ArticleAmerican journal of translational research2024
Analysis of effectiveness in an artificial intelligent film reading system combined with liquid based cytology examination for cervical cancer screening.
Article in American journal of translational research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
5 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A bibliometric analysis and visualization of trends in cervical cancer screening technologies based on dual-database analysis.Frontiers in oncology · 2026Pooled it
- Correlation between atypical squamous cells-cannot exclude high-grade squamous intraepithelial lesion and cervical lesions: a retrospective analysis of opportunistic and organized cervical screening populations.Frontiers in oncology · 2026Article
- AI in Cervical Cancer Cytology Diagnostics: A Narrative Review of Cutting-Edge Studies.Bioengineering (Basel, Switzerland) · 2025Review
- Recent advances in applications of machine learning in cervical cancer research: a focus on prediction models.Obstetrics & gynecology science · 2025Article
- Advances in artificial intelligence for the early detection of cervical cancer in adult women: a scoping review.Revista brasileira de ginecologia e obstetricia : revista da Federacao Brasileira das Sociedades de Ginecologia e Obstetricia · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveTo explore the effectiveness of combining an artificial intelligence (AI) film reading system with a cervical liquid-based ThinPrep cytology test (TCT) in cervical cancer screening.
methodsA total of 1200 adult women who underwent cervical cancer screening in the Gynecology Department of The Fifth People's Hospital of Jinan from July 2022 to June 2023 were included in the study. All participants underwent TCT followed by both manual and AI examination. The AI examination was performed using an AI film reading system that employed advanced machine learning algorithms and image processing techniques to analyze digital TCT slides. Pathological tissue biopsy was performed on all cases with abnormalities, and the results were used as the gold standard to analyze the effectiveness of the different screening methods.
resultsTCT screening results revealed that the average time for manual film reading was shorter than that for the AI film reading system (P<0.001). The AI film reading system significantly detected more lesions than the manual film reading method (P<0.001). The overall compliance rate between AI imaging and manual imaging interpretation was 79.75%, with a corresponding Kappa value of 0.588, indicating moderate agreement between the two methods. The accuracy of the AI screening system for low-grade lesions and inflammation was 87.47%, compared to 79.41% for manual screening (P=0.018). For high-grade cancer lesions, the accuracy rates were 82.54% for AI and 75.90% for manual screening (P=0.241). The AI screening system had a sensitivity of 67.53% (104/154) for detecting high-grade lesions and cancers, higher than the 40.91% (63/154) sensitivity of manual screening. However, the specificity of the AI screening system was 94.07% (349/371), while manual screening had a specificity of 94.61% (351/371). The Youden index for AI screening system was 0.616, significantly higher than the 0.355 for manual screening.
conclusionIn TCT screening, the AI screening system outperforms manual screening. The combination of the AI film reading system and TCT may hold significant value in cervical cancer screening, as well as in the early diagnosis and treatment of the disease.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.