Dent J (Basel). 2026 Jul 12;14(7):429. doi: 10.3390/dj14070429.
ABSTRACT
Background/Objectives: Artificial intelligence (AI) has emerged as a transformative tool in oral medicine, where it holds significant promise for enhancing the diagnosis, treatment, and management of oral cancer. Our team aimed to develop a new tool, capable of diagnosing oral cancer utilizing the capabilities of AI. Methods: The task we aim to solve from computer vision's perspective is an object detection task. Under this context, a detection is essentially a bounding box drawn around an oral lesion accompanied by the disease's description. To solve the task at hand, we collected and annotated a wide set of images which were used for training our model. Specifically, we used 205 images of Oral Squamous Cell Carcinoma (OSCC). Following common practice, 80% of the total images were allocated for training, 10% for validation, and 10% for testing. The training set was used to optimize the model's parameters across multiple iterations. The validation set served to prevent overfitting during training and to guide hyperparameter tuning. Lastly, the test set was used for evaluation of data that had not been previously seen by the model and had not influenced any decisions regarding its architecture or hyperparameters. Moreover, during evaluation, we supplemented the test set by adding 100 images of healthy mucosa to examine whether the model generated false positives on healthy tissue. To broaden the dataset's coverage, we generated synthetic images by applying data augmentation techniques such as random rotation, scale and noise injection. The model's architecture was based on YOLO11, which is a widely spread neural network architecture known for its balance between efficiency and performance, used in object detection tasks. Results: The model's detections were accompanied by a confidence measure, which was used to filter out those with low confidence, and one could choose a lower threshold for maximizing precision or a higher threshold for maximizing recall. Among images that correspond to oral cancer (oral squamous cell carcinoma), the model achieved 59% precision and 41% recall on the validation set and 56% precision and 42% recall on the test set. The limitations of this study include the single institutional design and the relatively small sample size. The number of images used for model training was relatively small (205 images of oral squamous cell carcinoma), which may limit the generalizability of the findings. The main limitation is that the model distinguishes oral squamous cell carcinoma from healthy mucosa. In routine clinical practice, however, the diagnostic challenge is to differentiate oral cancer from a variety of benign and potentially malignant disorders that may present with similar clinical features. The inclusion of other oral lesions is planned in future studies. Conclusions: The efficiency of our AI application may be considered as encouraging, taking the pilot nature of the study into consideration. More clinical photos and better training of the model may lead to better precision and recall, enabling its inclusion in standard clinical practice. Larger multicenter datasets will be required for clinical implementation. AI-driven tools can assist in risk stratification, helping clinicians determine the best treatment plans by analyzing patient data and predicting the likelihood of recurrence or metastasis. AI's potential extends beyond diagnosis and treatment; it also contributes to monitoring patient outcomes. As research and technology evolve, AI's role in oral cancer is likely to become increasingly indispensable.
PMID:42505737 | PMC:PMC13408865 | DOI:10.3390/dj14070429