An Update on the Use of Artificial Intelligence in Digital Pathology for Oral Epithelial Dysplasia Research.
Artificial intelligence
Deep learning
Machine learning
Oral dysplasia
Precancerous
Premalignant
Journal
Head and neck pathology
ISSN: 1936-0568
Titre abrégé: Head Neck Pathol
Pays: United States
ID NLM: 101304010
Informations de publication
Date de publication:
10 May 2024
10 May 2024
Historique:
received:
08
02
2024
accepted:
30
03
2024
medline:
10
5
2024
pubmed:
10
5
2024
entrez:
10
5
2024
Statut:
epublish
Résumé
Oral epithelial dysplasia (OED) is a precancerous histopathological finding which is considered the most important prognostic indicator for determining the risk of malignant transformation into oral squamous cell carcinoma (OSCC). The gold standard for diagnosis and grading of OED is through histopathological examination, which is subject to inter- and intra-observer variability, impacting accurate diagnosis and prognosis. The aim of this review article is to examine the current advances in digital pathology for artificial intelligence (AI) applications used for OED diagnosis. We included studies that used AI for diagnosis, grading, or prognosis of OED on histopathology images or intraoral clinical images. Studies utilizing imaging modalities other than routine light microscopy (e.g., scanning electron microscopy), or immunohistochemistry-stained histology slides, or immunofluorescence were excluded from the study. Studies not focusing on oral dysplasia grading and diagnosis, e.g., to discriminate OSCC from normal epithelial tissue were also excluded. A total of 24 studies were included in this review. Nineteen studies utilized deep learning (DL) convolutional neural networks for histopathological OED analysis, and 4 used machine learning (ML) models. Studies were summarized by AI method, main study outcomes, predictive value for malignant transformation, strengths, and limitations. ML/DL studies for OED grading and prediction of malignant transformation are emerging as promising adjunctive tools in the field of digital pathology. These adjunctive objective tools can ultimately aid the pathologist in more accurate diagnosis and prognosis prediction. However, further supportive studies that focus on generalization, explainable decisions, and prognosis prediction are needed.
Identifiants
pubmed: 38727841
doi: 10.1007/s12105-024-01643-4
pii: 10.1007/s12105-024-01643-4
doi:
Types de publication
Journal Article
Review
Langues
eng
Sous-ensembles de citation
IM
Pagination
38Informations de copyright
© 2024. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.
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