Machine learning in the detection of dental cyst, tumor, and abscess lesions.

BACKGROUND AND OBJECTIVE: Dental panoramic radiographs are utilized in computer-aided image analysis, which detects abnormal tissue masses by analyzing the produced image capacity to recognize patterns of intensity fluctuations. This is done to reduce the need for invasive biopsies for arriving to a diagnosis. The aim of the current study was to examine and compare the accuracy of several texture analysis techniques, such as Grey Level Run Length Matrix (GLRLM), Grey Level Co-occurrence Matrix (GLCM), and wavelet analysis in recognizing dental cyst, tumor, and abscess lesions. MATERIALS & METHODS: The current retrospective study retrieved a total of 172 dental panoramic radiographs with lesion including dental cysts, tumors, or abscess. Radiographs that failed to meet technical criteria for diagnostic quality (such as significant overlap of teeth, a diffuse image, or distortion) were excluded from the sample. The methodology adopted in the study comprised of five stages. At first, the radiographs are improved, and the area of interest was segmented manually. A variety of feature extraction techniques, such GLCM, GLRLM, and the wavelet analysis were used to gather information from the area of interest. Later, the lesions were classified as a cyst, tumor, abscess, or using a support vector machine (SVM) classifier. Eventually, the data was transferred into a Microsoft Excel spreadsheet and statistical package for social sciences (SPSS) (version 21) was used to conduct the statistical analysis. Initially descriptive statistics were computed. For inferential analysis, statistical significance was determined by a p value < 0.05. The sensitivity, specificity, and accuracy were used to find the significant difference between assessed and actual diagnosis. RESULTS: The findings demonstrate that 98% accuracy was achieved using GLCM, 91% accuracy using Wavelet analysis & 95% accuracy using GLRLM in distinguishing between dental cyst, tumor, and abscess lesions. The area under curve (AUC) number indicates that GLCM achieves a high degree of accuracy. The results achieved excellent accuracy (98%) using GLCM. CONCLUSION: The GLCM features can be used for further research. After improving the performance and training, it can support routine histological diagnosis and can assist the clinicians in arriving at accurate and spontaneous treatment plans.

Assessment of periodontal status among dental fluorosis subjects using community periodontal index of treatment needs.

AIM: To determine the periodontal status and treatment needs among dental fluorosis subjects residing in Ennore, Chennai, using Community Periodontal Index of Treatment Needs (CPITN). MATERIALS AND METHODS: All the subjects with dental fluorosis above 15 years of age, permanent residents of Ennore, were included in the study. Subjects with known systemic diseases and subjects with other intrinsic dental stains were excluded from the study. Periodontal status was estimated using CPITN and Dental fluorosis was recorded using Dean's Dental Fluorosis Index. RESULTS: The total number of study subjects was 1075, of which 489 were males and 586 were females. Males were predominantly affected with periodontal disease than females. This was found to be statistically significant (P=0.000). The association between Degree of Fluorosis and Periodontal Status is statistically significant (P=0.000). There was statistically significant difference in mean number of sextants between the degree of fluorosis in each of the periodontal status (P=0.000). CONCLUSION: The finding that the lower prevalence of shallow pockets in the study area, where the fluoride level in the drinking water ranges from 1.83 to 2.01 ppm, indicates that the use of fluoride in water is beneficial to the periodontal tissues.