This study uses digital bitewing radiography as a standard for diagnosing proximal secondary caries. Patients will undergo imaging with a parallel technique and fixed settings to ensure high-quality, consistent images. Radiographs are interpreted by experienced dental professionals to maintain diagnostic accuracy. Machine learning models YOLO and Mask-RCNN will analyze these images in three phases: pre-analytical, analytical, and post-analytical. A dataset of 322 labeled images, annotated by experts, is used to train these models. Data augmentation methods enhance model performance, and accuracy is assessed against radiographic results to confirm reliability.
Dental caries are chronic diseases that results in the destruction of the hard tooth tissues. It is a multifactorial condition that often goes undiagnosed, especially when it is hidden or in its initial stages. Detecting non-cavitated lesions is crucial for their early management. The standard visual-tactile inspection often fails to identify early lesions on hard-to-reach surfaces, such as proximal areas and beneath restorations. Detecting proximal caries early is crucial for implementing effective treatments and achieving optimal outcomes. A common supplementary method for detecting early lesions on proximal surfaces and assessing their extent is bitewing radiography. The routine diagnostic approach combines clinical examination with radiographic evaluation. To increase the detection rate of proximal secondary caries, experts recommend integrating visual and clinical examinations with bitewing radiography. Intraoral bitewing radiographs can be captured using either film or digital sensors, with preference for digital systems due to their benefits of reduced patient exposure, time savings, image enhancement, and ease of image storage, retrieval, and transmission. Although more sensitive for detecting early lesions than visual-tactile assessments, bitewing evaluations comes with significant variance between examiners and a considerable proportion of false-positive or false-negative detections. Recent literature has explored the use of artificial intelligence (AI), a field of computer science focused on developing machines capable of mimicking human cognitive abilities, as a diagnostic tool for detecting caries lesions using dental (digital radiographic) images. As AI technology advances, an increasing number of studies have examined the diagnostic performance of AI-based models, emphasizing the importance of creating reliable tools like AI to enhance the diagnostic process. Numerous studies have assessed the performance of AI models on diverse types of dental radiographs, with a significant focus on bitewing radiographs (BWR). AI has been used for various applications in oral and dental health, including the detection of dental caries, endodontic treatment and diagnosis, periodontal issues, and the detection of oral lesion pathology. A reference dataset of caries diagnoses from bitewing radiographs by different examiners created this benchmark which serves as a crucial tool for comparing the diagnostic performance of AI models against human examiners, emphasizing the potential improvements in accuracy and reliability that AI can bring to dental diagnostics.
Study Type
OBSERVATIONAL
Enrollment
322
machine learning model will used to detect secondary caries around restorations by comparing the results with digital bitewing radiography
two deep learning models, YOLO and Mask-RCNN, will be trained on this dataset to accurately detect and classify images showing signs of secondary caries
models will detect the presence or absence of secondary caries around restorations
Time frame: baseline
This platform is for informational purposes only and does not constitute medical advice. Always consult a qualified healthcare professional.