An Automated Size Recognition Technique for Acetabular Implant in Total Hip Replacement

Preoperative templating in Total Hip Replacement (THR) is a method to estimate the optimal size and position of the implant. Today, observational (manual) size recognition techniques are still used to find a suitable implant for the patient. Therefore, a digital and automated technique should be developed so that the implant size recognition process can be effectively implemented. For this purpose, we have introduced the new technique for acetabular implant size recognition in THR preoperative planning based on the diameter of acetabulum size. This technique enables the surgeon to recognise a digital acetabular implant size automatically. Ten randomly selected X-rays of unidentified patients were used to test the accuracy and utility of an automated implant size recognition technique. Based on the testing result, the new technique yielded very close results to those obtained by the observational method in nine studies (90%)

Applications of Computer Aided Design (CAD) in Medical Image Technology

This paper is submitting the idea of Computer Aided Design (CAD) software application in manipulation of digital x-ray images (DICOM). The study also discusses the concept of raster and vector images as DICOM images will be referred to as raster graphic and CAD as vector graphic. Vectorizing allows an image to be more flexible and can be manipulated so that more information can be loaded into it. As such, it is not impossible that vectorizing method can also be performed on medical images using CAD software such as AutoCAD, Solidworks and others. A DICOM image with DCM format is converted to JPEG format using Medweb software. Then, by using Image2CAD software, the x-ray image is converted to DXF format. The results showed that patient's x-ray image can be manipulated by using AutoCAD software. Study shows that CAD is not only used in the manufacturing field, but it can also be used in the medical field as well

Activity based costing software for manufacturing industries

Manufacturing companies in Malaysia face ever-increasing competition in today's global marketplace. Companies must react quickly and manufacture high quality, low cost products to be successful in this new environment. Nowadays, complexity-manufacturing technology has led to increasing indirect cost or overhead cost in the calculation of the total production cost. The failure of traditional costing method in tracing overhead cost to products has caused the distortion of product cost. A new costing method named Activity Based Costing (ABC) has been introduced as an alternative for solving the problem. This ABC method has been implemented for product costing. The main purpose for this study is to develop computerized ABC system for the manufacturing companies. This software enables the companies to identify the characteristics of activities and the costs involved in activities. ABC can assign dollar values to every activity or process. The ABC software is hoped to improve further the process planning and action of manufacturing companies through cost savings and improving revenue

A systematic literature review on outlier detection in wireless sensor networks

Obesity is considered a principal public health concern and ranked as the fifth foremost reason for death globally. Overweight and obesity are one of the main lifestyle illnesses that leads to further health concerns and contributes to numerous chronic diseases, including cancers, diabetes, metabolic syndrome, and cardiovascular diseases. The World Health Organization also predicted that 30% of death in the world will be initiated with lifestyle diseases in 2030 and can be stopped through the suitable identification and addressing of associated risk factors and behavioral involvement policies. Thus, detecting and diagnosing obesity as early as possible is crucial. Therefore, the machine learning approach is a promising solution to early predictions of obesity and the risk of overweight because it can offer quick, immediate, and accurate identification of risk factors and condition likelihoods. The present study conducted a systematic literature review to examine obesity research and machine learning techniques for the prevention and treatment of obesity from 2010 to 2020. Accordingly, 93 papers are identified from the review articles as primary studies from an initial pool of over 700 papers addressing obesity. Consequently, this study initially recognized the significant potential factors that influence and cause adult obesity. Next, the main diseases and health consequences of obesity and overweight are investigated. Ultimately, this study recognized the machine learning methods that can be used for the prediction of obesity. Finally, this study seeks to support decision-makers looking to understand the impact of obesity on health in the general population and identify outcomes that can be used to guide health authorities and public health to further mitigate threats and effectively guide obese people globally