ArticleNature communications2021
Hybrid AI-assistive diagnostic model permits rapid TBS classification of cervical liquid-based thin-layer cell smears.
Article in Nature communications, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 48 papers, 2 of them syntheses 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
48 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Advancements in automated nuclei segmentation for histopathology using you only look once-driven approaches: A systematic review.Computers in biology and medicine · 2025Pooled it
- Artificial intelligence in cytopathological applications for cancer: a review of accuracy and analytic validity.European journal of medical research · 2024Pooled it
- Artificial Intelligence in Cervical Cytology: Opportunities and Limitations in Screening, Triage, and Diagnostic Support.Diagnostics (Basel, Switzerland) · 2026Review
- Deep learning-assisted versus manual reading in routine cervical cytopathology: a multicentre randomised crossover trial.NPJ digital medicine · 2026Article
- Robust Cell-Level Classification for Liquid-Based Cervical Cytology Using Deep Transfer Learning: A Multi-Source Study Addressing Scanner-Induced Domain Shifts.Bioengineering (Basel, Switzerland) · 2026Article
- The diagnostic value of colposcopy in postmenopausal women: a retrospective study.BMC women's health · 2026Article
- UniCAS: A foundation model for cervical cytology screening.Cell reports. Medicine · 2026Article
- Pre-analytical reporting in AI-assisted cervical cytology: a scoping review of data acquisition documentation.Frontiers in artificial intelligence · 2026Review
- Cytological Classification Diagnosis for Thyroid Nodules via Multimodal Model Deep Learning.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Assessment of the efficiency and accuracy of an artificial intelligence assistive system in the diagnosis of Pap cervical atypical glandular cell cytology.Cancer cytopathology · 2025Article
- AI-assisted cervical cytology precancerous screening for high-risk population in resource-limited regions using a compact microscope.Nature communications · 2025Article
- Progress in the application research of cervical cancer screening developed by artificial intelligence in large populations.Discover oncology · 2025Review
- Assessment of the efficacy and accuracy of cervical cytology screening with the Hologic Genius Digital Diagnostics System.Cancer cytopathology · 2025Article
- Machine and Deep Learning for the Diagnosis, Prognosis, and Treatment of Cervical Cancer: A Scoping Review.Diagnostics (Basel, Switzerland) · 2025Review
- Single-Cell Sequencing-Guided Annotation of Rare Tumor Cells for Deep Learning-Based Cytopathologic Diagnosis of Early Lung Cancer.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Evaluating the performance of large language & visual-language models in cervical cytology screening.NPJ precision oncology · 2025Article
- StereoMM: a graph fusion model for integrating spatial transcriptomic data and pathological images.Briefings in bioinformatics · 2025Article
- Deep learning enabled liquid-based cytology model for cervical precancer and cancer detection.Nature communications · 2025Article
- Performance of artificial intelligence for diagnosing cervical intraepithelial neoplasia and cervical cancer: a systematic review and meta-analysis.EClinicalMedicine · 2025Article
- The promise of PD1/PDL1 targeted immunotherapy in locally advanced cervical cancer: a game-changer for patients outcome?Frontiers in immunology · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
34 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Technical advancements significantly improve earlier diagnosis of cervical cancer, but accurate diagnosis is still difficult due to various factors. We develop an artificial intelligence assistive diagnostic solution, AIATBS, to improve cervical liquid-based thin-layer cell smear diagnosis according to clinical TBS criteria. We train AIATBS with >81,000 retrospective samples. It integrates YOLOv3 for target detection, Xception and Patch-based models to boost target classification, and U-net for nucleus segmentation. We integrate XGBoost and a logical decision tree with these models to optimize the parameters given by the learning process, and we develop a complete cervical liquid-based cytology smear TBS diagnostic system which also includes a quality control solution. We validate the optimized system with >34,000 multicenter prospective samples and achieve better sensitivity compared to senior cytologists, yet retain high specificity while achieving a speed of <180s/slide. Our system is adaptive to sample preparation using different standards, staining protocols and scanners.
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.