Nosocomial infection is one of the most important problems that occurs in hospitals, as it directly affects susceptible patients or patients with immune deficiency. Klebsiella pneumoniae (K. pneumoniae) is the most common cause of nosocomial infections in hospitals. K. pneumoniae can cause various diseases such as pneumonia, urinary tract infections, septicemias, and soft tissue infections, and it has also become highly resistant to antibiotics. The principal routes for the transmission of K. pneumoniae are via the gastrointestinal tract and the hands of hospital personnel via healthcare workers, patients, hospital equipment, and interventional procedures. These bacteria can spread rapidly in the hospital environment and tend to cause nosocomial outbreaks. In this research, we developed a MIP-based electrochemical biosensor to detect K. pneumoniae. Quantitative detection was performed using an electrochemical technique to measure the changes in electrical signals in different concentrations of K. pneumoniae ranging from 10 to 10⁵ CFU/mL. Our MIP-based K. pneumoniae sensor was found to achieve a high linear response, with an R² value of 0.9919. A sensitivity test was also performed on bacteria with a similar structure to that of K. pneumoniae. The sensitivity results show that the MIP-based K. pneumoniae biosensor with a gold electrode was the most sensitive, with a 7.51 (% relative current/log concentration) when compared with the MIP sensor applied with Pseudomonas aeruginosa and Enterococcus faecalis, where the sensitivity was 2.634 and 2.226, respectively. Our sensor was also able to achieve a limit of detection (LOD) of 0.012 CFU/mL and limit of quantitation (LOQ) of 1.61 CFU/mL.

This research concerns the design and development of an assistive system based on eye tracking, which can be used to improve the quality of life of disabled patients. With the use of their eye movement, whose function is not affected by their illness, patients are capable of communicating with and sending notifications to caretakers, controlling various appliances, including wheelchairs. The designed system is divided into two subsystems: stationary and mobile assistive systems. Both systems provide a graphic user interface (GUI) that is used to link the eye tracker with the appliance control. There are six GUI pages for the stationary assistive system and seven for the mobile assistive system. GUI pages for the stationary assistive system include the home page, smart appliance page, eye-controlled television page, eye-controlled air conditional page, i-speak page and entertainment page. GUI pages for the mobile assistive system are similar to the GUI pages for the stationary assistive system, with the additional eye-controlled wheelchair page. To provide hand-free secure access, an authentication based on facial landmarks is developed. The operational test of the proposed assistive system provides successful and promising results.

Lung cancer is a life-threatening disease with the highest morbidity and mortality rates of any cancer worldwide. Clinical staging of lung cancer can significantly reduce the mortality rate, because effective treatment options strongly depend on the specific stage of cancer. Unfortunately, manual staging remains a challenge due to the intensive effort required. This paper presents a computer-aided diagnosis (CAD) method for detecting and staging lung cancer from computed tomography (CT) images. This CAD works in three fundamental phases: segmentation, detection, and staging. In the first phase, lung anatomical structures from the input tomography scans are segmented using gray-level thresholding. In the second, the tumor nodules inside the lungs are detected using some extracted features from the segmented tumor candidates. In the last phase, the clinical stages of the detected tumors are defined by extracting locational features. For accurate and robust predictions, our CAD applies a double-staged classification: the first is for the detection of tumors and the second is for staging. In both classification stages, five alternative classifiers, namely the Decision Tree (DT), K-nearest neighbor (KNN), Support Vector Machine (SVM), Ensemble Tree (ET), and Back Propagation Neural Network (BPNN), are applied and compared to ensure high classification performance. The average accuracy levels of 92.8% for detection and 90.6% for staging are achieved using BPNN. Experimental findings reveal that the proposed CAD method provides preferable results compared to previous methods; thus, it is applicable as a clinical diagnostic tool for lung cancer.

One of the common causes of a heart attack is fibrillation, a condition that causes an irregular and often abnormally fast heart rate. There is scientific evidence that the survival rate of sudden cardiac arrest patients who are rescued with cardiopulmonary resuscitation (CPR) and with the use of an automated external defibrillator (AED) is significantly increased. Despite the recommendation that automated external defibrillators should be installed in the workplace, along with a proper management system and training for employees on how to use the device, less than 70% of non-residential areas have an AED installed. The situation is even worse in residential areas, with less than 30% having an AED installed. This research concerns the development of a medical drone managing system that can deliver an AED in case of emergency. An application was developed that can be installed on the mobile phone and/or tablet of the patient or the accompanying person. In the event of a heart attack, the patient or the accompanying person can call a medical drone by sending coordinates to the drone station and a notification to medical staff. The drone station administrator can respond by sending the drone, which automatically lands at the patient’s location. After being tested in a simulation situation, the operational field test yielded satisfactory results. The medical drone can land within 1.5 m of the destination. The designed AED drone can be used not only to deliver AEDs, but also first aid kits and prescribed drugs suitable for medical care. Such a system is especially useful in the current context of the COVID-19 pandemic.

Automated segmentation methods are critical for early detection, prompt actions, and immediate treatments in reducing disability and death risks of brain infarction. This paper aims to develop a fully automated method to segment the infarct lesions from T1-weighted brain scans. As a key novelty, the proposed method combines variational mode decomposition and deep learning-based segmentation to take advantages of both methods and provide better results. There are three main technical contributions in this paper. First, variational mode decomposition is applied as a pre-processing to discriminate the infarct lesions from unwanted non-infarct tissues. Second, overlapped patches strategy is proposed to reduce the workload of the deep-learning-based segmentation task. Finally, a three-dimensional U-Net model is developed to perform patch-wise segmentation of infarct lesions. A total of 239 brain scans from a public dataset is utilized to develop and evaluate the proposed method. Empirical results reveal that the proposed automated segmentation can provide promising performances with an average dice similarity coefficient (DSC) of 0.6684, intersection over union (IoU) of 0.5022, and average symmetric surface distance (ASSD) of 0.3932, respectively.

Anemia is a condition in which red blood cells are not able to carry adequate oxygen to the body’s tissues, and is widely found in nearly a quarter of the world population. The typical method to screen for the iron-deficiency anemia, which is the major anemia found in the world, is to implement a blood test called a complete blood count (CBC). However, even though this test gives a highly accurate result, it requires an invasive blood drawing and lab analyzing which could potentially cause physical pain, high risk of infection and take a long time to analyze. Therefore, this research presents an alternative method using an optical technique to measure hemoglobin concentration, which is the common indicator for diagnosing anemia. The light absorbance of the oxyhemoglobin at the wavelength of 660 nm and the deoxyhemoglobin at the wavelength of 880 nm were measured using the MAX30100 sensor. These wavelengths of light are obtained from red and infrared (IR) LEDs. The concept is based on the different absorption coefficients of blood at different electromagnetic wavelengths. This fact is used to indirectly calculate the hemoglobin concentration of blood through the modified Beer–Lambert law. Moreover, the result has been further converted to absolute hemoglobin concentration using a calibration curve derived from the cyanmethemoglobin test, which is the regular method for hemoglobin determination. Besides, the android application was also provided which can wirelessly record or monitor the data. The experiment shows that an accuracy of 90.9% can be achieved by our proposed noninvasive method. Therefore, the noninvasive portable hemoglobin concentration monitoring by the optical sensor has an acceptable result when compared with the invasive method, with less pain and lower risk of infection, as well as shorter processing time.