ArticleScientific data2024
High-resolution AI image dataset for diagnosing oral submucous fibrosis and squamous cell carcinoma.
Article in Scientific data, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.
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
12 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Artificial Intelligence Innovations in Understanding Oral Submucous Fibrosis: A Nouvelle Modernistic Approach: A Systematic Review.Asian Pacific journal of cancer prevention : APJCP · 2025Pooled it
- Enhanced Histopathologic Image Analysis for Mouth Cancer Classification Using Morphological Reconstruction and UNet.Journal of imaging informatics in medicine · 2026Article
- Oral squamous cell carcinoma diagnosis: an interpretable deep learning approach using multi-scale pathological images.BMC oral health · 2026Article
- Efficient deep learning models for oral squamous cell carcinoma classification in histopathological images.Scientific reports · 2026Article
- Dental Odontogenic Lesion CBCT and Histopathology Integrated Dataset for Benchmarking Deep Learning Algorithms.Scientific data · 2026Article
- A Smartphone-based Comprehensive Dataset of Annotated Oral Cavity Images for Enhanced Oral Disease Diagnosis.Scientific data · 2026Article
- A High Magnifications Histopathology Image Dataset for Oral Squamous Cell Carcinoma Diagnosis and Prognosis.Scientific data · 2026Article
- Domain-generalizable oral cancer classification: bridging multi-center histopathological datasets.Frontiers in medicine · 2026Article
- Non-Invasive Techniques for Early Detection of Oral Squamous Cell Carcinoma: A Narrative Review.Cancer management and research · 2026Review
- A histopathology image-based computer-aided classification study for oral squamous cell carcinoma.Frontiers in oncology · 2026Article
- Oral submucosal fibrosis: a comprehensive review on pathogenesis, diagnosis, therapeutics and computational advances.Frontiers in cell and developmental biology · 2025Review
- High-resolution AI image dataset for diagnosing oral submucous fibrosis and squamous cell carcinoma.Scientific data · 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
10 authors.
Funding
Abstract
Oral cancer is a global health challenge with a difficult histopathological diagnosis. The accurate histopathological interpretation of oral cancer tissue samples remains difficult. However, early diagnosis is very challenging due to a lack of experienced pathologists and inter- observer variability in diagnosis. The application of artificial intelligence (deep learning algorithms) for oral cancer histology images is very promising for rapid diagnosis. However, it requires a quality annotated dataset to build AI models. We present ORCHID (ORal Cancer Histology Image Database), a specialized database generated to advance research in AI-based histology image analytics of oral cancer and precancer. The ORCHID database is an extensive multicenter collection of high-resolution images captured at 1000X effective magnification (100X objective lens), encapsulating various oral cancer and precancer categories, such as oral submucous fibrosis (OSMF) and oral squamous cell carcinoma (OSCC). Additionally, it also contains grade-level sub-classifications for OSCC, such as well- differentiated (WD), moderately-differentiated (MD), and poorly-differentiated (PD). The database seeks to aid in developing innovative artificial intelligence-based rapid diagnostics for OSMF and OSCC, along with subtypes.
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.