Temporomandibular dysfunctions are a heterogeneous group of conditions involving the temporomandibular joints (TMJs) and periarticular musculoskeletal structures. This study aimed to evaluate the effectiveness of a physiotherapy program for TMJ dysfunctions and the relationship with cervical spine. The study design was a non-randomized clinical trial with two parallel treatment groups: 33 subjects in the experimental group that underwent conservative drug treatment and physiotherapy treatment, and 31 subjects in the control group that underwent only conservative drug treatment. The participants were examined at baseline and re-examined after 3 months. In this study there was a higher incidence of female subjects. After 3 months of treatment of the TMJs and cervical spine, pain decreased in both groups (p = 0001). Muscle testing at the cervical spine and temporomandibular level showed a decrease in pain and muscles spasms. The average percentage values of the Neck Disability Index (NDI) and the Jaw Functional Limitation Scale 8 (JFLS 8) decreased significantly in both groups, but especially in the experimental group (p = 0.001). Physiotherapy treatments could maintain the functional state at the temporomandibular and cervical levels, thus contributing to increasing the quality of daily life.

One of the deadliest diseases is skin cancer, especially melanoma. The high resemblance between different skin lesions such as melanoma and nevus in the skin colour images increases the complexity of identification and diagnosis. An efficient automated early detection system for skin cancer detection is essential in order to save human lives, time, and effort. In this article, an automatic skin lesion classification system using a pretrained deep learning network and transfer learning was proposed. Here, diagnosing melanoma in premature stages, a detection system has been designed which contains the following digital image processing techniques. First, dermoscopy images of skin were taken and this is subjected to a preprocessing step for noise removal and postprocessing step for image enhancement. Then the processed image undergoes image segmentation using k-means and modified k-means clustering. Second, using feature extraction technology, Gray Level Co-occurrence Matrix, and first order statistics, characteristics are extracted. Features are selected on the basis of Harris Hawks optimization (HHO). Finally, various classifiers are used for predicting the stages and efficiency of the proposed work. Measures of well-known quantities, sensitivity, precision, accuracy, and specificity are used in assessing the efficiency of the suggested method, where higher values were obtained. Compared to the current methods, it is found that the classification rate exceeded the output of the current approaches in the performance of the proposed approach.

This study aims to introduce a resistance training protocol (6 repetitions × 70% of 1 maximum repetition (1RM), followed by 6 repetitions × 50% of 1RM within the same set) specifically designed for postmenopausal women with osteopenia/osteoporosis and monitor the effect of the protocol on bone mineral density (BMD) in the lumbar spine, assessed by dual-energy X-ray absorptiometry (DEXA). The subjects included in the study were 29 postmenopausal women (56.5 ± 2.8 years) with osteopenia or osteoporosis; they were separated into two groups: the experimental group (n = 15), in which the subjects participated in the strength training protocol for a period of 6 months; and the control group (n = 14), in which the subjects did not take part in any physical activity. BMD in the lumbar spine was measured by DEXA. The measurements were performed at the beginning and end of the study. A statistically significant increase (Δ% = 1.82%) in BMD was observed at the end of the study for the exercise group (0.778 ± 0.042 at baseline vs. 0.792 ± 0.046 after 6 months, p = 0.018, 95% CI [−0.025, −0.003]); while an increase was observed for the control group (Δ% = 0.14%), the difference was not statistically significant (0.762 ± 0.057 at baseline vs. 0.763 ± 0.059, p = 0.85, 95% CI [−0.013, 0.011]). In conclusion, our strength training protocol seems to be effective in increasing BMD among women with osteopenia/osteoporosis and represents an affordable strategy for preventing future bone loss.

Modalities like MRI give information about organs and highlight diseases. Organ information is visualized in intensities. The segmentation method plays an important role in the identification of the region of interest (ROI). The ROI can be segmented from the image using clustering, features, and region extraction. Segmentation can be performed in steps; firstly, the region is extracted from the image. Secondly, feature extraction performed, and better features are selected. They can be shape, texture, or intensity. Thirdly, clustering segments the shape of tumor, tumor has specified shape, and shape is detected by feature. Clustering consists of FCM, K‐means, FKM, and their hybrid. To support the segmentation, we conducted three studies (region extraction, feature, and clustering) which are discussed in the first line of this review paper. All these studies are targeting MRI as a modality. MRI visualization proved to be more accurate for the identification of diseases compared with other modalities. Information of the modality is compromised due to low pass image. In MRI Images, the tumor intensities are variable in tumor areas as well as in tumor boundaries.

A pneumothorax is a condition that occurs in the lung region when air enters the pleural space—the area between the lung and chest wall—causing the lung to collapse and making it difficult to breathe. This can happen spontaneously or as a result of an injury. The symptoms of a pneumothorax may include chest pain, shortness of breath, and rapid breathing. Although chest X-rays are commonly used to detect a pneumothorax, locating the affected area visually in X-ray images can be time-consuming and prone to errors. Existing computer technology for detecting this disease from X-rays is limited by three major issues, including class disparity, which causes overfitting, difficulty in detecting dark portions of the images, and vanishing gradient. To address these issues, we propose an ensemble deep learning model called PneumoNet, which uses synthetic images from data augmentation to address the class disparity issue and a segmentation system to identify dark areas. Finally, the issue of the vanishing gradient, which becomes very small during back propagation, can be addressed by hyperparameter optimization techniques that prevent the model from slowly converging and poorly performing. Our model achieved an accuracy of 98.41% on the Society for Imaging Informatics in Medicine pneumothorax dataset, outperforming other deep learning models and reducing the computation complexities in detecting the disease.

Identifying patterns, recognition systems, prediction methods, and detection methods is a major challenge in solving different medical issues. Few categories of devices for personal and professional assessment of body composition are available. Bioelectrical impedance analyzer is a simple, safe, affordable, mobile, non-invasive, and less expensive alternative device for body composition assessment. Identifying the body composition pattern of different groups with varying age and gender is a major challenge in defining an optimal level because of the body shape, body mass, energy requirements, physical fitness, health status, and metabolic profile. Thus, this research aims to identify the statistical medical pattern recognition of body composition data by using a bioelectrical impedance analyzer. In previous studies, a pattern was identified for four indicators that concern body composition (e.g., body mass index (BMI), body fat, muscle mass, and total body water). The novelty of our study is the fact that we identified a recognition pattern by using medical statistical methods for a body composition that contains seven indicators (e.g., body fat, visceral fat, BMI, muscle mass, skeletal muscle mass, sarcopenic index, and total body water). The youth that exhibited the body composition pattern identified in our study could be considered healthy. Every deviation of one or more parameters outside the margins of the pattern for body composition could be associated with health issues, and more medical investigations would be needed for a diagnosis. BIA is considered a valid and reliable device to assess body composition along with medical statistical methods to identify a pattern for body composition according to the age, gender, and other relevant parameters.