ArticleJournal of thoracic disease2017
An overview of the use of artificial neural networks in lung cancer research.
Article in Journal of thoracic disease, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 1 of them a synthesis that pooled it.
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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.
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Who cites it
21 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Predicting Absenteeism and Temporary Disability Using Machine Learning: a Systematic Review and Analysis.Journal of medical systems · 2020Pooled it
- Transcriptomics and AI-driven approaches to the diagnosis and treatment of rheumatoid arthritis.Frontiers in immunology · 2026Review
- Artificial intelligence in lung cancer.Annals of translational medicine · 2024Article
- Article
- Artificial Neural Networks in Lung Cancer Research: A Narrative Review.Journal of clinical medicine · 2023Review
- Advances in neuroproteomics for neurotrauma: unraveling insights for personalized medicine and future prospects.Frontiers in neurology · 2023Review
- The state of the art for artificial intelligence in lung digital pathology.The Journal of pathology · 2022Review
- Federated Learning Approach with Pre-Trained Deep Learning Models for COVID-19 Detection from Unsegmented CT images.Life (Basel, Switzerland) · 2022Article
- Machine Learning Algorithms: Prediction and Feature Selection for Clinical Refracture after Surgically Treated Fragility Fracture.Journal of clinical medicine · 2022Article
- Identification of Signature Genes and Construction of an Artificial Neural Network Model of Prostate Cancer.Journal of healthcare engineering · 2022Article
- Artificial Intelligence in Surveillance, Diagnosis, Drug Discovery and Vaccine Development against COVID-19.Pathogens (Basel, Switzerland) · 2021Review
- Artificial intelligence as a fundamental tool in management of infectious diseases and its current implementation in COVID-19 pandemic.Environmental science and pollution research international · 2021Review
- Application of Artificial Neural Network to PreoperativeFrontiers in medicine · 2021Article
- Development of an artificial neural network as a tool for predicting the chemical attributes of fresh peach fruits.PloS one · 2021Article
- An artificial neural network model predicting pathologic nodal metastases in clinical stage I-II esophageal squamous cell carcinoma patients.Journal of thoracic disease · 2020Article
- Restructured society and environment: A review on potential technological strategies to control the COVID-19 pandemic.The Science of the total environment · 2020Review
- Article
- Assessment of risk based on variant pathways and establishment of an artificial neural network model of thyroid cancer.BMC medical genetics · 2019Article
- [An Overview of the Application of Artificial Neural Networks in Lung Cancer Research].Zhongguo fei ai za zhi = Chinese journal of lung cancer · 2019Review
- A reliable method for colorectal cancer prediction based on feature selection and support vector machine.Medical & biological engineering & computing · 2019Article
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
No grant is acknowledged in the PubMed record.
Abstract
The artificial neural networks (ANNs) are statistical models where the mathematical structure reproduces the biological organisation of neural cells simulating the learning dynamics of the brain. Although definitions of the term ANN could vary, the term usually refers to a neural network used for non-linear statistical data modelling. The neural models applied today in various fields of medicine, such as oncology, do not aim to be biologically realistic in detail but just efficient models for nonlinear regression or classification. ANN inference has applications in tasks that require attention focusing. ANNs also have a niche to carve in clinical decision support, but their success depends crucially on better integration with clinical protocols, together with an awareness of the need to combine different paradigms to produce the simplest and most transparent overall reasoning structure, and the will to evaluate this in a real clinical environment. We have performed an assessment of the evidence for improvements in the use of ANN in lung cancer research. Our analysis showed that often the use of ANN in the medical literature had not been performed in an accurate manner. A strict cooperation between physician and biostatisticians could be helpful in determine and resolve these errors.
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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.