Asian Journal Of Medical Technology

INVESTIGATING LENS ELASTICITY AND ITS CORRELATION WITH REFRACTIVE ERRORS AND BIOMETRIC PARAMETERS ACROSS AGE GROUPS USING ULTRASONIC ELASTOGRAPHY SHEAR WAVE

Sat, 30 Nov 2024 00:00:00 +0000

The lens in human eyes possesses elastic properties that facilitate shape change to focus light onto the retina, with ultrasound elastography shear wave being a common imaging technique to assess tissue elasticity. This study aimed to determine the correlation between lens elasticity and various factors across different age ranges, including refractive error and biometric properties (axial length and lens thickness). The study included responses from 84 individuals aged 19 to 65, with eligibility determined through tests assessing refractive errors and visual acuity. Axial length and lens thickness were measured after selecting the best eye for testing, followed by scanning with ultrasound elastography using shear wave technology. The data analysis revealed a significant correlation between participants' age and lens elasticity (r = 0.83, P = 0.00), an insignificant correlation between elasticity and refractive error (r = 0.247), weak correlations with axial length (r = 0.006), lens thickness (r = 0.27, P = 0.14), and a negative correlation between axial length and lens thickness (r = 0.233, P = 0.033). The results indicated that the alterations do not appreciably influence lens elasticity changes with age, but the variations in refractive error or ocular morphology.

SIGNAL ANALYSIS BASED ON HAND ACTIVITY: IMPLICATIONS FOR PROSTHETIC DEVELOPMENT

Mohammed Alfadheeli, Norhashimah Mohd Saad, Sanaullah Khan — Sat, 30 Nov 2024 00:00:00 +0000

ABSTRACT: This study examines electromyography (EMG) signal pattern recognition to elucidate the relationship between EMG signals, hand movement, and biceps muscle force. The exercises will be performed at angles of 45°, 90°, and 120° relative to the elbow joint. Utilizing Matlab for EMG signal optimal features parameters, the research focuses on hand movements under varying loads (2kg, 4kg, and 6kg). Data acquisition involved tasks of lifting and holding, with EMG signals analysed across different phases of muscle activation. Results indicate a positive correlation between EMG signal amplitudes and both load and motion angles, revealing distinct muscle activation phases during lifting, holding, and releasing. The results show that the normalized average peak force at different loading levels is increases if the load increases, and if the load decreases the amplitude also decreases for trained and untrained subjects. The findings underscore the potential for these insights to inform the development of flexible prosthetic arms and assistive technologies.

Revolutionizing Workout Analytics: Machine Learning Models for Calorie Burn Estimation

Sat, 30 Nov 2024 00:00:00 +0000

In predicting calories burned based on personal data, researchers aimed at deploying machine learning. The first step involved data preparation and cleaning, which included imputation of missing values, before undertaking Exploratory Data Analysis. Five different machine learning models were used: K-Nearest Neighbors (KNN), Decision Tree (DT), AdaBoost (AB), Support Vector Machine (SVM) and XGBoost (XGB). Both default and optimized hyperparameters were utilized to train the models for better outcomes. Some of the models predicted calories burned successfully with XGBoost being the best fit. Feature interactions influencing the output became apparent through Explainable AI which was employed for this purpose. This study emphasized on hyperparameter tuning as a way to achieve optimal results when using any given model. With an RMSE value of 2.13, and a coefficient of determination R² equal to one, XGBoost carded home the best performance. This research shows that it is possible to apply machine learning in order to forecast calorie loss from individual data, hence improving fitness and health programs.

A PRELIMINARY STUDY ON TRUSTWORTHINESS FRAMEWORK FOR TRADITIONAL MALAY MEDICINE DATA RETRIEVAL

Sat, 30 Nov 2024 00:00:00 +0000

Traditional Malay Medicine data retrieval may have the potential to contribute to the COVID-19 pandemic response. However, trustworthiness issues may hinder for medical professionals from accepting its formulations. This paper presents a preliminary study of trustworthiness frameworks in PubMed and IEEE related to Covid-19. The study examined three aspects of Traditional Malay Medicine data retrieval; (1) Manuscript Original Verification, (2) Author Profiling method, and (3) in-database module trust framework. Three relevance frameworks are selected based on each focus aspect. We proposed a scoring system that emphasizes on the authenticity of the source or provider of the manuscript. We suggested a replicability evaluation of the authors' formulation to enhance their credibility. In-database module trust framework covered by a set of dynamic rules. It offers reliable ways to implement trustworthiness in the database. This review will help establish standards and guidelines for evaluating Traditional Malay Medicine Trustworthiness. Thus, inspire current researchers to explore Traditional Malay Medicine's role in response to future pandemics.

COMPARATIVE ANALYSIS OF ARTIFICIAL INTELLIGENCE (AI) AND HUMAN EXPERTISE IN HEART RHYTHM DIAGNOSIS: A SYSTEMATIC REVIEW AND META-ANALYSIS

Sat, 30 Nov 2024 00:00:00 +0000

Arrhythmias, characterized by irregular, fast, or slow heartbeats, can lead to severe complications if not detected and managed promptly. Artificial intelligence (AI) has emerged as a promising tool for analysing cardiac rhythm recordings, potentially improving the accuracy and efficiency of arrhythmia diagnosis. This systematic review and meta-analysis aimed to compare the accuracy of AI and human analysis in interpreting cardiac rhythm recordings and to explore the potential of AI to enhance diagnoses in pre-hospital care settings. A comprehensive search was conducted in multiple electronic databases, including PubMed, Scopus, Web of Science, IEEE Xplore, and the Cochrane Library, to identify studies comparing the accuracy of AI and human analysis in interpreting cardiac rhythm recordings. The study followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The quality of the included studies was assessed using the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool. A random-effects model was used for meta-analysis, and subgroup analyses were performed based on AI algorithm type and data acquisition method. Twenty-two studies were included in the qualitative synthesis, and 18 were suitable for meta-analysis. The pooled sensitivity, specificity, and area under the receiver operating characteristic curve (AUC-ROC) were consistently higher for AI compared to human analysis. Deep learning algorithms demonstrated superior accuracy compared to machine learning algorithms. Studies using electrocardiogram (ECG) as the data acquisition method showed higher pooled AUC-ROC compared to those using Holter monitors. The findings suggested that AI algorithms, particularly deep learning methods, have higher accuracy in interpreting cardiac rhythm recordings compared to human analysis. AI-based diagnostic tools have the potential to improve the early detection and management of arrhythmias in pre-hospital care settings. However, further research is needed to validate these results in real-world clinical settings, address the limitations of current studies, and explore the long-term impact of AI on patient outcomes and healthcare delivery.