“Every year, nearly a million people succumb to premature deaths that could be prevented, attributable to the consumption of smoked tobacco products like cigarettes, bidis, cigars, and hookah, along with smokeless varieties such as Gutkha, zarda, and khaini.   In India, where around 300 million people currently use hazardous tobacco products, including 200 million who consume smokeless tobacco, reducing tobacco consumption and the consequent fatalities from cancers and other preventable illnesses continues to be a top public health concern. The impact of smokeless tobacco is most evident in the oral cavity, leading to conditions such as submucosal fibrosis, leukoplakia, and oral cancers.

Oral cancer is a significant global health burden, with an estimated 377,713 new cases and 177,757 deaths worldwide in 2020 alone. Oral potentially malignant disorders (OPMDs) are a set of disorders that exhibit an increased risk of malignant transformation. These lesions present an array of clinical variations, including white, red, or mixed red-white lesions with verrucous, papillary, corrugated, atrophic, and ulcerated presentations. In addition, lesions like frictional keratosis, chemical injury, leukoedema, candidiasis, denture-associated stomatitis, and desquamative or autoimmune disorders exhibit overlapping clinical features, making the diagnosis of OPMDs challenging. Though oral cancers can develop de novo, OPMDs share numerous risk factors and molecular/genetic alterations with oral cancers. Studies indicate that most habit-associated oral cancers evolve from pre-existing OPMDs. Preliminary epidemiological research and systematic reviews report that 0.1 to 40% of leukoplakia develops into oral cancer. Hence, the early diagnosis and differentiation of OPMDs from clinically similar-appearing lesions are vital for limiting the possible malignant change and improving treatment outcomes.

 â€œPatients exhibiting oral lesions frequently have their initial consultations with general practitioners (GPs), both in medical and dental fields. This places GPs in a critical position to identify oral cancer in its early stages. However, research has highlighted a concerning gap in GPs’ knowledge and awareness regarding oral cancer diagnosis, particularly in recognizing its early signs. This lack of awareness is a major factor contributing to delayed referrals and treatment of oral cancer. A delay in diagnosis can lead to the need for more invasive treatments, significantly increasing the morbidity of oral functions such as speech, chewing, and swallowing. This not only affects the physical health of the patient but also profoundly impacts their quality of life”.

“The oral cavity is more readily accessible for examination without specialized instruments compared to other internal organs. A Conventional Oral Examination (COE), which includes a visual inspection by a healthcare professional, is the standard approach for identifying oral lesions. While clinical evaluation of oral potentially malignant diseases (OPMDs) is based on subjective assessment, biopsies are considered the definitive method for accurate diagnosis. However, in many low- and middle-income countries, access to specialists and adequate healthcare services is limited, leading to delayed diagnosis and referral for patients with OPMDs and oral cancer. On the other hand, while biopsy-based diagnoses are considered definitive, they are not ideal for large-scale screening due to their invasive nature, the requirement for expert analysis, and limited availability in point-of-care or remote areas. Additionally, biopsy diagnoses are subject to interpretive variability and inconsistencies, which can affect the objectivity of the results. Hence, it is crucial to create user-friendly, non-invasive oral screening tools that can augment the current system, offering more precise, rapid, and standardized diagnoses to enhance survival rates for oral cancer patients”.

Advances in artificial intelligence (AI) have introduced new possibilities for enhancing the accuracy and efficiency of medical diagnoses, including the early detection of oral cancer. AI-based tools, particularly those employing machine learning algorithms, have shown promise in analyzing medical images, identifying patterns, and predicting the presence of malignant and premalignant lesions with high precision.