The aim of this study is to investigate the estimation of density from the Hounsfield unit of cone beam computed tomography data in dental imaging, especially for dental implant application. A jaw phantom with various known densities of anatomical parts (e.g. soft tissue, cortical bone, trabecular bone, tooth enamel, tooth dentin, sinus cavity, spinal cord and spinal disc) has been used to test the accuracy of the Hounsfield unit of cone beam computed tomography in estimating the mechanical density (true density). The Hounsfield unit of cone beam computed tomography data was evaluated via the MIMICS software using both two-dimensional and three-dimensional methods, and the results showed correlation with the true density of the object. In addition, the results revealed that the Hounsfield unit of cone beam computed tomography and bone density had a logarithmic relation, rather than a linear one. To this end, the correlation coefficient of logarithmic correlation (R² = 0.95) is higher than the linear one (R² = 0.77).

This study presents a comparative analysis of tumor growth models based on logistic, exponential, and Gompertz formulations. Their response to therapeutic intervention is examined to identify which model shows better behavior with minimal decline of immune cells. The framework incorporates three main cell populations as follows: natural killer cells, cytotoxic T cells, and tumor cells, along with treatment effects. Dynamical properties such as positive invariance, existence, boundedness, and equilibrium stability are investigated. Numerical simulations indicate that the logistic model gives more favorable treatment outcomes compared to the exponential and Gompertz models. The results also show a faster decline of immune cell populations in the exponential and Gompertz models than in the logistic model under varying drug flux.

Machine learning (ML) and artificial intelligence (AI) are widely applied in many disciplines including medicine. Pattern recognition or automatization has been successfully implemented in various field studies. Similarly, multiple efforts have been made in medicine to implement AI/ML technology to solve medical problems, for example, for automating osteoporosis detection. In general, the success of AI/ML technology is highly dependent on the amount of available data, especially during the training stage. Feature generation is a common technique that allows the manipulation of available data for the training stages. This paper aims to study the feasibility of adopting signal-processing techniques for feature generation in medical image processing. Signal attributes from signal processing workflow were adopted and applied to image processing of CT and DEXA scanning data to differentiate between normal and osteoporotic bone. Five attributes, namely amplitude, frequency, instantaneous phase, roughness, and first derivative or contrast attributes, have been tested. An attribute index number is formulated to indicate the attribute’s strength at the selected region of interest (ROI). A case study applying these attributes to the CNN model is presented. More than five hundred CT scan images of normal and osteoporosis bone were used during the training stage to test classification performance with and without developed attributes as an input. From the ten selected CT scan images used to test the CNN model, 90% were well predicted in the scenario only utilizing the grayscale as input. However, when including the developed attributes, the CNN can predict all the images well (100% were well predicted). In conclusion, the technique adopted from the signal-processing technique has the potential to enhance feature generation in image processing, whereby the results can be used for the early application of AI/ML in osteoporosis identification. Further research testing this proposed method in different image modalities needs to be conducted to verify the robustness of the proposed method.

Introduction: Cleft lip and palate (CLP) are the most common congenital craniofacial deformities that can cause a variety of dental abnormalities in children. The purpose of this study was to predict the maxillary arch growth and to develop a neural network logistic regression model for both UCLP and non-UCLP individuals. Methods: This study utilizes a novel method incorporating many approaches, such as the bootstrap method, a multi-layer feed-forward neural network, and ordinal logistic regression. A dataset was created based on the following factors: socio-demographic characteristics such as age and gender, as well as cleft type and category of malocclusion associated with the cleft. Training data were used to create a model, whereas testing data were used to validate it. The study is separated into two phases: phase one involves the use of a multilayer neural network and phase two involves the use of an ordinal logistic regression model to analyze the underlying association between cleft and the factors chosen. Results: The findings of the hybrid technique using ordinal logistic regression are discussed, where category acts as both a dependent variable and as the study’s output. The ordinal logistic regression was used to classify the dependent variables into three categories. The suggested technique performs exceptionally well, as evidenced by a Predicted Mean Square Error (PMSE) of 2.03%. Conclusion: The outcome of the study suggests that there is a strong association between gender, age, and cleft. The difference in width and length of the maxillary arch in UCLP is mainly related to the severity of the cleft and facial growth pattern.

Osteoporosis is a silent degenerative disease that commonly affects the elderly, leading to decreased bone density and increased risk of fractures. Dual-Energy X-ray Absorptiometry (DEXA) is the standard tool for assessing Bone Mineral Density (BMD), but its limited availability and high cost in many healthcare facilities, especially in developing countries like Indonesia, necessitate alternative diagnostic methods. This study aims to assess the potential use of Computed Tomography (CT) scan images as a substitute for DEXA in detecting osteoporosis. The objective is to analyze bone texture attributes from CT images to differentiate between normal and osteoporotic bone structures. The method involved collecting CT scan data from patients at Hospital Universiti Sains Malaysia (HUSM). The vertebrae regions were segmented from the abdominal CT images using image processing techniques to isolate bone tissue. Four image-derived texture attributes—Roughness, Contrast, Greyscale, and Phase—were then extracted using MATLAB-based analysis. The results showed that osteoporotic bones had higher values in Roughness and Contrast, and lower values in Greyscale and Phase compared to normal bones. Among these, Roughness was identified as the most sensitive attribute in detecting changes associated with osteoporosis. These findings indicate that CT scan images, when analyzed through proper segmentation and texture evaluation, have the potential to serve as a viable alternative for osteoporosis screening, particularly in settings where DEXA is unavailable.