Detection of oral cancer and oral potentially malignant disorders using artificial intelligence-based image analysis.
artificial intelligence
deep learning
oral cancer
oral squamous cell carcinoma
Journal
Head & neck
ISSN: 1097-0347
Titre abrégé: Head Neck
Pays: United States
ID NLM: 8902541
Informations de publication
Date de publication:
11 Jun 2024
11 Jun 2024
Historique:
revised:
02
06
2024
received:
18
12
2023
accepted:
02
06
2024
medline:
11
6
2024
pubmed:
11
6
2024
entrez:
11
6
2024
Statut:
aheadofprint
Résumé
We aimed to construct an artificial intelligence-based model for detecting oral cancer and dysplastic leukoplakia using oral cavity images captured with a single-lens reflex camera. We used 1043 images of lesions from 424 patients with oral squamous cell carcinoma (OSCC), leukoplakia, and other oral mucosal diseases. An object detection model was constructed using a Single Shot Multibox Detector to detect oral diseases and their locations using images. The model was trained using 523 images of oral cancer, and its performance was evaluated using images of oral cancer (n = 66), leukoplakia (n = 49), and other oral diseases (n = 405). For the detection of only OSCC versus OSCC and leukoplakia, the model demonstrated a sensitivity of 93.9% versus 83.7%, a negative predictive value of 98.8% versus 94.5%, and a specificity of 81.2% versus 81.2%. Our proposed model is a potential diagnostic tool for oral diseases.
Sections du résumé
BACKGROUND
BACKGROUND
We aimed to construct an artificial intelligence-based model for detecting oral cancer and dysplastic leukoplakia using oral cavity images captured with a single-lens reflex camera.
SUBJECTS AND METHODS
METHODS
We used 1043 images of lesions from 424 patients with oral squamous cell carcinoma (OSCC), leukoplakia, and other oral mucosal diseases. An object detection model was constructed using a Single Shot Multibox Detector to detect oral diseases and their locations using images. The model was trained using 523 images of oral cancer, and its performance was evaluated using images of oral cancer (n = 66), leukoplakia (n = 49), and other oral diseases (n = 405).
RESULTS
RESULTS
For the detection of only OSCC versus OSCC and leukoplakia, the model demonstrated a sensitivity of 93.9% versus 83.7%, a negative predictive value of 98.8% versus 94.5%, and a specificity of 81.2% versus 81.2%.
CONCLUSIONS
CONCLUSIONS
Our proposed model is a potential diagnostic tool for oral diseases.
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Informations de copyright
© 2024 The Author(s). Head & Neck published by Wiley Periodicals LLC.
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