Id
- 9457
- Core Evidence Id
- 9457
- Source Meta Analysis Id
- 873
- Herb2 Meta Analysis Id
- HBMA000873
- Crd Id
- CRD42021270745
- Title
- Machine learning-based cervical cancer screening using cervigrams during visual inspection with acetic acid: a systematic review
- Review Question
- Is machine-learning based screening during visual inspection with acetic acid for cervical cancer in women as effective as histology in identifying women with precancerous and cancerous lesions (CIN2+)?
- Study Type Included
- There are no restrictions on the types of study design.
- Condition Being Studied
- Cervical cancer | Each year, around 266, 000 women die of cervical cancer and this number is projected to reach 416, 000 by 2035. More than 85% of these deaths occur in low- and medium-income countries where availability of trained healthcare providers and access to expensive screening devices are limited. However, most of these deaths could be avoided with wider access to early detection methods such as computer-aided diagnosis tools.
- Participant
- Women either negative (normal/CIN1) or positive (CIN2+)
- Animal
- Human Disease Modelled
- Intervention
- Machine learning algorithms for cervical cancer screening based on cervigrams taken during visual inspection with acetic acid
- Comparator Control
- Not applicable
- Main Outcome
- The objective of the study is to evaluate the accuracy of machine learning algorithms for the detection of histologically confirmed cervical precancer and cancer. Measures of effect Screening test accuracy: sensitivity and specificity
- Outcome Measure
- Additional Outcome
- (1) Comparison of machine learning algorithms for cervical cancer diagnosis considering histopathology results as gold standard (2) Feasibility of application in low- and middle-income settings
- Study Method
- Diagnostic, Systematic review
- Keyword
- Acetates; Cervical Intraepithelial Neoplasia; Early Detection of Cancer; Female; Humans; Machine Learning; Uterine Cervical Neoplasms
- Contact
- Organisational Affiliation
- Funding Source
- Other Selection Criteria
- Final Publication
- Same Topic Review
- Published Protocol
- Review Type
- Language
- English
- Country
- Review Stage
- First Submission Date
- Registration Date
- Anticipated Start Date
- Anticipated Completion Date
- Title Cn
- 基于机器学习的醋酸肉眼观察宫颈图像筛查宫颈癌：系统评价
- Title En
- Machine learning-based cervical cancer screening using cervigrams during visual inspection with acetic acid: a systematic review
- Bilingual Status
- complete