Effects of fetal position on the loading of the fetal brain during the onset of the second stage of labour

During vaginal labour, the delivery requires the fetal head to mould to accommodate the geometric constraints of the birth canal. Excessive moulding can produce brain injuries and long-term sequelae. Understanding the loading of the fetal brain during the second stage of labour (fully dilated cervix, active pushing, and expulsion of fetus) could thus help predict the safety of the newborn during vaginal delivery. To this end, this study proposes a finite element model of the fetal head and maternal canal environment that is capable of predicting the stresses experienced by the fetal brain at the onset of the second phase of labour. Both fetal and maternal models were adapted from existing studies to represent the geometry of full-term pregnancy. Two fetal positions were compared: left-occiput-anterior and left-occiput-posterior. The results demonstrate that left-occiput-anterior position reduces the maternal tissue deformation, at the cost of higher stress in the fetal brain. In both cases, stress is concentrated underneath the sutures, though the location varies depending on the presentation. In summary, this study provides a patient-specific simulation platform for the study of vaginal labour and its effect on both the fetal brain and maternal anatomy. Finally, it is suggested that such an approach has the potential to be used by obstetricians to support their decision-making processes through the simulation of various delivery scenarios

Tongue Disease Prediction Based on Machine Learning Algorithms

The diagnosis of tongue disease is based on the observation of various tongue characteristics, including color, shape, texture, and moisture, which indicate the patient’s health status. Tongue color is one such characteristic that plays a vital function in identifying diseases and the levels of progression of the ailment. With the development of computer vision systems, especially in the field of artificial intelligence, there has been important progress in acquiring, processing, and classifying tongue images. This study proposes a new imaging system to analyze and extract tongue color features at different color saturations and under different light conditions from five color space models (RGB, YcbCr, HSV, LAB, and YIQ). The proposed imaging system trained 5260 images classified with seven classes (red, yellow, green, blue, gray, white, and pink) using six machine learning algorithms, namely, the naïve Bayes (NB), support vector machine (SVM), k-nearest neighbors (KNN), decision trees (DTs), random forest (RF), and Extreme Gradient Boost (XGBoost) methods, to predict tongue color under any lighting conditions. The obtained results from the machine learning algorithms illustrated that XGBoost had the highest accuracy at 98.71%, while the NB algorithm had the lowest accuracy, with 91.43%. Based on these obtained results, the XGBoost algorithm was chosen as the classifier of the proposed imaging system and linked with a graphical user interface to predict tongue color and its related diseases in real time. Thus, this proposed imaging system opens the door for expanded tongue diagnosis within future point-of-care health systems

Computer vision for eye diseases detection using pre‐trained deep learning techniques and raspberry Pi

Abstract Early diagnosis of eye diseases is very important to prevent visual impairment and guide appropriate treatment methods. This paper presents a unique approach that can detect numerous eye diseases automatically. Initially, this approach used the pre‐trained ImageNet models that provides various pre‐trained models for training the acquired data. The existing data sets are composed of 645 data images acquired clinically, represented by two groups of subjects as healthy and others holding the proposed eye defect like cataracts, foreign bodies, glaucoma, subconjunctival haemorrhage, and viral conjunctivitis. Followed by comparisons of the pre‐trained model's coefficients and prediction performance. Later, the first‐class execution model is integrated within the Raspberry Pi staging and the real‐time digital camera detection. The evaluation process used the confusion matrix, model accuracy, precision factor, recall coefficient, F1 score, and the Matthews Correlation Coefficient (MCC). Resulting in the performance of these pre‐trained ImageNet models used in this study represented by 93% (InceptionResNetV2), 90% (MobileNet), 86% (Residual Network ResNet50), 85% (InceptionV3), 78% (Visual Geometry Group VGG19), and 72% (Neural Architecture Search Network NASNetMobile). The results show that the InceptionResNetV2 achieved the highest performance. This proposed approach shows its efficiency and strength by early detection of the subject's unhealthy eyes through real‐time monitoring in the field of ophthalmology

A coupled physical-computational methodology for the investigation of short fall related infant head impact injury

Head injury in childhood is the most common cause of death or permanent disability from injury. However, insufficient understanding exists of the response of a child’s head to injurious loading scenarios to establish cause and effect relationships to assist forensic and safetly investigations. Largely as a result of a lack of availability of paediatric clinical and Post-Mortem-Human-Surrogate (PMHS) experimental data, a new approach to infant head injury experimentation has been developed. A coupled-methodology, combining a physical infant head surrogate, producing “real world” global, regional and localised impact response data and a computational Finite-Element (FE-head) model was created and validated against available PMHS and physical model global impact response data. Experimental impact simulations were performed to investigate regional and localised injury vulnerability. Different regions of the head produced accelerations significantly greater than those calculated using the currently available method of measuring the global, whole head response. The majority of material strain was produced within the relatively elastic suture and fontanelle regions, rather than the skull bones. A subsequent parametric analysis was conducted to provide a correlation between fall height and areas of maximum-stress-response and fracture-risk-probability. The FE-head was further applied to investigating fracture risk, simulating injurious PMHS impacts and a good qualitative match was observed. The FE-head shows significant potential for the study of infant head injury and is anticipated to be a motivating tool for the improvement of head injury understanding across a range of potentially injurious head loading scenarios

Characterization of Infant Cardiopulmonary Resuscitation Delivery with Range Sensor Feedback on Performance

Cardiac arrest (CA) in infants is an issue worldwide, which causes significant morbidity and mortality rates. Cardiopulmonary resuscitation (CPR) is a technique performed in case of CA to save victims’ lives. However, CPR is often not performed effectively, even when delivered by qualified rescuers. Therefore, international guidelines have proposed applying a CPR feedback device to achieve high-quality application of CPR to enhance survival rates. Currently, no feedback device is available to guide learners through infant CPR performance in contrast to a number of adult CPR feedback devices. This study presents a real-time feedback system to improve infant CPR performance by medical staff and laypersons using a commercial CPR infant manikin. The proposed system uses an IR sensor to compare CPR performance obtained with no feedback and with a real-time feedback system. Performance was validated by analysis of the CPR parameters actually delivered against the recommended target parameters. Results show that the real-time feedback system significantly improves the quality of chest compression parameters. The two-thumb compression technique is the achievable and appropriate mechanism applied to infant subjects for delivering high-quality CPR. Under the social distancing constraints imposed by the SARS-CoV-2 pandemic, the results from the training device were sent to a CPR training center and provided each participant with CPR proficiency

Corrosion Behavior of Aluminium-Coated Cans

Hundreds of billions of aluminium-based cans are manufactured and used every year worldwide including those containing soft drinks. This study investigates and evaluates the performance and quality of two well-known energy and soft drinks brands, Green Cola and Red Bull. Recent health hazards and concerns have been associated with aluminium leakage and bisphenol A (BPA) dissociation from the can’s internal protective coating. The cans were examined under four conditions, including coated and uncoated samples, the soft drink’s main solution, and 0.1 M acetic acid solution. Electrochemical measurements such as potentiodynamic polarization and impedance spectroscopy (EIS), element analyses using inductively coupled plasma optical emission spectrometry (ICP-OES), and energy dispersive X-ray spectroscopy (EDS) were performed. In addition, sample characterization by scanning electron microscopy (SEM) and X-ray diffraction spectroscopy (XRD) were employed to comprehensively study and analyze the effect of corrosion on the samples. Even though the internal coating provided superior corrosion protection concerning main or acetic acid solutions, it failed to prevent aluminium from dissolving in the electrolyte. Green Cola’s primary solution appears to be extremely corrosive, as the corrosion rate increased by approximately 333% relative to the acetic acid solution. Uncoated samples resulted in increases in the percentage of oxygen, the appearance of more corrosion spots, and decreases in crystallinity. The ICP-OES test detected dangerous levels of aluminium in the Green Cola solution, which increased significantly after increasing the conductivity of the solution