Mobile-based oral cancer classification for point-of-care screening

Abstract

. Significance: Oral cancer is among the most common cancers globally, especially in low- and middle-income countries. Early detection is the most effective way to reduce the mortality rate. Deep learning-based cancer image classification models usually need to be hosted on a computing server. However, internet connection is unreliable for screening in low-resource settings. Aim: To develop a mobile-based dual-mode image classification method and customized Android application for point-of-care oral cancer detection. Approach: The dataset used in our study was captured among 5025 patients with our customized dual-modality mobile oral screening devices. We trained an efficient network MobileNet with focal loss and converted the model into TensorFlow Lite format. The finalized lite format model is and ideal for smartphone platform operation. We have developed an Android smartphone application in an easy-to-use format that implements the mobile-based dual-modality image classification approach to distinguish oral potentially malignant and malignant images from normal/benign images. Results: We investigated the accuracy and running speed on a cost-effective smartphone computing platform. It takes to process one image pair with the Moto G5 Android smartphone. We tested the proposed method on a standalone dataset and achieved 81% accuracy for distinguishing normal/benign lesions from clinically suspicious lesions, using a gold standard of clinical impression based on the review of images by oral specialists. Conclusions: Our study demonstrates the effectiveness of a mobile-based approach for oral cancer screening in low-resource settings.

Efficacy of Artificial Intelligence-Assisted Discrimination of Oral Cancerous Lesions from Normal Mucosa Based on the Oral Mucosal Image: A Systematic Review and Meta-Analysis

“…This analysis included 14 studies [ 6 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 ]. Table 1 presents the assessment of bias.…”

“… Forest plot of the diagnostic odds ratios for ( A ) screening only oral cancerous lesions [ 13 , 16 , 17 , 21 , 22 , 23 , 25 ] and ( B ) screening all premalignant mucosal lesions [ 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 23 , 24 ]. …”

“… Forest plots of ( A ) sensitivity, ( B ) specificity, and ( C ) negative predictive values for screening all premalignant mucosal lesions [ 6 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 23 , 24 ]. …”

“…The Egger's test result (p > 0.05) also shows that the possibility of publication bias is low. Forest plot of the diagnostic odds ratios for (A) screening only oral cancerous lesions [13,16,17,[21][22][23]25] and (B) screening all premalignant mucosal lesions [13][14][15][16][17][18][19][20][21]23,24].…”

The Effectiveness of Artificial Intelligence in Detection of Oral Cancer

“…Algorithms based on spectra from 310 nonkeratinised anatomic sites (buccal, tongue, floor of mouth, and lip) yielded an area under the receiver operating characteristic curve of 0.96 in the training set and 0.93 in the validation set. 12 Song et al 29 USA 6211 pairs of intraoral images from 5025 patients Intraoral images Learning machine = dual-modality mobile-based classification using deep learning model MobileNet/ Training = 300 epochs. AC = 81%, SN = 79%, SP = 82% The proposed method achieved 81% accuracy for distinguishing normal/benign lesions from clinically suspicious lesions.…”