Over the past decades, laser use in medicine has expanded from its initial application as a light-based scalpel to a plethora of clinical uses, ranging from surgical treatment through composite polymerization, dental ablation, vision correction, and skin resurfacing to diverse diagnostic modalities. Recently, the concept of light-based diagnostics and therapy has come under investigation. Low light intensities are used to excite endogenous or exogenous fluorophores, some of which have characteristic fluorescence emissions in pathological tissues. Thus, premalignancy and malignancy potentially can be detected and diagnosed. Photosensitized superficial lesions can subsequently be destroyed selectively by using higher intensities of laser light. The application of fluorescence emission-based detection and diagnosis of precancer and cancer is reviewed, based on its application to the oral cavity-the author's primary anatomical area of expertise. This approach is justified as the same principles apply throughout the human body; to any area accessible to the clinician either directly or by some sort of fiber-optic probe.

Early detection of oral cancer in low-resource settings necessitates a Point-of-Care screening tool that empowers Frontline-Health-Workers (FHW). This study was conducted to validate the accuracy of Convolutional-Neural-Network (CNN) enabled m(mobile)-Health device deployed with FHWs for delineation of suspicious oral lesions (malignant/potentially-malignant disorders). The effectiveness of the device was tested in tertiary-care hospitals and low-resource settings in India. The subjects were screened independently, either by FHWs alone or along with specialists. All the subjects were also remotely evaluated by oral cancer specialist/s. The program screened 5025 subjects (Images: 32,128) with 95% (n = 4728) having telediagnosis. Among the 16% (n = 752) assessed by onsite specialists, 20% (n = 102) underwent biopsy. Simple and complex CNN were integrated into the mobile phone and cloud respectively. The onsite specialist diagnosis showed a high sensitivity (94%), when compared to histology, while telediagnosis showed high accuracy in comparison with onsite specialists (sensitivity: 95%; specificity: 84%). FHWs, however, when compared with telediagnosis, identified suspicious lesions with less sensitivity (60%). Phone integrated, CNN (MobileNet) accurately delineated lesions (n = 1416; sensitivity: 82%) and Cloud-based CNN (VGG19) had higher accuracy (sensitivity: 87%) with tele-diagnosis as reference standard. The results of the study suggest that an automated mHealth-enabled, dual-image system is a useful triaging tool and empowers FHWs for oral cancer screening in low-resource settings.