The innovation of AI-based software in oral diseases: clinical-histopathological correlation diagnostic accuracy primary study.

BACKGROUND: Machine learning (ML) through artificial intelligence (AI) could provide clinicians and oral pathologists to advance diagnostic problems in the field of potentially malignant lesions, oral cancer, periodontal diseases, salivary gland disease, oral infections, immune-mediated disease, and others. AI can detect micro-features beyond human eyes and provide solution in critical diagnostic cases. OBJECTIVE: The objective of this study was developing a software with all needed feeding data to act as AI-based program to diagnose oral diseases. So our research question was: Can we develop a Computer-Aided Software for accurate diagnosis of oral diseases based on clinical and histopathological data inputs? METHOD: The study sample included clinical images, patient symptoms, radiographic images, histopathological images and texts for the oral diseases of interest in the current study (premalignant lesions, oral cancer, salivary gland neoplasms, immune mediated oral mucosal lesions, oral reactive lesions) total oral diseases enrolled in this study was 28 diseases retrieved from the archives of oral maxillofacial pathology department. Total 11,200 texts and 3000 images (2800 images were used for training data to the program and 100 images were used as test data to the program and 100 cases for calculating accuracy, sensitivity& specificity). RESULTS: The correct diagnosis rates for group 1 (software users), group 2 (microscopic users) and group 3 (hybrid) were 87%, 90.6, 95% respectively. The reliability for inter-observer value was done by calculating Cronbach's alpha and interclass correlation coefficient. The test revealed for group 1, 2 and 3 the following values respectively 0.934, 0.712 & 0.703. All groups showed acceptable reliability especially for Diagnosis Oral Diseases Software (DODS) that revealed higher reliability value than other groups. However, The accuracy, sensitivity & specificity of this software was lower than those of oral pathologists (master's degree). CONCLUSION: The correct diagnosis rate of DODS was comparable to oral pathologists using standard microscopic examination. The DODS program could be utilized as diagnostic guidance tool with high reliability & accuracy.

TestDimorph: An R package for analysis of interpopulation sexual dimorphism differences using summary statistics.

OBJECTIVE: The degree of sexual dimorphism in certain traits between males and females differ from one sample to another. Although trait differences by sex are often reported in bioanthropological research, few studies test for statistical significance or make raw data available. TestDimorph is the first R package dedicated to testing and comparing the degree of sexual dimorphism among different samples by leveraging summary statistics. MATERIALS AND METHODS: We provide two approaches of analysis of inter-sample differences in degree of sexual dimorphism: univariate and multivariate for two or more samples. The methods follow upon publications primarily from the AJBA. Within-sex size variability between samples is compared using one-way ANOVA followed by control for multiple pairwise comparisons. In addition, we compute the overlapping area between the density functions of two normal distributions from the mixture intersection index or the non-overlapping area using the dissimilarity index as well as Hedges' g with inferential support using the 95% confidence interval. Finally, we use a multivariate analysis of differences in patterning of sexual dimorphism between samples. RESULTS: We demonstrate various results from applying TestDimorph functions to data supplied with the package. DISCUSSION: The package has many features including functionality for working with summary statistics, simulating data from summary statistics, and the extraction of summary statistics from raw data, so that the entire analysis can be performed through the package.