Advances in Oral Cancer Diagnosis: Is AI the Magic Tool?

Oral care diagnosis is a critical aspect of maintaining overall health and well-being. Early detection and accurate diagnosis of oral diseases can significantly improve patient outcomes and reduce healthcare costs [1]. In recent years, AI has emerged as a promising tool for enhancing diagnostic accuracy and efficiency in oral care [2]. This review article focuses on the advances in AIbased techniques for oral care diagnosis, their potential impact on clinical practice, and the challenges and future directions for this field.

A comprehensive literature search was conducted using PubMed, Scopus and Web of Science databases to identify relevant articles published in the last decade. The search terms included “artificial intelligence,” “machine learning,” “deep learning,” “oral care,” “oral diagnosis,” “dental,” and “periodontal.” The articles were screened for relevance, and a total of 10 key studies were selected for analysis and discussion.

AI in Dental Imaging

Dental imaging, including intraoral radiographs and conebeam computed tomography (CBCT), plays a vital role in oral care diagnosis [3]. AI techniques, particularly deep learning algorithms, have shown promising results in automating the detection and diagnosis of dental caries [4], periodontal bone loss [5], and root fractures [6]. For instance, Lee et al. [7] demonstrated that a convolutional neural network (CNN) could accurately detect dental caries in bitewing radiographs, with a sensitivity of 94.3% and a specificity of 92.9%. Similarly, Tuzoff et al. [8] reported that a deep learning model could identify periodontal bone loss in CBCT images with a sensitivity of 91.2% and a specificity of 88.7%. These studies highlight the potential of AI in improving the accuracy and efficiency of dental imaging interpretation.

Oral Cancer Detection

Early detection of oral cancer is crucial for improving patient outcomes. AI-based techniques have shown promise in detecting oral cancer from clinical images and histopathological slides [9]. For example, Zhang et al. [10] developed a deep learning model that achieved an accuracy of 92.4% in detecting oral squamous cell carcinoma from histopathological images. These advances in AIbased oral cancer detection have the potential to enhance screening and diagnostic processes.

Orthodontic and Prosthodontic Applications

AI has also been applied in orthodontic and prosthodontic diagnosis and treatment planning. In orthodontics, AI algorithms have been used for automated cephalometric analysis [11], tooth segmentation [12], and treatment outcome prediction [13]. For instance, Park et al. [14] demonstrated that a deep learning model could accurately perform cephalometric landmark detection with a mean error of 1.83 mm. In prosthodontics, AI has been employed for the assessment of dental implant sites [15] and the design of dental prostheses (16). These applications have the potential to streamline treatment planning and improve patient care.

Despite the promising advances in AI-based oral care diagnosis, several challenges remain. Data privacy and security are significant concerns, as the use of AI requires large datasets containing sensitive patient information [17]. Additionally, the integration of AI into clinical workflows and the need for interdisciplinary collaboration between dental professionals and AI experts present logistical challenges [18]. Future research should focus on addressing these challenges, as well as ontic and prosthodontic treatment planning. Despite the challenges, the integration of AI into clinical practice holds promise for improving patient outcomes and reducing healthcare costs. Future research should focus on addressing the existing challenges and validating the performance of AI algorithms in diverse clinical settings [19,20].

None.

No conflict of interest.

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