Critical Reviews in Biomedical Engineering最新文献

Nailfold capillaroscopy is a tool which is non-invasive in nature and can be useful for diagnosis, research, therapeutic study and prognosis. Research shows that specific capillary morphology patterns are identified for diabetic subjects, hypertensive subjects and normal controls. In this study, we have proposed RATHEW approach of classifying these three classes of subjects. RATHEW approach employs a three step process for classifying nailfold images: one, identify six abnormality parameters from the image dataset; two, score these abnormality parameters based on the defined scoring rules; and three, combine them mathematically to segregate them into three classes. This technique can be further enhanced to grade the severity of disease and organ involvement. This can bring in a paradigm shift to the disease detection and therapeutic study mechanism.

The present-day healthcare system operates on a 4G network, where the data rate needed for many IoT devices is impossible. Also, the latency involved in the network does not support the use of many devices in the network. The 5G-based cellular technology promises an effective healthcare management system with high speed and low latency. The 5G communication technology will replace the 4G technology to satisfy the increasing demand for high data rates. It incorporates higher frequency bands of around 100 MHz using millimetre waves and broadband modulation schemes. It is aimed at providing low latency while supporting real-time machine-to-machine communication. It requires a more significant number of antennas, with an average base station density three times higher than 4G. However, the rise in circuit and processing power for multiple antennas and transceivers deteriorates energy efficiency. Also, the data transmission power for 5G is three times higher than for 4G technology. One of the advanced processors used in today's mobile equipment is NVIDIA Tegra, which has a multicore system on chip (SoC) architecture with two ARM Cortex CPU cores to handle audio, images, and video. The state-of-the-art software coding using JAVA or Python has achieved smooth data transmission from mobile equipment, desktop or laptop through the internet with the support of 5G communication technology. This paper discusses some key areas related to 5G-based healthcare systems such as the architecture, antenna designs, power consumption, file protocols, security, and health implications of 5G networks.

Cardiovascular disease (CVD) has become the most serious health concern in India and globally. The cost of treatment for CVD is very high and in a country like India, where most of the population belongs to rural area, affording treatment is not possible. Diagnosis and treatment are further hampered due to shortage of medical expertise as well as the unavailability of the wearable device. This makes the condition worst in rural areas. As a result of delay in diagnosis, patients do not receive appropriate treatment on time, thus risking lives. Hence, early detection of physiological abnormalities in patients is the best solution to avoid sudden death. In India, the majority of ECG diagnosis is done using a standard ECG machine or Holter monitor, which are not adequate to detect transient or infrequent arrhythmia as the window of detection is 30 s or up to 48 h. So, for arrhythmia diagnosis or syncope and palpitation, external cardiac loop recorder (ECLR) is preferred. ECLR is a monitoring device which records cardiac activities and detects infrequent arrhythmias with syncope and palpitation of a subject for longer period continuously. Due to recent improvements in technology, such as flexible electronics and wireless body area network (WBAN), wearable medical devices are progressively assisting people to monitor their health status while doing their day-to-day activities and furnishing more information to clinicians for early diagnosis and treatment. Flexible electronics allows to develop an electronic circuit on a flexible substrate hence making the device bendable and stretchable. WBAN is a wireless communication between different nodes like sensors and processors that are located at different points on the body. By incorporating technologies such as miniaturization of electronics, making flexible electronics and WBAN concept in ECLR, the device can be made wearable so as to not interfere with the patient's day-to-day activities. This review paper discusses the limitations of existing standard ECG machines as well as how to make the existing ECLR devices more robust, more advanced, more comfortable and also affordable.

One of the key echographic signs of focal pathology of the pancreas is the presence of formation contours and their nature. Endoscopic ultrasonography has a unique ability to visualize the echographic texture of the pancreatic parenchyma, and also allows you to assess in detail the boundaries and nature of the contours of the tumor formations of the organ due to the proximity of the ultrasound sensor. However, the differential diagnosis of focal pancreatic lesions remains a difficult clinical task due to the similarity of their echosemiotics. One of the ways to objectify and improve the accuracy of ultrasound data is the use of artificial intelligence methods for interpreting images. Improving the quality of differential diagnosis of focal pathology of the pancreas according to endoscopic ultrasonography based on the analysis of the nature of the contours of focal formations using fuzzy mathematical models.

This work aims at improving the quality of health assessments, specifically under the influence of occupational risk factors. For this purpose, additional informative indicators are utilized in prognostic and diagnostic models. The models are used to characterize the level of body protection based on oxidative status. A quantitative method is proposed to assess the body's level of protection by means of the levels of lipid peroxidation and antioxidant activity, which characterize the body's oxidative status. A mechanism is developed for integrating the proposed method into prognostic and diagnostic decision rules. The developed rules are in the form of mathematical models used to synthesize hybrid fuzzy decision rules, which are then used to quantify the level of body protection (LBP) against external risk factors, based on the use of protection level functions in terms of lipid peroxidation and antioxidant activity. A mechanism for embedding LBP into predictive and diagnostic decision rules has been proposed. The proposed method is used to predict the occurrence and development of coronary heart disease in railroad locomotive drivers. It was found that to improve the predicting and diagnosing of diseases caused by external pathogenic factors, quantitative assessments of LBP, determined by oxidative status, can be implemented. It has been established that the use of the protection level indicator in predictive decision rules makes it possible to increase the efficiency of the prediction while simultaneously increasing its accuracy.

Congenital heart disease (CHD) is the most widely occurring congenital defect and accounts to about 28% of the overall congenital defects. Analysis of the development of the fetal heart thus plays an important role for detection of abnormality in early stages and to take corrective measures. Cardiac chamber analysis is one of the important diagnosing methods. Segmentation of the cardiac chambers must be done appropriately to avoid false interpretations. Effective segmentation of fetal ventricular chambers is a challenging task as the speckle noise inherent in ultrasound images cause blurring of the boundaries of anatomical structures. Several segmentation techniques have been proposed for extracting the fetal cardiac chambers. This article discusses the performance evaluation of automated, probability based segmentation approach, and Markov random field (MRF) for segmenting the fetal ventricular chambers of ultrasonic cineloop sequences. 837 ultrasonic biometery sequences of various gestations were collected from local diagnostic center after due ethical clearance and used for the study. In order to assess the efficiency of the segmentation technique, four metrics such as dice coefficient, true positive ratio (TPR), false positive ratio (FPR), similarity ratio (SIR), and precision (PR) were used. In order to perform ground truth validation, 56% of the data used in this study were annotated by clinical experts. The automated segmentation yielded comparable results with manual annotation. The technique results in average value of 0.68 for Dice coefficient, 0.723 for TPR, 0.604 for SIR, and 0.632 for PR.

Medical images are providing vital information to aid physicians in diagnosing a disease afflicting the organ of a human body. Magnetic resonance imaging is an important imaging modality in capturing the soft tissues of the brain. Segmenting and extracting the brain is essential in studying the structure and pathological condition of brain. There are several methods that are developed for this purpose. Researchers in brain extraction or segmentation need to know the current status of the work that have been done. Such an information is also important for improving the existing method to get more accurate results or to reduce the complexity of the algorithm. In this paper we review the classical methods and convolutional neural network-based deep learning brain extraction methods.