ReviewBiomedical engineering and computational biology2016
Artificial Neural Networks as Decision Support Tools in Cytopathology: Past, Present, and Future.
Review in Biomedical engineering and computational biology, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
What it found
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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.
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Who cites it
24 citing papers in PubMed.
- Predicting Pre- and Post-Diagnostic Depression in Women with Abnormal Pap Screening Tests: A Neural Network Approach.Life (Basel, Switzerland) · 2025Article
- A low-cost platform for automated cervical cytology: addressing health and socioeconomic challenges in low-resource settings.Frontiers in medical technology · 2025Article
- Revisiting the utility of identifying nuclear grooves as unique nuclear changes by an object detector model.Journal of pathology and translational medicine · 2024Article
- A brief review of cytology in dentistry.British dental journal · 2024Review
- Detecting the impact of diagnostic procedures in Pap-positive women on anxiety using artificial neural networks.PloS one · 2024Article
- Large-scale validation study of an improved semiautonomous urine cytology assessment tool: AutoParis-X.Cancer cytopathology · 2023Article
- Current status of machine learning in thyroid cytopathology.Journal of pathology informatics · 2023Review
- Trends in AI-powered Classification of Thyroid Neoplasms Based on Histopathology Images - a Systematic Review.Acta informatica medica : AIM : journal of the Society for Medical Informatics of Bosnia & Herzegovina : casopis Drustva za medicinsku informatiku BiH · 2023Article
- Endometrial cancer in women with abnormal uterine bleeding: Data mining classification methods.Caspian journal of internal medicine · 2023Article
- BestCyte® primary screening of 500 ThinPrep Pap Test thin-layers: 3 Cytologists' Interobserver diagnostic concordance with predicate manual microscopy relative to Truth Reference diagnoses defining NILM, ASCUS+, LSIL+, and ASCH+ thresholds for specificity, sensitivity, and equivalency grading.Journal of pathology informatics · 2023Article
- Two Needle Passes Achieve Similar Diagnostic Yield Compared to Three Passes Regarding Diagnosis of Solid Pancreatic Lesions in Endoscopic Ultrasound-Guided Fine Needle Aspiration.Diagnostics (Basel, Switzerland) · 2021Article
- Effectiveness of convolutional neural networks in the interpretation of pulmonary cytologic images in endobronchial ultrasound procedures.Cancer medicine · 2021Article
- Advances in Imaging Modalities, Artificial Intelligence, and Single Cell Biomarker Analysis, and Their Applications in Cytopathology.Frontiers in medicine · 2021Review
- A Calibrated Multiexit Neural Network for Detecting Urothelial Cancer Cells.Computational and mathematical methods in medicine · 2021Article
- The Utility of Deep Learning in Breast Ultrasonic Imaging: A Review.Diagnostics (Basel, Switzerland) · 2020Review
- Artificial intelligence and machine learning in nephropathology.Kidney international · 2020Review
- Radial Basis Function Artificial Neural Network for the Investigation of Thyroid Cytological Lesions.Journal of thyroid research · 2020Article
- Progress of Artificial Intelligence in Gynecological Malignant Tumors.Cancer management and research · 2020Review
- Accuracy of EUS-FNA in Solid Pancreatic Lesions: Sometimes Size Does Matter.Digestive diseases and sciences · 2019Article
- Factors associated withTranslational cancer research · 2019Article
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveThis study aims to analyze the role of artificial neural networks (ANNs) in cytopathology. More specifically, it aims to highlight the importance of employing ANNs in existing and future applications and in identifying unexplored or poorly explored research topics. STUDY
designA systematic search was conducted in scientific databases for articles related to cytopathology and ANNs with respect to anatomical places of the human body where cytopathology is performed. For each anatomic system/organ, the major outcomes described in the scientific literature are presented and the most important aspects are highlighted.
resultsThe vast majority of ANN applications are related to cervical cytopathology, specifically for the ANN-based, semiautomated commercial diagnostic system PAPNET. For cervical cytopathology, there is a plethora of studies relevant to the diagnostic accuracy; in addition, there are also efforts evaluating cost-effectiveness and applications on primary, secondary, or hybrid screening. For the rest of the anatomical sites, such as the gastrointestinal system, thyroid gland, urinary tract, and breast, there are significantly less efforts relevant to the application of ANNs. Additionally, there are still anatomical systems for which ANNs have never been applied on their cytological material.
conclusionsCytopathology is an ideal discipline to apply ANNs. In general, diagnosis is performed by experts via the light microscope. However, this approach introduces subjectivity, because this is not a universal and objective measurement process. This has resulted in the existence of a gray zone between normal and pathological cases. From the analysis of related articles, it is obvious that there is a need to perform more thorough analyses, using extensive number of cases and particularly for the nonexplored organs. Efforts to apply such systems within the laboratory test environment are required for their future uptake.
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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.