BackgroundLittle detailed knowledge is available regarding the etiology and outcome of CNS infection, particularly in HIV-infected individuals, in low-resource settings.MethodsFrom January 2015 to April 2016, we prospectively included all adults with suspected CNS infection in a referral hospital in Jakarta, Indonesia. Systematic screening included HIV testing, CSF examination, and neuroimaging.ResultsA total of 274 patients with suspected CNS infection (median age 26 years) presented after a median of 14 days with headache (77%), fever (78%), seizures (27%), or loss of consciousness (71%). HIV coinfection was common (54%), mostly newly diagnosed (30%) and advanced (median CD4 cell count 30/µL). Diagnosis was established in 167 participants (65%), including definite tuberculous meningitis (TBM) (n = 44), probable TBM (n = 48), cerebral toxoplasmosis (n = 48), cryptococcal meningitis (n = 14), herpes simplex virus/varicella-zoster virus/cytomegalovirus encephalitis (n = 10), cerebral lymphoma (n = 1), neurosyphilis (n = 1), and mucormycosis (n = 1). In-hospital mortality was 32%; 6-month mortality was 57%. The remaining survivors had either moderate or severe disability (36%) according to Glasgow Outcome Scale.ConclusionIn this setting, patients with CNS infections present late with severe disease and often associated with advanced HIV infection. Tuberculosis, toxoplasmosis, and cryptococcosis are common. High mortality and long-term morbidity underline the need for service improvements and further study.

Lumbar Spinal Stenosis causes low back pain through pressures exerted on the spinal nerves. This can be verified by measuring the anteroposterior diameter and foraminal widths of the patient’s lumbar spine. Our goal is to develop a novel strategy for assessing the extent of Lumbar Spinal Stenosis by automatically calculating these distances from the patient’s lumbar spine MRI. Our method starts with a semantic segmentation of T1- and T2-weighted composite axial MRI images using SegNet that partitions the image into six regions of interest. They consist of three main regions-of-interest, namely the Intervertebral Disc, Posterior Element, and Thecal Sac, and three auxiliary regions-of-interest that includes the Area between Anterior and Posterior elements. A novel contour evolution algorithm is then applied to improve the accuracy of the segmentation results along important region boundaries. Nine anatomical landmarks on the image are located by delineating the region boundaries found in the segmented image before the anteroposterior diameter and foraminal widths can be measured. The performance of the proposed algorithm was evaluated through a set of experiments on the Lumbar Spine MRI dataset containing MRI studies of 515 patients. These experiments compare the performance of our contour evolution algorithm with the Geodesic Active Contour and Chan-Vese methods over 22 different setups. We found that our method works best when our contour evolution algorithm is applied to improve the accuracy of both the label images used to train the SegNet model and the automatically segmented image. The average error of the calculated right and left foraminal distances relative to their expert-measured distances are 0.28 mm ( p = 0.92) and 0.29 mm ( p = 0.97), respectively. The average error of the calculated anteroposterior diameter relative to their expert-measured diameter is 0.90 mm ( p = 0.92). The method also achieves 96.7% agreement with an expert opinion on determining the severity of the Intervertebral Disc herniations.

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In this paper, we proposed the modified deep learning method that combined Convolutional Neural Network (CNN) and Kernel K-Means clustering for lung cancer diagnosis. The Anti-PD-1 Immunotherapy Lung dataset obtained from The Cancer Imaging Archive was used to evaluate our proposed method. From this dataset, we use 400 Magnetic Resonance Imaging (MRI) images that manually labeled consists of 150 healthy lung images and 250 lung cancer images. As the first step, all the data was examined through the CNN architecture. The flatten neuron of the feature map for every image resulted from the convolutional layers in CNN is gained and passed through the kernel k-means clustering algorithm. This algorithm then used to obtain the centroid of each cluster that determines the prediction class of every data point in the validation set. The performance of our proposed method was evaluated using several k values in k-fold cross-validation. According to our experiments, our proposed method achieved the highest performance measure with 98.85 percent accuracy, 98.32 percent sensitivity, 99.40 percent precision, 99.39 percent specificity, and 98.86 percent F1-Score when using RBF kernel function with sigma=0.05 in 9-fold cross-validation. Those performance improves 1.31% sensitivity, 1.12% accuracy, 1.11% F1-Score, 0.92% specificity, and 0.91% precision compared to when using 5-fold crossvalidation. It is even obtained in less than 8 seconds for passing the dataset to the CNN model and 40 ± 0.77 seconds for examined in kernel k-means clustering. Therefore, it was proved that our proposed method has an efficient and promised performance for lung cancer diagnosis from MRI images.

Stroke has become a global health problem, due to high mortality and disability, with two-thirds of all strokes occurring in developing countries. In Indonesia, stroke is a disease with the highest mortality rate, namely in the first rank for more than two decades, 1990-2017. Stroke is divided into two types, ischemic and hemorrhagic; however, 87% of stroke sufferers are ischemic stroke. Suppose an ischemic stroke is found, and the patient is a new sufferer. In that case, the patient should get direct treatment because there is a golden period in stroke management that is if 4.5 hours to help and reduce the risk of death or permanent disability. High mortality and disability raise awareness of the importance of early detection of ischemic stroke; therefore, research has been carried out, especially in technology. To carry out automatic diagnosis, machine learning and deep learning can be used, especially because of their ability to provide high accuracy prediction results. In this study, the authors will provide an update in the detection of ischemic stroke based on patient CT scan by replacing NN's role on CNN with random forests. Thus, after feature extraction on CNN, the fully connected layer on CNN is completely replaced by random forests in classifying data. Based on the proposed method, the accuracy of testing is 100% when the percentage of the testing dataset is 10% and the number of trees more than 100 with criterion Gini or entropy.

Abnormalities and defects that can cause lumbar spinal stenosis often occur in the Intervertebral Disc (IVD) of the patient’s lumbar spine. Their automatic detection and classification require an application of an image analysis algorithm on suitable images, such as mid-sagittal images or traverse mid-height intervertebral disc slices, as inputs. Hence the process of selecting and separating these images from other medical images in the patient’s set of scans is necessary. However, the technological progress in making this process automated is still lagging behind other areas in medical image classification research. In this paper, we report the result of our investigation on the suitability and performance of different approaches of machine learning to automatically select the best traverse plane that cuts closest to the half-height of an IVD from a database of lumbar spine MRI images. This study considers images features extracted using eleven different pre-trained Deep Convolution Neural Network (DCNN) models. We investigate the effectiveness of three dimensionality-reduction techniques and three feature-selection techniques on the classification performance. We also investigate the performance of five different Machine Learning (ML) algorithms and three Fully Connected (FC) neural network learning optimizers which are used to train an image classifier with hyperparameter optimization using a wide range of hyperparameter options and values. The different combinations of methods are tested on a publicly available lumbar spine MRI dataset consisting of MRI studies of 515 patients with symptomatic back pain. Our experiment shows that applying the Support Vector Machine algorithm with a short Gaussian kernel on full-length image features extracted using a pre-trained DenseNet201 model is the best approach to use. This approach gives the minimum per-class classification performance of around 0.88 when measured using the precision and recall metrics. The median performance measured using the precision metric ranges from 0.95 to 0.99 whereas that using the recall metric ranges from 0.93 to 1.0. When only considering the L3/L4, L4/L5, and L5/S1 classes, the minimum F1-Scores range between 0.93 to 0.95, whereas the median F1-Scores range between 0.97 to 0.99.

The medical sector is currently in need of a method to aid in the classification of diseases, which contemporarily progresses into varying types. Therefore, the role of technology is highly relevant in the process of overcoming this challenge. This report discusses acute sinusitis, which is one of the most common forms of sinusitis, possibly caused by viruses, bacteria, fungi, pollutants, allergies, and also autoimmune reactions. Furthermore, the Support Vector Machines (SVM) and Fuzzy Support Vector Machines (FSVM) are used as a classification method to diagnose a person of acute sinusitis, therefore, this research aims to compare how both work, using Radial Basis Function (RBF) and Polynomial Kernel. Data of CT scan from Cipto Mangunkusumo Hospital, Indonesia was used to evaluate acute sinusitis, in terms of Accuracy, Sensitivity, Precision, and F1-Score. Thus, the final results indicate a better performance for FSVM than SVM in all perspectives, especially using the RBF kernel.

30,1%). LV geometry patterns were concentric LVH (49 pts/40%), eccentric LVH (41 pts/33%), normal geometry (22 pts/18%), and concentric remodeling (11 pts/9%). Concentric LVH tends to occur in women, age >65 years, and obese patients. Eccentric LVH tends to occur in patients with comorbid CAD, VHD, rEF, and DM II. Concentric remodeling and normal geometry are never dominant as the most pattern of geometry in hypertension patients. Conclusion:The LV geometry of hypertension patients majority have experienced LVH with the most pattern is concentric LVH.