Convolutional neural networks for accurate real-time diagnosis of oral epithelial dysplasia and oral squamous cell carcinoma using high-resolution in vivo confocal microscopy

Ramani, RS; Tan, I; Bussau, L; O&#39;Reilly, LA; Silke, J; Angel, C; Celentano, A; Whitehead, L; McCullough, M; Yap, T

Author(s): Ramani, RS; Tan, I; Bussau, L; O'Reilly, LA; Silke, J; Angel, C; Celentano, A; Whitehead, L; McCullough, M; Yap, T;
Details: Publication Year 2025-01-20,Volume 15,Issue #1,Page 2555
Journal Title: Scientific Reports
Publication Type: Research article
Abstract: Oral cancer detection is based on biopsy histopathology, however with digital microscopy imaging technology there is real potential for rapid multi-site imaging and simultaneous diagnostic analysis. Fifty-nine patients with oral mucosal abnormalities were imaged in vivo with a confocal laser endomicroscope using the contrast agents acriflavine and fluorescein for the detection of oral epithelial dysplasia and oral cancer. To analyse the 9168 images frames obtained, three tandem applied pre-trained Inception-V3 convolutional neural network (CNN) models were developed using transfer learning in the PyTorch framework. The first CNN was used to filter for image quality, followed by image specific diagnostic triage models for fluorescein and acriflavine, respectively. Images were categorised based on a histopathological diagnosis into 4 categories: no dysplasia, lichenoid lesions, low-grade dysplasia and high-grade dysplasia/oral squamous cell carcinoma (OSCC). The quality filtering model had an accuracy of 89.5%. The acriflavine diagnostic model performed well for identifying lichenoid (AUC = 0.94) and low-grade dysplasia (AUC = 0.91) but poorly for identifying no dysplasia (AUC = 0.44) or high-grade dysplasia/OSCC (AUC = 0.28). In contrast, the fluorescein diagnostic model had high classification performance for all diagnostic classes (AUC range = 0.90-0.96). These models had a rapid classification speed of less than 1/10th of a second per image. Our study suggests that tandem CNNs can provide highly accurate and rapid real-time diagnostic triage for in vivo assessment of high-risk oral mucosal disease.
Publisher: Springer Nature
Keywords: Humans; *Mouth Neoplasms/diagnostic imaging/pathology/diagnosis; *Neural Networks, Computer; *Microscopy, Confocal/methods; *Mouth Mucosa/pathology/diagnostic imaging; *Carcinoma, Squamous Cell/diagnostic imaging/pathology/diagnosis; Female; Male; Middle Aged; Aged; Adult; Acriflavine; Fluorescein; Deep learning; Digital microscopy; Early diagnosis; Oral cancer
Department(s): Pathology
Publisher's Version: https://doi.org/10.1038/s41598-025-86400-5
Open Access at Publisher's Site: https://doi.org/10.1038/s41598-025-86400-5
Terms of Use/Rights Notice: Refer to copyright notice on published article.

Creation Date: 2025-01-28 02:47:13

Last Modified: 2025-01-28 02:54:38