ArticleWorld journal of clinical oncology2020
Artificial intelligence in dentistry: Harnessing big data to predict oral cancer survival.
Article in World journal of clinical oncology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 5 of them syntheses that pooled it.
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Who cites it
17 citing papers in PubMed, 5 syntheses or guidelines pooled it, 31 citations in OpenAlex.
- Artificial intelligence for oral cancer diagnosis: a systematic review and meta-analysis of image-based and non-imaging models.BMC cancer · 2026Pooled it
- Accuracy of machine learning methods in predicting prognosis of patients with psychotic spectrum disorders: a systematic review.BMJ open · 2025Pooled it
- Prognosing post-treatment outcomes of head and neck cancer using structured data and machine learning: A systematic review.PloS one · 2024Pooled it
- Use of Deep Neural Networks in the Detection and Automated Classification of Lesions Using Clinical Images in Ophthalmology, Dermatology, and Oral Medicine-A Systematic Review.Journal of digital imaging · 2023Pooled it
- Neural networks for classification of cervical vertebrae maturation: a systematic review.The Angle orthodontist · 2022Pooled it
- AI-assisted image analysis ofBioinformation · 2026Article
- "Enhancing early detection of oral cancer: a comparative study of artificial intelligence models and clinical specialist in lesion classification".BMC cancer · 2025Article
- DeepOmicsSurv: a deep learning-based model for survival prediction of oral cancer.Discover oncology · 2025Article
- AI-based diagnostic tools for oral cancer: A systematic review.Bioinformation · 2025Article
- Machine Learning as a Tool for Early Detection: A Focus on Late-Stage Colorectal Cancer across Socioeconomic Spectrums.Cancers · 2024Article
- The use of artificial intelligence models to predict survival in patients with laryngeal squamous cell carcinoma.Scientific reports · 2023Article
- Exploring the Intersection of Artificial Intelligence and Clinical Healthcare: A Multidisciplinary Review.Diagnostics (Basel, Switzerland) · 2023Review
- A Current Review of Machine Learning and Deep Learning Models in Oral Cancer Diagnosis: Recent Technologies, Open Challenges, and Future Research Directions.Diagnostics (Basel, Switzerland) · 2023Review
- Machine Learning Incorporating Host Factors for Predicting Survival in Head and Neck Squamous Cell Carcinoma Patients.Cancers · 2021Article
- Deep Learning in Head and Neck Tumor Multiomics Diagnosis and Analysis: Review of the Literature.Frontiers in genetics · 2021Review
- Article
- Optimization of Tumor Disease Monitoring in Medical Big Data Environment Based on High-Order Simulated Annealing Neural Network Algorithm.Computational intelligence and neuroscience · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundOral cancer is the sixth most prevalent cancer worldwide. Public knowledge in oral cancer risk factors and survival is limited.
aimTo come up with machine learning (ML) algorithms to predict the length of survival for individuals diagnosed with oral cancer, and to explore the most important factors that were responsible for shortening or lengthening oral cancer survival.
methodsWe used the Surveillance, Epidemiology, and End Results database from the years 1975 to 2016 that consisted of a total of 257880 cases and 94 variables. Four ML techniques in the area of artificial intelligence were applied for model training and validation. Model accuracy was evaluated using mean absolute error (MAE), mean squared error (MSE), root mean squared error (RMSE),
resultsThe most important factors predictive of oral cancer survival time were age at diagnosis, primary cancer site, tumor size and year of diagnosis. Year of diagnosis referred to the year when the tumor was first diagnosed, implying that individuals with tumors that were diagnosed in the modern era tend to have longer survival than those diagnosed in the past. The extreme gradient boosting ML algorithms showed the best performance, with the MAE equaled to 13.55, MSE 486.55 and RMSE 22.06.
conclusionUsing artificial intelligence, we developed a tool that can be used for oral cancer survival prediction and for medical-decision making. The finding relating to the year of diagnosis represented an important new discovery in the literature. The results of this study have implications for cancer prevention and education for the public.
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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.