ReviewBiomedicines2022
Applications of Neural Networks in Biomedical Data Analysis.
Review in Biomedicines, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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
15 citing papers in PubMed.
- Article
- A human epithelial cell cycle dataset.Scientific data · 2026Article
- Use of artificial neural network optimization for MHD and non-Newtonian peritoneal Jeffery nanofluid dynamics on female reproductive health.Journal of biological engineering · 2026Article
- Tracing Genetic Images Formed During Evolution.International journal of molecular sciences · 2026Article
- Artificial Intelligence in Organoid-Based Disease Modeling: A New Frontier in Precision Medicine.Biomimetics (Basel, Switzerland) · 2025Review
- Cannabidiol does not cause DNA double-strand breaks in a human liver-derived cell model.Journal of cannabis research · 2025Article
- Reconstructing strontium-90 intake in beagles using neural networks: a data-driven assessment of historical inhalation records.Journal of radiological protection : official journal of the Society for Radiological Protection · 2025Article
- Artificial Neural Network for the Fast Screening of Samples from Suspected Urinary Tract Infections.Antibiotics (Basel, Switzerland) · 2025Article
- CAPI-Detect: machine learning in capillaroscopy reveals new variables influencing diagnosis.Rheumatology (Oxford, England) · 2025Article
- Assessment of Heart Rhythm and Arrhythmia Detection from Electromyographic Signals Using a Convolutional Neural Network.Sovremennye tekhnologii v meditsine · 2025Article
- IFIT3 activation significantly contributes to HIV-1-associated neurodegenerative disorder-mediated neuroinflammation.Frontiers in immunology · 2025Article
- Techniques for learning and transferring knowledge for microbiome-based classification and prediction: review and assessment.Briefings in bioinformatics · 2024Review
- The Role of Neural Network Analysis in Identifying Predictors of Gastric Cancer.Acta informatica medica : AIM : journal of the Society for Medical Informatics of Bosnia & Herzegovina : casopis Drustva za medicinsku informatiku BiH · 2024Article
- Application of artificial neural networks to evaluate femur development in the human fetus.PloS one · 2024Article
- Why Does Rehabilitation Not (Always) Work in Osteoarthritis? Does Rehabilitation Need Molecular Biology?International journal of molecular sciences · 2023Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
Neural networks for deep-learning applications, also called artificial neural networks, are important tools in science and industry. While their widespread use was limited because of inadequate hardware in the past, their popularity increased dramatically starting in the early 2000s when it became possible to train increasingly large and complex networks. Today, deep learning is widely used in biomedicine from image analysis to diagnostics. This also includes special topics, such as forensics. In this review, we discuss the latest networks and how they work, with a focus on the analysis of biomedical data, particularly biomarkers in bioimage data. We provide a summary on numerous technical aspects, such as activation functions and frameworks. We also present a data analysis of publications about neural networks to provide a quantitative insight into the use of network types and the number of journals per year to determine the usage in different scientific fields.
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.