ABSTRACT
Background Dental behavior management problems of children towards preventive dental care at school dental camps in India remain largely undocumented. This study aimed to assess such behavior patterns in preschool and school-age children at a school dental health camp. Materials and methods The cross-sectional study included 462 children, with 261 children each in the preschool (three to five years old) and school (six to 12 years old) age groups in Bengaluru. On the school dental camp day, their behavior and anxiety were gauged using the Frankl Behavior Rating Scale and the Raghavendra, Madhuri, and Sujata Pictorial Scale, respectively. The Chi-square test was used to uncover predictive variables for children's behavior patterns toward preventive dental procedures at the dental school camps. Results A high prevalence of definitely negative Frankl Behavior Rating Scale ratings (59%, n=272) and dental anxiety (53%, n=245) were noted among the participants. Age, sex, the area of residence of the child, and the previous history of dental visits and treatment were predictors of their behavior at a school dental camp setup. Conclusion The present study gives an insight into the behavior of children towards preventive dental care at a school dental camp in a mobile dental van, stressing the need for behavior assessment before the treatment.
ABSTRACT
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.
