ArticleInternational journal of oral science2024
Digital pathology-based artificial intelligence models for differential diagnosis and prognosis of sporadic odontogenic keratocysts.
Article in International journal of oral science, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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18 citing papers in PubMed, 34 citations in OpenAlex.
- Multimodal integration of radiomics, pathomics, and clinical data enhances grading of adult diffuse gliomas using machine learning.Neuroradiology · 2026Article
- Treatment Modalities and Recurrence Outcomes in Odontogenic Keratocysts: A 24-Year Retrospective Analysis.Healthcare (Basel, Switzerland) · 2026Article
- Development of a Prediction Model for Progression Risk in High-Grade Gliomas Based on Habitat Radiomics and Pathomics.Annals of clinical and translational neurology · 2026Article
- Computational pathology-based artificial intelligence platform for the identification of common oral potentially malignant disorders.BMC oral health · 2026Article
- Interpretative diagnostic model for neuroblastoma metastases using bone marrow cytology.Journal of translational medicine · 2026Article
- Dental Odontogenic Lesion CBCT and Histopathology Integrated Dataset for Benchmarking Deep Learning Algorithms.Scientific data · 2026Article
- A deep learning approach for the diagnosis and recurrence prediction of OKC.Scientific reports · 2026Article
- Deep learning-based approach for differential diagnosis of odontogenic cysts from histopathological images.Medicina oral, patologia oral y cirugia bucal · 2026Article
- Comparative analysis of large language models as decision support tools in oral pathology.Scientific reports · 2026Article
- Impact of Transfer Learning on Convolutional Neural Networks for Odontogenic Tumor Diagnosis.Head and neck pathology · 2026Article
- Assessment of Deep Convolutional Neural Network Models for the Classification of Benign Fibro-Osseous Lesions of the Jaws.Clinical and experimental dental research · 2025Article
- Glandular odontogenic cyst: a ten-case series and literature review with emphasis on radiological diagnostic challenges.BMC oral health · 2025Review
- Development of a deep learning system for predicting biochemical recurrence in prostate cancer.BMC cancer · 2025Article
- Deep learning models for cervical cancer subtyping using whole slide images.Frontiers in oncology · 2025Article
- Prognostic and predictive value of pathohistological features in gastric cancer and identification of SLITRK4 as a potential biomarker for gastric cancer.Scientific reports · 2024Article
- Odontogenic Tumors: A Challenge for Clinical Diagnosis and an Opportunity for AI Innovation.Journal of dentistry (Shiraz, Iran) · 2024Article
- Article
- Construction of a 2.5D Deep Learning Model for Predicting Early Postoperative Recurrence of Hepatocellular Carcinoma Using Multi-View and Multi-Phase CT Images.Journal of hepatocellular carcinoma · 2024Article
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Authors and funding
5 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Odontogenic keratocyst (OKC) is a common jaw cyst with a high recurrence rate. OKC combined with basal cell carcinoma as well as skeletal and other developmental abnormalities is thought to be associated with Gorlin syndrome. Moreover, OKC needs to be differentiated from orthokeratinized odontogenic cyst and other jaw cysts. Because of the different prognosis, differential diagnosis of several cysts can contribute to clinical management. We collected 519 cases, comprising a total of 2 157 hematoxylin and eosin-stained images, to develop digital pathology-based artificial intelligence (AI) models for the diagnosis and prognosis of OKC. The Inception_v3 neural network was utilized to train and test models developed from patch-level images. Finally, whole slide image-level AI models were developed by integrating deep learning-generated pathology features with several machine learning algorithms. The AI models showed great performance in the diagnosis (AUC = 0.935, 95% CI: 0.898-0.973) and prognosis (AUC = 0.840, 95%CI: 0.751-0.930) of OKC. The advantages of multiple slides model for integrating of histopathological information are demonstrated through a comparison with the single slide model. Furthermore, the study investigates the correlation between AI features generated by deep learning and pathological findings, highlighting the interpretative potential of AI models in the pathology. Here, we have developed the robust diagnostic and prognostic models for OKC. The AI model that is based on digital pathology shows promise potential for applications in odontogenic diseases of the jaw.
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