NTU Journal of Engineering and Technology

Spondylolisthesis, a condition marked by vertebral slippage, presents a challenge in medical diagnosis and grading. This study examines previous research on image processing for spondylolisthesis severity evaluation. Methodologies, sample sizes, algorithms, and measurement accuracy are the main topics of interest. The study shows the potential of computer-assisted methods for diagnosing spondylolisthesis, particularly in situations where qualified medical personnel are scarce. Machine learning techniques and deep learning models, including convolutional neural networks (CNNs), are utilized to accurately detect and assess spondylolisthesis. Notably, these findings address a gap in previous research by measuring spondylolisthesis severity and distinguishing between normal and abnormal spines. The analysis emphasizes the significance of selecting the appropriate modality and data quality, with X-rays predominating as the preferred imaging technique. This review highlights how deep learning and machine learning models can improve spondylolisthesis diagnosis, enabling enhanced diagnosis and treatment methods.

Due to their growing popularity and rapid performance improvements, smartphones have the potential to be an accurate, low-cost physiological monitoring tool that is useful outside of the clinical setting. Since the cardiac pulse is responsible for the slight color changes in skin, a pulsatile signal that is also known as a photoplethysmographic (PPG) signal can be evaluated by utilizing a digital camera to record a video of your Region Of Interest (ROI).

This review Study focuses on several research works that have used PPG-based video smartphone camera methods to measure Heart Rate (HR), Blood Pressure (BP), Oxygen Saturation (SpO2), and Respiratory Rate (RR). Comparing the approaches and results of each study (HR measurement studies produced acceptable findings with error rates less than 5%, BP error rates of results less than 10%, and SpO2 =<3.5%, by comparing the predicted models reading with the standard references reading) shows how smartphone-based physiological monitoring might be used in medical settings.

Cone Beam Computed Tomography (CBCT) has emerged as a valuable imaging modality for various medical applications due to its ability to provide three-dimensional information with minimal radiation exposure. However, CBCT images often suffer from inherent limitations, such as increased noise, artifacts, and reduced spatial resolution. This paper presents a comprehensive review of image processing techniques employed to enhance the quality of CBCT images, addressing the challenges posed by acquisition hardware and image reconstruction algorithms. The review covers a range of preprocessing and post-processing methods, including denoising, artifact correction, and resolution improvement techniques. These methods encompass various mathematical algorithms, machine learning approaches, and hybrid models, which aim to mitigate the imperfections present in CBCT data while preserving diagnostically relevant information. Additionally, this paper discusses the application of deep learning methods, convolutional neural networks, and generative adversarial networks in CBCT image enhancement. These advanced techniques have shown promise in tackling the complex nature of CBCT data and optimizing image quality.

This study delves deeply into the various facets of bioimpedance analysis as it relates to the development of chronic kidney disease (CKD). The use of bioimpedance spectroscopy (BIS) to evaluate the effectiveness of peritoneal dialysis (PD) and determine dialysis adequacy using variables like volume (V) and Kt/V is at the heart of this compilation. The investigations closely look at how important water distribution is for the advancement of CKD and how that affects patient outcomes. Evaluation of several body composition measurement methods is emphasized, especially the combination of the bioelectrical impedance analysis (BIA) ratio and the dry mass index (DMI). By applying the BIA TBW Watson ratio, these approaches provide important insights into the relationships between fluid volume and physiological indicators, such as body mass index (BMI) and total body water (TBW). The study emphasizes the differences in fluid overload diagnosis between conventional clinical assessments and sophisticated bioimpedance techniques, particularly in older patients with chronic kidney disease (CKD), underscoring the need for proper monitoring and intervention.

Bioimpedance analysis is crucial to understanding chronic renal disease and associated cardiac complications, as shown in the assembled study. Bioimpedance analysis, used in devices like the Body Composition Monitor (BCM), is highlighted in this study collection to monitor hydration and improve dialysis adequacy.
Bioimpedance analysis improves illness treatment, giving chronic renal failure and heart failure patients a more precise and nuanced grasp of fluid management, according to the literature.

Health Apps Facilitate Rapid and Timely Response to COVID-19 Outbreak. There is, however, no site that gives a comprehensive overview of the various applications created to battle the epidemic. This paper aims to look at the features and usefulness of free mobile health applications accessible in the Google Play and App stores that were used during the COVID-19 epidemic. The article found 11 mobile applications which are Mawid, Tabaud, Tawakkalna, Sehha, Aarogua Setu, Immuni, COVID-19 Symptom, NHS COVID-19, COVID watch, and finally PathCheck. Different features are investigated such as contact tracing, personalized notes, educational resources, network tools (GPS and Bluetooth), security and safety. Various applications have been built for various activities such as contact tracking, appointment booking, and online consultation. Only a few applications, however, have integrated functions and features including self-assessment, consultation, assistance, and information access. The majority of the applications are supported with GPS and Bluetooth technology for monitoring and tracing people. There were no applications found with built-in social media functionalities. It is advised that most of the characteristics and functions examined in this study have been used to build and construct an integrated mobile health application.

Wireless localization technologies have undergone a paradigm shift with the advent of the emerging trend which is the Reconfigurable Intelligent Surfaces (RISs). This paper presents a comprehensive survey of the state-of-the-art RIS-aided localization techniques, exploring the transformative impact of intelligent surfaces on location-based services. The survey comprehensively reviews existing wireless localization methods, ranging from the localization techniques in the wireless communications generations. It addition the challenges posed by conventional localization methods are discussed, including accuracy and coverage issues. In response to these challenges, the incorporation of RIS is explored as a promising paradigm to enhance the precision and reliability of wireless localization. The core of the paper focuses on the integration of RIS into localization systems, highlighting how these RISs can be strategically deployed to enhance accuracy, mitigate multipath effects, integration in different propagation environment and solve non feasible localization problems. The survey encompasses a wide range of applications, including outdoor positioning, indoor positioning, and Internet of Things (IoT) devices, showcasing the versatility of RIS-aided localization across various scenarios. Also, critical design aspects are examined, including propagation regions, hardware requirements, deployment strategies, necessary measurements, multiplexing, and signalling systems.