ArticleData in brief2024
A comprehensive dental dataset of six classes for deep learning based object detection study.
Article in Data in brief, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 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
3 citing papers in PubMed.
- Advanced Deep Learning Models for Classifying Dental Diseases from Panoramic Radiographs.Diagnostics (Basel, Switzerland) · 2026Article
- ACO-optimized MobileNetV2-ShuffleNet hybrid model for automated dental caries classification.Scientific reports · 2025Article
- A comprehensive dental dataset of six classes for deep learning based object detection study.Data in brief · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This article presents a dental dataset for the improvement of research on deep learning-based detection and classification of dental diseases. The dataset is consisted of 232 panoramic dental radiographs, categorized into six major classes: healthy teeth, caries, impacted teeth, infections, fractured teeth, and broken-down crowns/roots (BDC/BDR). The images were collected from three renowned private clinics in Dhaka, Bangladesh, with the help of an experienced dental practitioner who ensured the confidentiality of patients and high-quality data acquisition using a 64-megapixel Android phone camera. To enhance the value of the dataset for machine and deep learning applications, we applied Contrast-Limited Adaptive Histogram Equalization (CLAHE) for image enhancement and augmented the data. The images were annotated using the CVAT tool and reviewed by dental experts. This benchmark dataset is publicly available and provides a valuable resource for researchers in artificial intelligence, computer science, and dental informatics to promote interdisciplinary collaboration and the development of advanced algorithms for dental disease detection.
Indexed as
Identifiers
What Socratic 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 Socratic graph.