Radiology is a vital diagnostic tool for multiple disorders that plays an essential role in the healthcare sector. Nurses are majorly involved in a healthcare setting by accompanying patients during the examination. Thus, nurses tend to be exposed during inward X-ray examination, requiring them to keep up with radiation use safety. However, nurses’ competence in radiation is still a concept that has not been well studied in Malaysia. The study aimed to define the level of usage understanding and radiation protection among Malaysian nurses. In this research, a cross-sectional survey was conducted among 395 nurses working in hospitals, clinics, and other healthcare sectors in Malaysia. The survey is based on the developed Healthcare Professional Knowledge of Radiation Protection (HPKRP) scale, distributed via the online Google Forms. SPSS version 25.0 (IBM Corporation) was used to analyze the data in this study. Malaysian nurses reported the highest knowledge level in radiation protection with a mean of 6.03 ± 2.59. The second highest is safe ionizing radiation guidelines with 5.83 ± 2.77, but low knowledge levels in radiation physics and radiation usage principle (4.69 ± 2.49). Therefore, healthcare facilities should strengthen the training standards for all nurses working with or exposed to radiation.

Background: Early diagnosis of liver cancer may increase life expectancy. Computed tomography (CT) and magnetic resonance imaging (MRI) play a vital role in diagnosing liver cancer. Together, both modalities offer significant individual and specific diagnosis data to physicians; however, they lack the integration of both types of information. To address this concern, a registration process has to be utilized for the purpose, as multimodal details are crucial in providing the physician with complete information. Objective: The aim was to present a model of CT-MRI registration used to diagnose liver cancer, specifically for improving the quality of the liver images and provide all the required information for earlier detection of the tumors. This method should concurrently address the issues of imaging procedures for liver cancer to fasten the detection of the tumor from both modalities. Methods: In this work, a registration scheme for fusing the CT and MRI liver images is studied. A feature point-based method with normalized cross-correlation has been utilized to aid in the diagnosis of liver cancer and provide multimodal information to physicians. Data on ten patients from an online database were obtained. For each dataset, three planar views from both modalities were interpolated and registered using feature point-based methods. The registration of algorithms was carried out by MATLAB (vR2019b, Mathworks, Natick, USA) on an Intel(R) Core (TM) i5-5200U CPU @ 2.20 GHz computer. The accuracy of the registered image is being validated qualitatively and quantitatively. Results: The results show that an accurate registration is obtained with minimal distance errors by which CT and MRI were accurately registered based on the validation of the experts. The RMSE ranges from 0.02 to 1.01 for translation, which is equivalent in magnitude to approximately 0 to 5 pixels for CT and registered image resolution. Conclusion: The CT-MRI registration scheme can provide complementary information on liver cancer to physicians, thus improving the diagnosis and treatment planning process.

The consumption of medicine is typical in geriatrics, having many problems related to medications. Geriatrics often forget to take their medicine, and this problem can be overcome by using an automatic reminder system. In this study, an automated reminder system is developed as an improved community element, acting as a system that can help geriatric in taking their medicine on time, thus, boosting their health condition. This reminder system also includes an interaction between the geriatrics and their caretakers. This reminder system includes Arduino UNO as the microcontroller, with the notification system, Blynk Application, a buzzer, and a light-emitting diode (LED) system. To make this reminder system more versatile, the buzzer will alarm during the medicine intake time, giving information to the elderly on which medicine to take. When the time has reached to take medication, the buzzer will produce a sound. Suppose the medicine box opens after the buzzer's sound and is detected by the passive infrared sensor (PIR sensor). In that case, the caretaker will receive a notification through the Blynk application that the geriatric already took medicine. On the contrary, if the medicine box is not open after 3 minutes following the buzzer's sound, which indicates that the geriatric did not take their medicine, the system will not send a notification to their caretakers on the status. This prototype is tested on ten users for its accuracy and effectiveness. It is believed that this system can provide geriatrics more alert in taking their medicine on time, enhancing their health status.

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A process that involves the registration of two brain Magnetic Resonance Imaging (MRI) acquisitions is proposed for the subtraction between previous and current images at two different follow-up (FU) time points. Brain tumours can be non-cancerous (benign) or cancerous (malignant). Treatment choices for these conditions rely on the type of brain tumour as well as its size and location. Brain cancer is a fast-spreading tumour that must be treated in time. MRI is commonly used in the detection of early signs of abnormality in the brain area because it provides clear details. Abnormalities include the presence of cysts, haematomas or tumour cells. A sequence of images can be used to detect the progression of such abnormalities. A previous study on conventional (CONV) visual reading reported low accuracy and speed in the early detection of abnormalities, specifically in brain images. It can affect the proper diagnosis and treatment of the patient. A digital subtraction technique that involves two images acquired at two interval time points and their subtraction for the detection of the progression of abnormalities in the brain image was proposed in this study. MRI datasets of five patients, including a series of brain images, were retrieved retrospectively in this study. All methods were carried out using the MATLAB programming platform. ROI volume and diameter for both regions were recorded to analyse progression details, location, shape variations and size alteration of tumours. This study promotes the use of digital subtraction techniques on brain MRIs to track any abnormality and achieve early diagnosis and accuracy whilst reducing reading time. Thus, improving the diagnostic information for physicians can enhance the treatment plan for patients.