Artificial Intelligence-driven Digital Cytology-based Cervical Cancer Screening: Is the Time Ripe to Adopt This Disruptive Technology in Resource-constrained Settings? A Literature Review

Gupta, Ruchika; Kumar, Neeta; Bansal, Shivani; Singh, Sompal; Sood, Neelam; Gupta, Sanjay

doi:10.1007/s10278-023-00821-0

Cited by 5 publications

(4 citation statements)

References 32 publications

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“…This fact was reflected by the decreased colposcopy referral rates and increased colposcopy efficiency of methylation triage to detect a CIN2+ case compared to cytology triage and HPV16/18 triage. Of note, thanks to the rapid development of artificial intelligence (AI), the accuracy of cytology is likely to be improved and become more cost-effective [ 39 ]. However, more real-world evidence is warranted to confirm its utility in comparison with various biomarker-based screening strategies.…”

Section: Discussionmentioning

confidence: 99%

“…careHPV test: careHPV is a nucleic acid hybridization assay with signal amplification using microplate chemiluminescence for the detection of 14 high-risk HPV types (16,18,31,33,35,39,45,51,52,56,58,59, 66, 68) in bulk. careHPV testing was carried out at the HPV laboratory of Tuoli Maternal and Child Health Hospital according to the standard protocol.…”

Section: Laboratory Testsmentioning

confidence: 99%

“…Type-specific primers target the L1 region of the HPV genome, whereas identification of amplicons is performed by reverse DNA hybridization using DNA chip technology. The test separately detects 24 HPV genotypes, including HPV6, 11,16,18,31,33,35,39,42,43,44,45,51,52,53,56,58,59, 66, 68, 73, 81, 82 and 83. In the present study, GenPlex ® HPV testing was performed according to the manufacturer's instructions using 0.5 mL from the third cervical specimen remnant after careHPV testing.…”

Section: Laboratory Testsmentioning

confidence: 99%

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Performance of Human Gene EPB41L3 and HPV 16/18 Viral DNA Methylation to Triage hrHPV-Positive Women

Rezhake,

Wang,

Zhao

et al. 2023

Vaccines

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More evidence from population-based cohort studies is required to confirm the application of methylation-based biomarkers in real-world settings. The cross-sectional and 24-month cumulative triage performance of a novel methylation assay targeting the host gene EPB41LE and HPV16/18 DNA L1/L2 regions among hrHPV-positive women was evaluated based on a population-based cohort study from China. Overall methylation positivity was 12.4% among hrHPV-positive women. Methylation-positive women had significantly higher risks of hrHPV persistence at 12M and 24M follow-up (RR12M = 1.9, 95%CI: 1.5–2.6 and RR24M = 1.7, 95%CI: 1.2–2.5). For CIN2+, cross-sectional triage sensitivity of methylation was similar to HPV16/18 (70.6% vs. 64.7%, pexact = 1.000), but was lower than cytology (94.1%), although not significantly (pexact = 0.213). The specificity (91.2%) of methylation was significantly higher than other triage methods (p < 0.001 for all). The longitudinal sensitivity of methylation over 24M follow-up was 56.0%, lower (but not significantly so) than HPV16/18 (64.0%, pexact = 0.688) and cytology (76.0%, pexact = 0.125). Methylation testing showed high positive predictive values for CIN2+ (41.4% at baseline, 50.0% at 24-month), while the CIN2+ risk of methylation negative women (cNPV) remained considerable (2.5% at baseline, 6.9% at 24-month). Study findings indicate that methylation has better specificity and predictive values for the presence or development of cervical precancer and might therefore be considered for the strategy of HPV screening and methylation triage followed by immediate treatment of triage-positive women and delayed follow-up of hrHPV-positive/methylation-negative women.

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Section: Discussionmentioning

confidence: 99%

Section: Laboratory Testsmentioning

confidence: 99%

Section: Laboratory Testsmentioning

confidence: 99%

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Performance of Human Gene EPB41L3 and HPV 16/18 Viral DNA Methylation to Triage hrHPV-Positive Women

Rezhake,

Wang,

Zhao

et al. 2023

Vaccines

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“…Emerging studies indicated the potential of artificial intelligence (AI) system for cervical cytology (12)(13)(14)(15)(16). For instance, Cheng et al applied a recurrent neural network-based whole slide image (WSI) classification model to achieve high specificity and sensitivity for slide classification (14).…”

Section: Introductionmentioning

confidence: 99%

AI-assisted system improves the work efficiency of cytologists via excluding cytology-negative slides and accelerating the slide interpretation

Du,

Dai,

Zhou

et al. 2023

Front. Oncol.

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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.

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A systematic review on deep learning based methods for cervical cell image analysis

Fang,

Liao,

Lei

et al. 2024

Neurocomputing

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Artificial Intelligence-driven Digital Cytology-based Cervical Cancer Screening: Is the Time Ripe to Adopt This Disruptive Technology in Resource-constrained Settings? A Literature Review

Cited by 5 publications

References 32 publications

Performance of Human Gene EPB41L3 and HPV 16/18 Viral DNA Methylation to Triage hrHPV-Positive Women

Performance of Human Gene EPB41L3 and HPV 16/18 Viral DNA Methylation to Triage hrHPV-Positive Women

AI-assisted system improves the work efficiency of cytologists via excluding cytology-negative slides and accelerating the slide interpretation

A systematic review on deep learning based methods for cervical cell image analysis

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