ArticlePediatric dentistry2021
An Automated Machine Learning Classifier for Early Childhood Caries.
Article in Pediatric dentistry, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 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
34 citing papers in PubMed, 37 citations in OpenAlex.
- Clinician-accessible automated machine learning in oral healthcare: A systematic review.The Japanese dental science review · 2026Review
- Article
- Artificial Intelligence in Pediatric Dentistry: Current Applications, Emerging Trends, and Future Directions.Dentistry journal · 2026Review
- [Current applications and future perspectives of automation technologies and devices in oral diagnosis and treatment].Hua xi kou qiang yi xue za zhi = Huaxi kouqiang yixue zazhi = West China journal of stomatology · 2026Review
- Artificial Intelligence Applications in Pediatric Dentistry: A Comprehensive Review.Biomimetics (Basel, Switzerland) · 2026Review
- A Lightweight and Explainable AI Framework Toward Automated Infraocclusion Detection in Pediatric Panoramic Radiographs.Diagnostics (Basel, Switzerland) · 2026Article
- Digital Dentistry in Clinical Practice: A Scoping Review of Current Capabilities and Future Directions.International dental journal · 2026Article
- Artificial Intelligence in Pediatric Dentistry: A Systematic Review and Meta-Analysis.Children (Basel, Switzerland) · 2026Review
- Mathematical and AI-Based Predictive Modelling for Dental Caries Risk Using Clinical and Behavioural Parameters.Bioengineering (Basel, Switzerland) · 2025Article
- Deep Learning Applications in Dental Image-Based Diagnostics: A Systematic Review.Healthcare (Basel, Switzerland) · 2025Review
- Artificial Intelligence Tools in Dentistry: A Systematic Review on Their Application and Outcomes.Cureus · 2025Review
- Artificial intelligence (AI) in restorative dentistry: current trends and future prospects.BMC oral health · 2025Review
- Automated Machine Learning in Dentistry: A Narrative Review of Applications, Challenges, and Future Directions.Diagnostics (Basel, Switzerland) · 2025Review
- Early childhood caries risk prediction using machine learning approaches in Bangladesh.BMC oral health · 2025Article
- Predicting Dental General Anesthesia Use among Children with Behavioral Health Conditions.JDR clinical and translational research · 2025Observational
- Article
- Tech Bytes-Harnessing Artificial Intelligence for Pediatric Oral Health: A Scoping Review.International journal of clinical pediatric dentistry · 2024Article
- The Use of Artificial Intelligence in Caries Detection: A Review.Bioengineering (Basel, Switzerland) · 2024Review
- Review
- Application of Artificial Intelligence in Pediatric Dentistry: A Literature Review.Journal of pharmacy & bioallied sciences · 2024Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors at 1 institution in 1 country.
Funding
Abstract
PubMed holds no abstract for this paper.
Indexed as
Identifiers
34172112PMC8278225W3173292660What 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.