Oral squamous cell carcinoma (OSCC) is a common malignant tumor of the head and neck. In recent years, the incidence rate has been increasing. Mitochondria are dynamic organelles involved in various cell behaviors in eukaryotic cells. Mitochondrial dysfunction is closely related to tumor development. As a switch that determines cancer cell death, targeting mitochondria has become the focus of OSCC treatment. This article reviews the relationship between mitochondria and tumorigenesis and development, OSCC treatment, and cisplatin resistant OSCC. Current studies have found that mitochondrial dysfunction promotes cell carcinogenesis, and the mitochondrial morphology and function of cancer cells are significantly changed. The increase of mitochondrial fission improves the invasiveness of cancer cells, and mitophagy dysfunction can induce cancer cell apoptosis. The emergence of drugs and the development of nanotechnology in targeted drug delivery systems have opened up new methods for targeting mitochondria to treat OSCC, reducing the side effects of systemic medication. The cisplatin resistance of OSCC is generated through the mitochondrial pathway, and the mitochondrial function and mutation mechanism of mitochondrial DNA are clarified in order to provide new ideas for targeting mitochondria to treat cisplatin resistant OSCC.

Tooth wear is a common and complex oral problem, with a gradually increasing global incidence. Tooth wear not only affects the oral function and esthetics of patients but may also lead to tooth sensitivity, temporomandibular joint diseases, and other related complications. The continuous progress of digital technology has shown significant potential for the diagnosis and treatment of tooth wear. In recent years, researchers have extensively studied the application of digital technology in tooth wear research from the perspectives of digital support devices, cutting-edge deep learning applications, technology diagnosis, design and prediction, and current limitations. Such studies have provided a deep exploration of the micrometer-level resolution advantages of three-dimensional oral scanning technology in the early detection of tooth wear, which can assist in precise clinical and scientific research practices. Deep learning technology can also achieve image recognition and automated analysis to reduce human error and improve diagnostic efficiency, while quantitative analysis techniques guide clinical decision-making by more accurately calculating the tooth volume, surface area, and wear depth. Finally, simulation techniques can be employed to enhance understanding of the biomechanical and chemical mechanisms of tooth wear and predict its progression. These studies have also highlighted the current difficulties in data management, privacy protection, and obtaining high-quality big data, as well as technical barriers and insufficient evidence-based medical evidence in this field. Nevertheless, digital technology will undoubtedly improve and play an increasingly important role in future dental practice.