Enhancing Early Oral Disease Detection Through Custom Multispectral Light-emitting Diode Imaging: A Noninvasive Digital Approach for Remote Dental Diagnostics

Abstract

Oral health remains a critical yet often neglected aspect of general well-being, with a significant percentage of the global population lacking access to early-stage dental diagnostics. In India, approximately 67% (924 million) of the population have never visited a dentist, and 87% (1.2 billion) seek dental care only when symptoms become severe. Compared to a global survey, 27% (2.2 billion) of the world's population visit a dentist only once a year. Current dental screening methods rely on in-clinic examinations, which can be costly and inaccessible to many. Advancements in digital imaging and sensor technology offer promising opportunities to enhance early detection and preventive care through noninvasive and cost-effective approaches. Oral conditions such as dental caries (54%—750 million), gingivitis (51%—708 million), and periodontitis (50%—700 million)—if diagnosed in the early stages, can be managed without disease progression using various preventive methods. This study proposes a custom-built multispectral light-emitting diode (LED) prototype system combined with a mobile camera for the early detection of oral conditions. The research explores the feasibility of this device in imaging and diagnosing various dental diseases, including caries, plaque, calculus, and oral lesions, using image processing techniques in matlab. A dataset of 1000 clinical images sourced from private clinics was analyzed using expert diagnoses and algorithmic classification, achieving a diagnostic accuracy of up to 90%. The results demonstrate that multispectral LED imaging enhances disease visualization compared to standard white-light images, facilitating improved detection and classification of dental conditions. The findings suggest that this novel LED-based imaging system could serve as a viable tool for at-home dental screenings, reducing the burden on clinical facilities and promoting proactive oral healthcare. The research also highlights the potential integration of artificial intelligence (AI) for automated diagnosis, paving the way for scalable, technology-driven solutions in preventive dentistry.