ArticleCancers2021
Convolutional Neural Network-Based Clinical Predictors of Oral Dysplasia: Class Activation Map Analysis of Deep Learning Results.
Article in Cancers, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 44 papers, 5 of them syntheses that pooled it.
What it found
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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
44 citing papers in PubMed, 5 syntheses or guidelines pooled it, 107 citations in OpenAlex.
- Automated Detection of Oral Malignant Lesions Using Deep Learning: Scoping Review and Meta-Analysis.Oral diseases · 2025Pooled it
- Deep learning in oral cancer- a systematic review.BMC oral health · 2024Pooled it
- Artificial intelligence for classification and detection of oral mucosa lesions on photographs: a systematic review and meta-analysis.Clinical oral investigations · 2024Pooled it
- Diagnostic performance of artificial intelligence in detecting oral potentially malignant disorders and oral cancer using medical diagnostic imaging: a systematic review and meta-analysis.Frontiers in oral health · 2024Pooled it
- Histopathological parameters reported in microinvasive oral squamous cell carcinoma: a systematic review.Medicina oral, patologia oral y cirugia bucal · 2023Pooled it
- Article
- LRF-CNN: An explainable lightweight receptive field-based CNN for colorectal cancer histopathological image classification.iScience · 2026Article
- StyleGAN-based synthetic image augmentation for multi-class otoscopy image classification.Scientific reports · 2026Article
- Oral Cancer Diagnosis Using an Optimized InceptionV3 Model Powered by the Aquila Metaheuristic Algorithm.Asian Pacific journal of cancer prevention : APJCP · 2026Article
- Redefining oropharyngeal cancer in the HPV era: integrating precision medicine and immunotherapeutic frontiers.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2026Review
- An Analysis of the Timeline to Diagnosis and Treatment in Oral Cavity and Oropharynx Cancer.Oral diseases · 2026Article
- Role of Three-Dimensional Convolution Neural Networks (3D-CNN) in Image Processing and Recognition in Oncology: A Systematic Review and Meta-Analysis.International journal of hematology-oncology and stem cell research · 2026Review
- Application of artificial intelligence in oral potentially malignant disorders: current opinions and future barriers.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2026Review
- Advancements in Image-Based Artificial Intelligence in the Diagnosis and Treatment of Head and Neck Squamous Cell Carcinoma: A Narrative Review.International journal of general medicine · 2026Review
- AI-driven prediction of progression to oral squamous cell carcinoma using a multiresolution pathology model.NPJ digital medicine · 2025Article
- PHSP-Net: Personalized Habitat-Aware Deep Learning for Multi-Center Glioblastoma Survival Prediction Using Multiparametric MRI.Bioengineering (Basel, Switzerland) · 2025Article
- A tailored deep learning approach for early detection of oral cancer using a 19-layer CNN on clinical lip and tongue images.Scientific reports · 2025Article
- Automated classification of oral potentially malignant disorders and oral squamous cell carcinoma using a convolutional neural network framework: a cross-sectional study.Lancet regional health. Americas · 2025Article
- Deep learning-based electrical impedance spectroscopy analysis for malignant and potentially malignant oral disorder detection.Scientific reports · 2025Article
- Prediction of pathological grade of oral squamous cell carcinoma and construction of prognostic model based on deep learning algorithm.Discover oncology · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors at 3 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Oral cancer/oral squamous cell carcinoma is among the top ten most common cancers globally, with over 500,000 new cases and 350,000 associated deaths every year worldwide. There is a critical need for objective, novel technologies that facilitate early, accurate diagnosis. For this purpose, we have developed a method to classify images as "suspicious" and "normal" by performing transfer learning on Inception-ResNet-V2 and generated automated heat maps to highlight the region of the images most likely to be involved in decision making. We have tested the developed method's feasibility on two independent datasets of clinical photographic images of 30 and 24 patients from the UK and Brazil, respectively. Both 10-fold cross-validation and leave-one-patient-out validation methods were performed to test the system, achieving accuracies of 73.6% (±19%) and 90.9% (±12%), F1-scores of 97.9% and 87.2%, and precision values of 95.4% and 99.3% at recall values of 100.0% and 81.1% on these two respective cohorts. This study presents several novel findings and approaches, namely the development and validation of our methods on two datasets collected in different countries showing that using patches instead of the whole lesion image leads to better performance and analyzing which regions of the images are predictive of the classes using class activation map analysis.
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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.