Evandro Eduardo Seron Ruiz

Digital image processing measurement of the upper eyelid contour in graves disease and congenital blepharoptosis

OBJECTIVE This study used image processing techniques to quantify the upper eyelid contour of patients with Graves upper eyelid retraction and congenital blepharoptosis. DESIGN The study design was a cross-sectional study. PARTICIPANTS A total of 29 patients with Graves disease, 22 patients with congenital blepharoptosis, and 50 patients with no history of eye disease participated. INTERVENTION The images of the palpebral fissure of all participants were transferred to a personal computer and processed with NIH Image 1.55 software. MAIN OUTCOME MEASURES The following parameters were analyzed: the curvature of the upper eyelid contour, the position of the contour peak relative to the midline, and the ratio between the temporal and nasal upper quadrant areas of the palpebral fissure. RESULTS All upper eyelid contours could be fitted with second-degree polynomial functions. The mean temporal/nasal area ratio was 1.33 mm in patients with Graves disease, 0.92 mm in patients with blepharoptosis, and 1.04 mm in control subjects. The peak of the upper eyelid contour was found to be lateral to the midline in control subjects (1.05 mm) and in patients with Graves disease (2.09 mm). In patients with blepharoptosis, the peak was 0.69 mm medial to the midline. Overall, the distance between the midpupil and the upper eyelid margin was correlated with several factors: the degree of curvature, the position of the peak of the eyelid contour, and the temporal/nasal area ratio. CONCLUSIONS In Graves eyelid retraction, the curvature of the upper eyelid is enhanced, the peak of the contour is displaced laterally, and the temporal upper quadrant area is increased. Conversely, in congenital blepharoptosis, the eyelid is almost flat, the peak of the contour is displaced medially, and the upper quadrant area is diminished. The lateral segment of the upper eyelid is more involved than the nasal segment in both Graves upper eyelid retraction and congenital blepharoptosis.

Thesaurus as a complex network

A thesaurus is one, out of many, possible representations of term (or word) connectivity. The terms of a thesaurus are seen as the nodes and their relationship as the links of a directed graph. The directionality of the links retains all the thesaurus information and allows the measurement of several quantities. This has lead to a new term classification according to the characteristics of the nodes, for example, nodes with no links in, no links out, etc. Using an electronic available thesaurus we have obtained the incoming and outgoing link distributions. While the incoming link distribution follows a stretched exponential function, the lower bound for the outgoing link distribution has the same envelope of the scientific paper citation distribution proposed by Tsallis and Albuquerque (Eur. Phys. J. B 13 (2000) 777). However, a better fit is obtained by simpler function which is the solution of Ricattis differential equation. We conjecture that this differential equation is the continuous limit of a stochastic growth model of the thesaurus network. We also propose a new manner to arrange a thesaurus using the “inversion method”.

Automated grading of left ventricular segmental wall motion by an artificial neural network using color kinesis images

The present study describes an auxiliary tool in the diagnosis of left ventricular (LV) segmental wall motion (WM) abnormalities based on color-coded echocardiographic WM images. An artificial neural network (ANN) was developed and validated for grading LV segmental WM using data from color kinesis (CK) images, a technique developed to display the timing and magnitude of global and regional WM in real time. We evaluated 21 normal subjects and 20 patients with LVWM abnormalities revealed by two-dimensional echocardiography. CK images were obtained in two sets of viewing planes. A method was developed to analyze CK images, providing quantitation of fractional area change in each of the 16 LV segments. Two experienced observers analyzed LVWM from two-dimensional images and scored them as: 1) normal, 2) mild hypokinesia, 3) moderate hypokinesia, 4) severe hypokinesia, 5) akinesia, and 6) dyskinesia. Based on expert analysis of 10 normal subjects and 10 patients, we trained a multilayer perceptron ANN using a back-propagation algorithm to provide automated grading of LVWM, and this ANN was then tested in the remaining subjects. Excellent concordance between expert and ANN analysis was shown by ROC curve analysis, with measured area under the curve of 0.975. An excellent correlation was also obtained for global LV segmental WM index by expert and ANN analysis (R2 = 0.99). In conclusion, ANN showed high accuracy for automated semi-quantitative grading of WM based on CK images. This technique can be an important aid, improving diagnostic accuracy and reducing inter-observer variability in scoring segmental LVWM.

A UMLS interoperable solution to support collaborative diagnosis decision making over the internet

Hospitals can supply clinical decision support systems with electronic healthcare record provided by different users and stored in different computer systems. This way, during a diagnosis decision a user can be supported by a remote knowledge database constructed by other users, like, for instance, a reference specialist discussion board. Usually, medical applications scenarios like this use different terminology systems leading to terminological interoperability problems. Still, clinical applications frequently make use of different clinical ontology systems that do not provide semantic types necessary to create a structure to carry shared knowledge database. The lack of this semantic types brings ontology interoperability problems which difficult the support of collaborative clinical decision-making. This paper presents a UMLS interoperable solution to promote clinical applications development intended to contribute with collaborative diagnosis decision, in a distributed computer environment based on service-oriented architecture. This paper still presents a case study to demonstrate DDSOnt usability through the development of a system prototype integrated to a collaborative and multimedia communication distance-learning environment.

Extraocular muscle quantification using mathematical morphology: A semi-automatic method for analyzing muscle enlargement in orbital diseases

Mathematical morphology was used to quantify the dimensions of the human extraocular muscles in computed tomography orbital scans. Coronal images of 28 patients with Graves orbitopathy and 5 controls (218 orbital scans) were analyzed with an algorithm for extraocular muscle segmentation. The results showed that measurements of extraocular muscle area obtained with semi-automatic segmentation are highly correlated with manual tracing and provides a simple method to quantify orbital structures including the extraocular muscles.

Surveillance for the prevention of chronic diseases through information association

BackgroundResearch on Genomic medicine has suggested that the exposure of patients to early life risk factors may induce the development of chronic diseases in adulthood, as the presence of premature risk factors can influence gene expression. The large number of scientific papers published in this research area makes it difficult for the healthcare professional to keep up with individual results and to establish association between them. Therefore, in our work we aim at building a computational system that will offer an innovative approach that alerts health professionals about human development problems such as cardiovascular disease, obesity and type 2 diabetes.MethodsWe built a computational system called Chronic Illness Surveillance System (CISS), which retrieves scientific studies that establish associations (conceptual relationships) between chronic diseases (cardiovascular diseases, diabetes and obesity) and the risk factors described on clinical records. To evaluate our approach, we submitted ten queries to CISS as well as to three other search engines (Google™, Google Scholar™ and Pubmed®;) — the queries were composed of terms and expressions from a list of risk factors provided by specialists.ResultsCISS retrieved a higher number of closely related (+) and somewhat related (+/-) documents, and a smaller number of unrelated (-) and almost unrelated (-/+) documents, in comparison with the three other systems. The results from the Friedman’s test carried out with the post-hoc Holm procedure (95% confidence) for our system (control) versus the results for the three other engines indicate that our system had the best performance in three of the categories (+), (-) and (+/-). This is an important result, since these are the most relevant categories for our users.ConclusionOur system should be able to assist researchers and health professionals in finding out relationships between potential risk factors and chronic diseases in scientific papers.

Mitigating the lack of knowledge about long noncoding RNA: extracting biological functions

Long non-coding RNAs (lncRNAs) play crucial roles in diverse biological processes. For example, they help to regulate gene expression and to shape the 3D organization of the cellular nucleus. However, their functions in these processes are not well known. Most of the ongoing research efforts regarding lncRNAs have focused on predicting their properties and their functions. Here, we aimed to extract the biological functions of lncRNAs referred in the biomedical literature. To this end, we performed gene enrichment tests for 417 verified gene names extracted from the scientific literature. We also succeeded in extracting biologically significant functions from 2455 single sentences of 2513 scientific abstracts retrieved from the PubMed digital library. These sentences had to be filtered to eliminate those that did not mention a biological function. They were also split into 2-3-grams and the meaningful function annotations based on Gene Ontology terms were extracted. The results provided by the gene enrichment test and the knowledge gained by automatic extraction of information from the scientific literature showed that lncRNAs play a critical role in all biological and cellular processes. Although simple natural language processing techniques fulfilled our purposes of extracting concepts concerning the biological functions of lncRNAs, in the future, we aim to use more sophisticated methods, such as grammatical rules, to extract more functions and to generate a full database for the biological functions related to lncRNAs.

Creating a Portuguese Context Sensitive Lexicon for Sentiment Analysis

This work presents and evaluates a new Portuguese context sensitive lexicon for sentiment analysis. Distinctive composition approaches to produce lexicons from established ones were also tested. The experiments were carried out with the corpus ReLi, composed of opinative texts about books, and with the following sentiment lexicons: Brazilian Portuguese LIWC, Opinion Lexicon, and SentiLex.

Applying Natural Language Processing, Information Retrieval and Machine Learning to Decision Support in Medical Coordination in an Emergency Medicine Context

The Medical Coordination, which is the application of logistics techniques to emergency context, is responsible for providing appropriate resources, in appropriate conditions to appropriate patients. A system for medical coordination of emergency requests was developed in 2009, although some activities related to medical coordination decision making are extremely subjective. Aiming to decrease subjectivity on activities like prioritization of requests and coordination flow, new technologies of decision support were incorporated to that system. These technologies include textual and semantic processing of clinical summaries and machine learning tools. Results indicate that automated tools could support decision on medical coordination process, allowing coordinators to focus attention on critical cases. These features may streamline the medical coordination, avoiding mistakes and increasing the chances of saving lives.

Information Learned from Monitoring Microblogs during the 2014 Seasonal Flu Vaccination in Brazil

We present preliminary data and information collected from microblogs during the 2014 seasonal flu vaccination in Brasil. A quantitative analysis of tweets compiled over 50 days is reported. A categorization analysis of these tweets in three major axis: content, qualifier and links, is described and preliminary results are revealed. A comparison with the data collected during the H1N1 outbreak in USA is also disclosed.