Artificial intelligence (AI) applications in oncology have been developed rapidly with reported successes in recent years. This work aims to evaluate the performance of deep convolutional neural network (CNN) algorithms for the classification and detection of oral potentially malignant disorders (OPMDs) and oral squamous cell carcinoma (OSCC) in oral photographic images. A dataset comprising 980 oral photographic images was divided into 365 images of OSCC, 315 images of OPMDs and 300 images of non-pathological images. Multiclass image classification models were created by using DenseNet-169, ResNet-101, SqueezeNet and Swin-S. Multiclass object detection models were fabricated by using faster R-CNN, YOLOv5, RetinaNet and CenterNet2. The AUC of multiclass image classification of the best CNN models, DenseNet-196, was 1.00 and 0.98 on OSCC and OPMDs, respectively. The AUC of the best multiclass CNN-base object detection models, Faster R-CNN, was 0.88 and 0.64 on OSCC and OPMDs, respectively. In comparison, DenseNet-196 yielded the best multiclass image classification performance with AUC of 1.00 and 0.98 on OSCC and OPMD, respectively. These values were inline with the performance of experts and superior to those of general practictioners (GPs). In conclusion, CNN-based models have potential for the identification of OSCC and OPMDs in oral photographic images and are expected to be a diagnostic tool to assist GPs for the early detection of oral cancer.

Background: Oral cancer is a life-threatening malignancy, which affects the survival rate and quality of life of patients. The aim of this systematic review was to review deep learning (DL) studies in the diagnosis and prognostic prediction of oral cancer. Methods: This systematic review was conducted following the PRISMA guidelines. Databases (Medline via PubMed, Google Scholar, Scopus) were searched for relevant studies, from January 2000 to June 2023. Results: Fifty-four qualified for inclusion, including diagnostic (n = 51), and prognostic prediction (n = 3). Thirteen studies showed a low risk of biases in all domains, and 40 studies low risk for concerns regarding applicability. The performance of DL models was reported of the accuracy of 85.0–100%, F1-score of 79.31 - 89.0%, Dice coefficient index of 76.0 - 96.3% and Concordance index of 0.78–0.95 for classification, object detection, segmentation, and prognostic prediction, respectively. The pooled diagnostic odds ratios were 2549.08 (95% CI 410.77–4687.39) for classification studies. Conclusions: The number of DL studies in oral cancer is increasing, with a diverse type of architectures. The reported accuracy showed promising DL performance in studies of oral cancer and appeared to have potential utility in improving informed clinical decision-making of oral cancer.

The purpose of this study was to evaluate the performance of convolutional neural network-based models for the detection and classification of maxillofacial fractures in computed tomography (CT) maxillofacial bone window images. A total of 3407 CT images, 2407 of which contained maxillofacial fractures, were retrospectively obtained from the regional trauma center from 2016 to 2020. Multiclass image classification models were created by using DenseNet-169 and ResNet-152. Multiclass object detection models were created by using faster R-CNN and YOLOv5. DenseNet-169 and ResNet-152 were trained to classify maxillofacial fractures into frontal, midface, mandibular and no fracture classes. Faster R-CNN and YOLOv5 were trained to automate the placement of bounding boxes to specifically detect fracture lines in each fracture class. The performance of each model was evaluated on an independent test dataset. The overall accuracy of the best multiclass classification model, DenseNet-169, was 0.70. The mean average precision of the best multiclass detection model, faster R-CNN, was 0.78. In conclusion, DenseNet-169 and faster R-CNN have potential for the detection and classification of maxillofacial fractures in CT images.