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Computational pathology-based artificial intelligence platform for the identification of common oral potentially malignant disorders
by
Cai, Xinjia
, Li, Long
, Zhang, Jianyun
in
Algorithms
/ Artificial Intelligence
/ Carcinoma, Squamous Cell - diagnosis
/ Carcinoma, Squamous Cell - pathology
/ Computational intelligence
/ Deep Learning
/ Dental research
/ Dentistry
/ Diagnosis
/ Diagnosis, Differential
/ Differential diagnosis
/ Female
/ Fibrosis
/ Humans
/ Intelligent Systems
/ Leukokeratosis
/ Leukoplakia, Oral - diagnosis
/ Leukoplakia, Oral - pathology
/ Lichen planus
/ Lichen Planus, Oral - diagnosis
/ Lichen Planus, Oral - pathology
/ Machine Learning
/ Medicine
/ Mouth cancer
/ Mouth Neoplasms - diagnosis
/ Mouth Neoplasms - pathology
/ Oral and Maxillofacial Surgery
/ Oral carcinoma
/ Oral leukoplakia
/ Oral lichen planus
/ Oral squamous cell carcinoma
/ Oral submucous fibrosis
/ Oral Submucous Fibrosis - diagnosis
/ Oral Submucous Fibrosis - pathology
/ Pathology
/ Physiological aspects
/ Precancerous Conditions - diagnosis
/ Precancerous Conditions - pathology
2026
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Computational pathology-based artificial intelligence platform for the identification of common oral potentially malignant disorders
by
Cai, Xinjia
, Li, Long
, Zhang, Jianyun
in
Algorithms
/ Artificial Intelligence
/ Carcinoma, Squamous Cell - diagnosis
/ Carcinoma, Squamous Cell - pathology
/ Computational intelligence
/ Deep Learning
/ Dental research
/ Dentistry
/ Diagnosis
/ Diagnosis, Differential
/ Differential diagnosis
/ Female
/ Fibrosis
/ Humans
/ Intelligent Systems
/ Leukokeratosis
/ Leukoplakia, Oral - diagnosis
/ Leukoplakia, Oral - pathology
/ Lichen planus
/ Lichen Planus, Oral - diagnosis
/ Lichen Planus, Oral - pathology
/ Machine Learning
/ Medicine
/ Mouth cancer
/ Mouth Neoplasms - diagnosis
/ Mouth Neoplasms - pathology
/ Oral and Maxillofacial Surgery
/ Oral carcinoma
/ Oral leukoplakia
/ Oral lichen planus
/ Oral squamous cell carcinoma
/ Oral submucous fibrosis
/ Oral Submucous Fibrosis - diagnosis
/ Oral Submucous Fibrosis - pathology
/ Pathology
/ Physiological aspects
/ Precancerous Conditions - diagnosis
/ Precancerous Conditions - pathology
2026
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Computational pathology-based artificial intelligence platform for the identification of common oral potentially malignant disorders
by
Cai, Xinjia
, Li, Long
, Zhang, Jianyun
in
Algorithms
/ Artificial Intelligence
/ Carcinoma, Squamous Cell - diagnosis
/ Carcinoma, Squamous Cell - pathology
/ Computational intelligence
/ Deep Learning
/ Dental research
/ Dentistry
/ Diagnosis
/ Diagnosis, Differential
/ Differential diagnosis
/ Female
/ Fibrosis
/ Humans
/ Intelligent Systems
/ Leukokeratosis
/ Leukoplakia, Oral - diagnosis
/ Leukoplakia, Oral - pathology
/ Lichen planus
/ Lichen Planus, Oral - diagnosis
/ Lichen Planus, Oral - pathology
/ Machine Learning
/ Medicine
/ Mouth cancer
/ Mouth Neoplasms - diagnosis
/ Mouth Neoplasms - pathology
/ Oral and Maxillofacial Surgery
/ Oral carcinoma
/ Oral leukoplakia
/ Oral lichen planus
/ Oral squamous cell carcinoma
/ Oral submucous fibrosis
/ Oral Submucous Fibrosis - diagnosis
/ Oral Submucous Fibrosis - pathology
/ Pathology
/ Physiological aspects
/ Precancerous Conditions - diagnosis
/ Precancerous Conditions - pathology
2026
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Computational pathology-based artificial intelligence platform for the identification of common oral potentially malignant disorders
Journal Article
Computational pathology-based artificial intelligence platform for the identification of common oral potentially malignant disorders
2026
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Overview
Objectives
Oral potentially malignant disorders (OPMDs) refer to a group of significant precursor lesions for oral squamous cell carcinoma (OSCC). Early differential diagnosis of its subtypes including oral leukoplakia (OLK), oral lichen planus (OLP) and oral submucous fibrosis (OSF) is crucial for timely prevention and management of OSCC. The diverse clinical features of aforementioned conditions render clinical diagnosis challenging. To facilitate accurate and efficient diagnosis, this study aimed to construct a digital pathology-based artificial intelligence (AI) platform based on a sample of 1080 cases.
Materials and methods
Four deep learning models–Twins-SVT, ResNet18, ResNet50, and Inception_v3–were employed for patch-level analysis. The AI platform was developed by fusing deep learning-derived predictive features with multiple machine learning algorithms.
Results
The AI platform developed in this study exhibited good diagnostic performance, with the diagnostic area under the receiver operating characteristic curve of 0.870 (95% CI: 0.827–0.913), 0.810 (95% CI: 0.751–0.870), and 0.833 (95% CI: 0.770–0.897) for OLK, OLP, and OSF, respectively. In addition, this study investigated the correlation between deep learning-derived AI features and pathological findings, demonstrating excellent interpretability of AI in pathology.
Conclusions
In this study, a robust AI platform was developed for differential diagnosis of OLK, OLP and OSF. Our research supports the feasibility of using deep learning methods to classify common OPMDs based on whole slide images.
Publisher
BioMed Central,BioMed Central Ltd,Springer Nature B.V,BMC
Subject
/ Carcinoma, Squamous Cell - diagnosis
/ Carcinoma, Squamous Cell - pathology
/ Female
/ Fibrosis
/ Humans
/ Leukoplakia, Oral - diagnosis
/ Leukoplakia, Oral - pathology
/ Lichen Planus, Oral - diagnosis
/ Lichen Planus, Oral - pathology
/ Medicine
/ Oral and Maxillofacial Surgery
/ Oral squamous cell carcinoma
/ Oral Submucous Fibrosis - diagnosis
/ Oral Submucous Fibrosis - pathology
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