Use of Flowchart for Automation of Clinical Protocols in mHealth.

For healthcare professionals to use mobile applications we need someone who knows software development, provide them. In healthcare institutions, health professionals use clinical protocols to govern care, and sometimes these documents are computerized through mobile applications to assist them. This work aims to present a proposal of an application of flow as a way of describing clinical protocols for automatic generation of mobile applications to assist health professionals. The purpose of this research is to enable health professionals to develop applications from the description of their own clinical protocols. As a result, we developed a web system that automates clinical protocols for an Android platform, and we validated with two clinical protocols used in a Brazilian hospital. Preliminary results of the developed architecture demonstrate the feasibility of this study.

Locally Adaptive Operators for Red Lesions Detection in Eye Fundus Images.

One of the major features required by automated software tools of screening for diabetic retinopathy is the detection of red lesions. This paper presents a new automatic method in order to locate red lesions in color eye fundus images. The method relies on mathematical morphology operators and has a coarse and a fine detection stages, respectively. The former detection stage detects structures of low-intensity values in the retina, such as microaneurysms, hemorrhages, blood vessels and the fovea center. Additionally, the latter stage proposes to improve the detection of red lesions identified in the previous stage. For experiments, we use the well-known publicly available DIARETDB1 database. The results indicate that our method detected red lesions with 75.81% and 93.48% of mean sensitivity and mean specificity, respectively.

Automatic Identification of Glaucoma Using Deep Learning Methods.

This paper proposes an automatic classification method to detect glaucoma in fundus images. The method is based on training a neural network using public image databases. The network used in this paper is the GoogLeNet, adapted for this proposal. The methodology was divided into two stages, namely: (1) detection of the region of interest (ROI); (2) image classification. We first used a sliding-window approach combined with the GoogLeNet network. This network was trained using manually extracted ROIs and other fundus image structures. Afterwards, another GoogLeNet model was trained using the previous resulting images. Then those images were used to train another GoogLeNet model to automatically detect glaucoma. To prevent overfitting, data augmentation techniques were used on smaller databases. The results demonstrated that the network had a good accuracy, even with poor quality images found in some databases or generated by the data augmentation algorithm.

CTdBem - A New Protocol for Ultra Low Radiation Dose MDCT.

This study describes a novel method of obtaining low-dose computed tomography (CT) scans followed by imaging postprocessing that provides diagnostic quality to such low-dose exams. In addition, we compared the Total Radiation Doses (DLP) of the 64-channel MDCT x 16-channel MDCT for a new Dental CT - CTdBem protocol for hospital use. DLP data obtained from 20 patients using 16-channel MDCT was compared with 20 other patients using 64-channel MDCT. In both tomographic (Aquilion 64 and Brightspeed 16) FOV was approximately 160(V) x 130(H) mm. An imaging postprocessing algorithm was used to provide diagnostic quality to the obtained low-dose CT scans. Imaging postprocessing included imaging smoothing, multiplanar reconstruction (MPR), and volume rendering (VR), as well as surface rendering (SR) to allow three-dimensional printing of the desired scans. The average DLPs were of 28,5 mGy.cm and 54,65 mGy.cm, using the 64-channel MDCT and 16-channel MDCT, respectively. The effective radiation dose (DLP) from the 64-channel MDCT statistically differs from the DLP data from 16-channel MDCT, resulting in a value of p < 0.05 for all comparisons. A novel low-dose CT protocol for dentomaxillofacial assessment using imaging postprocessing techniques is described. The authors concluded that although the DLP values differ statistically (p < 0.05), both equipment (64 and 16-channel MDCT) produce tomographic images of patients with low radiation doses. The greater the number of detectors, the lower the mAs product and, consequently, the amount of X-radiationproduced.

An infrequent cause of persistent cough: endobronchial lipoma.

A 35-year-old nonsmoker man presented complaining of productive and irritant cough for approximately 1 month. No history of fever, shortness of breath, weight loss, or other associated symptoms were present at admission. Physical examination, including careful evaluations of cardiovascular and respiratory systems, did not reveal any abnormalities. Past medical records (including allergies and occupational agent exposure) were also found to be unremarkable. A chest roentgenogram with frontal and lateral incidences and a blood sample analysis were therefore performed, but the reports of both these analyses were normal, demanding further imaging investigation.