Prostate cancer (PCa) is a major contributor to cancer mortality in the male population globally, and precise classification of PCa into clinically significant (CS) and clinically insignificant (CiS) is essential for selecting suitable therapeutic approaches. Multiparametric magnetic resonance imaging (mpMRI) has proven to be an indispensable technique for detecting and characterising PCa lesions. This study proposes an effective framework to classify PCa using transfer learning with pre-trained VGG19 and Vision Transformer (ViT) models for feature extraction from nine mpMRI sequences of the PROSTATEx dataset, followed by feature fusion and support vector machine (SVM) classification. An ensemble approach combining multiple outputs is employed to obtain the final classification result. The proposed framework demonstrated promising results, with the ensemble output achieving an AUC score of 0.85. Key findings include the importance of incorporating higher b-values in DWI sequences and the effectiveness of combining CNN-based and transformer-based features extracted by VGG19 and ViT models. The framework has the potential to support radiologists in accurate and efficient PCa diagnosis by differentiating between CS and CiS lesions.

Faulty mandible implant designs with stress concentration regions result in failure in cyclic loading during daily activities. Difficulties in biomechanical evaluation of the mandible arise from multi-bellied muscle groups. This study aimed to identify and characterise critical stress concentration regions in an intact mandible, and to study the effects of combining masticatory muscle sub-groups on these critical regions. A finite element analysis was conducted on an intact mandible subjected to various mastication and punching tasks. Jaw-closing muscles were simulated in four configurations, either isolating or combining the masseter and temporalis sub-groups. Critical mandible regions were sought based on stress values exceeding the fatigue shear strength and yield strength of cortical bone. Maximum shear stress was less than the critical stress value for all mastication tasks. For tasks constituting the alternating bilateral mastication pattern, maximum shear stress regions were predominantly observed on the mandible body; whereas, condylar necks were critical during fist punching. This implies avoiding stress concentration features in the mandibular body and condylar neck when designing mandibular implants. Different muscle configurations resulted in similar magnitudes (variations < 10%) and regions of critical stress, which supports combining muscle sub-groups for quasi-static computational and experimental testing of the mandible.

It is a well-known fact that studying entropies in physiological systems, like the cardiovascular system, to quantify dynamic complexity is a well-established field that aids in autonomous health condition evaluation, reducing the need for continuous expert monitoring. Pathological cardiac conditions such as Congestive Heart Failure (CHF) patients and Atrial Fibrillation (AFib) often pose a risk of co-occurrence, which can be lethal. The primary goal of this study is to collectively analyse the symbolic dynamics of human heart rhythms obtained from normal (healthy), CHF, and AFib subjects, using Normalised Corrected Shannon Entropy (NCSE). This study performs joint differentiation of CHF and AFib conditions, instead of performing individual detection as done in previous studies. Initially, the RR time series data, both filtered and raw, from ECGs of 15 subjects are symbolised and converted into series of codes. These code series are further evaluated to compute absolute and mean NCSE values across different thresholds and word lengths. In all the scenarios, the NCSE outcomes showed significant differences between normal and diseased cardiovascular systems, with AFib exhibiting the highest entropy, followed by normal and CHF systems. The results of NCSE and Shannon entropy (SE) were compared and it was observed that NCSE demonstrated superior performance in separating the three conditions, as well as in executing joint detection. The NCSE results have also been statistically analysed and validated using surrogate data analysis, one-way ANOVA and pairwise t-tests. Furthermore, this approach did not require any classification algorithms or noise cancellation methods, indicating robustness to noise.

Deep learning’s swift development has generated substantial excitement about its application in medical imaging. Machine learning (ML) methods can support radiologists in diagnosing breast cancer (BC) without resorting to invasive procedures. However, traditional ML classifiers require the extraction of detailed hand-crafted features, which is a time-intensive task to achieve accurate results. Hence, this paper proposes a novel Feature-driven Breast Cancer Classification using the Modified Loss and Activation function-assisted LeNet (MLAL) model, named F-BCC-ML. The process of detecting BC using mammogram images comprises several key stages. In the first step, the image undergoes enhancement using the Improved Bilateral Filtering Technique (IBFT), which reduces the noise while conserving critical structural details like edges. Next, the image is subjected to segmentation using SegNet, a deep-learning model designed for semantic segmentation. After segmentation, the next phase is feature extraction, where various features like Weber Local descriptor assisted Local Gabor XOR Pattern (WLD-LGXP) for texture analysis, Median Binary Pattern (MBP), colour features, and deep features are derived from the segmented image. Once the features are extracted, they are fed into the classification stage, where the Modified Loss and Activation function assisted LeNet (MLAL) model, more sophisticated Deep Convolutional Neural Network (DCNN) are used to classify the image as either normal or cancerous. The result is a prediction that indicates whether the breast tissue is benign or shows signs of cancer, helping radiologists make more accurate and informed decisions. The MLAL+DCNN accomplished the maximum accuracy of 0.936, precision of 0.947 and F-measure of 0.942, respectively.

Objective Diabetic Peripheral Neuropathy (DPN) is the most common prolonged complication of diabetes. A nerve reaches to the hands, legs, and a foot is damaged due to excessive glucose level. This leads to the loss of sensation, numbness and pain in the feet, legs or hands. Currently available devices are expensive, take more time and need more expertise to operate them to detect the level of DPN. This study is designed to detect the level of diabetic peripheral neuropathy (DPN) from first joint of index finger using a novel 128-Hz electronic tuning fork prototype which is capable of performing accurate vibration perception duration (VPD). Methods A total of 169 diabetic patients were recruited from the secondary author’s practice for assessment of level of DPN with our device. All the patients were enrolled according to an approved protocol. Patient places index finger on the tip of our device in such a way that the tip covers the first joint of index finger. Our device then provides the vibration of desired frequency and voltage to the index finger via tactile platform and patient starts feeling the vibration. Depending on the vibration perception duration (VPD) for which the patient feels the vibration, 4 levels of DPN i.e. Normal, Mild, Moderate and Severe are calculated. Three repeated measurements were taken from all 169 patients. Results Our device detected 74 DPN patients (6 severe, 26 moderates, 42 mild) and 89 normal (no DPN) patients. The mean of vibration perception duration (VPD) was 6.8 s, with a standard deviation (SD) of ± 0.84 s of all 169 patients. Mean VPD of severe, moderate, mild and normal level of DPN patients was 1.73 (mean SD = 0.7 s), 5.82 (mean SD = 0.84 s), 8.32 (mean SD = 1 s) and 11.3 s (mean SD = 0.84 s), respectively. Considering the Biothesiometer as the reference standard, our results were compared against it and our device’s result accuracy was > 92%. Conclusion VPD was a sensitive measure of a detection of level of DPN. The device is compact, handy, easy to use and takes only few seconds to diagnose the level of DPN level in diabetic patients

This review article delves into the cutting-edge realm of 3D printing and its impact on the fabrication of customised orthodontic retainers, which is an essential utility in the prevention of relapse post orthodontic treatment. This review evaluates the use of biocompatible materials and provides insight into future perspectives and improvements in this field. It highlights the potential of data collecting method and 3D printing to improve orthodontic retainers’ fabrication and emphasises the importance of using biocompatible materials for patient safety and efficacy. It also explains cytotoxic qualities of retainer fabrication materials, which are vital for safeguarding the oral health of the patient. The evaluation procedure enables the early diagnosis and correction of any potential difficulties, such as maladjustment or inappropriate fit, allowing for a more effective treatment. It illustrates the breakthroughs and innovations in the field of orthodontics, the advantages of 3D printing over conventional methods, as well as the advantages and disadvantages of various fabrication method. Incorporating 3D printing and review into the production of orthodontic retainers enhances the overall effectiveness and efficiency of patient treatment.