Automatic Glaucoma Detection Based on Optic Disc Segmentation and Texture Feature Extraction

The use of digital image processing techniques is prominent in medical settings for the automatic diagnosis of diseases. Glaucoma is the second leading cause of blindness in the world and it has no cure. Currently, there are treatments to prevent vision loss, but the disease must be detected in the early stages. Thus, the objective of this work is to develop an automatic detection method of Glaucoma in retinal images. The methodology used in the study were: acquisition of image database, Optic Disc segmentation, texture feature extraction in different color models and classification of images in glaucomatous or not. We obtained results of 93% accuracy

Computerized Medical Diagnosis of Melanocytic Lesions based on the ABCD approach

Melanoma is a type of skin cancer and is caused by the uncontrolled growth of atypical melanocytes. In recent decades, computer aided diagnosis is used to support medical professionals; however, there is still no globally accepted tool. In this context, similar to state-of-the-art we propose a system that receives a dermatoscopy image and provides a diagnostic if the lesion is benign or malignant. This tool is composed with next modules: Preprocessing, Segmentation, Feature Extraction, and Classification. Preprocessing involves the removal of hairs. Segmentation is to isolate the lesion. Feature extraction is considering the ABCD dermoscopy rule. The classification is performed by the Support Vector Machine. Experimental evidence indicates that the proposal has 90.63 % accuracy, 95 % sensitivity, and 83.33 % specificity on a data-set of 104 dermatoscopy images. These results are favorable considering the performance of diagnosis by traditional progress in the area of dermatology

Machine Learning methods for hepatocellular malignancies segmentation and MVI prediction

The aim of this thesis project is to automatically localize HCC tumors in the human liver and subsequently predict if the tumor will undergo microvascular infiltration (MVI), the initial stage of metastasis development. The input data for the work have been partially supplied by Sant'Orsola Hospital and partially downloaded from online medical databases. Two Unet models have been implemented for the automatic segmentation of the livers and the HCC malignancies within it. The segmentation models have been evaluated with the Intersection-over-Union and the Dice Coefficient metrics. The outcomes obtained for the liver automatic segmentation are quite good (IOU = 0.82; DC = 0.35); the outcomes obtained for the tumor automatic segmentation (IOU = 0.35; DC = 0.46) are, instead, affected by some limitations: it can be state that the algorithm is almost always able to detect the location of the tumor, but it tends to underestimate its dimensions. The purpose is to achieve the CT images of the HCC tumors, necessary for features extraction. The 14 Haralick features calculated from the 3D-GLCM, the 120 Radiomic features and the patients' clinical information are collected to build a dataset of 153 features. Now, the goal is to build a model able to discriminate, based on the features given, the tumors that will undergo MVI and those that will not. This task can be seen as a classification problem: each tumor needs to be classified either as “MVI positive” or “MVI negative”. Techniques for features selection are implemented to identify the most descriptive features for the problem at hand and then, a set of classification models are trained and compared. Among all, the models with the best performances (around 80-84% ± 8-15%) result to be the XGBoost Classifier, the SDG Classifier and the Logist Regression models (without penalization and with Lasso, Ridge or Elastic Net penalization).

Application of active and transfer learning to wound image segmentation

Wound management is a fundamental task in standard clinical practice. Automated solutions already exist for humans, but there is a lack of applications on wound management for pets. The importance of a precise and efficient wound assessment is helpful to improve diagnosis and to increase the effectiveness of treatment plans for the chronic wounds. The goal of the research was to propose an automated pipeline capable of segmenting natural light-reflected wound images of animals. Two datasets composed by light-reflected images were used in this work: Deepskin dataset, 1564 human wound images obtained during routine dermatological exams, with 145 manual annotated images; Petwound dataset, a set of 290 wound photos of dogs and cats with 0 annotated images. Two implementations of U-Net Convolutioal Neural Network model were proposed for the automated segmentation. Active Semi-Supervised Learning techniques were applied for human-wound images to perform segmentation from 10% of annotated images. Then the same models were trained, via Transfer Learning, adopting an Active Semi- upervised Learning to unlabelled animal-wound images. The combination of the two training strategies proved their effectiveness in generating large amounts of annotated samples (94% of Deepskin, 80% of PetWound) with the minimal human intervention. The correctness of automated segmentation were evaluated by clinical experts at each round of training thus we can assert that the results obtained in this thesis stands as a reliable solution to perform a correct wound image segmentation. The use of Transfer Learning and Active Semi-Supervied Learning allows to minimize labelling effort from clinicians, even requiring no starting manual annotation at all. Moreover the performances of the model with limited number of parameters suggest the implementation of smartphone-based application to this topic, helping the future standardization of light-reflected images as acknowledge medical images.

Motivating Medical Students To Learn Basic Science Concepts Using Chronic Myeloid Leukemia As An Integration Theme.

To report on the use of chronic myeloid leukemia as a theme of basic clinical integration for first year medical students to motivate and enable in-depth understanding of the basic sciences of the future physician. During the past thirteen years we have reviewed and updated the curriculum of the medical school of the Universidade Estadual de Campinas. The main objective of the new curriculum is to teach the students how to learn to learn. Since then, a case of chronic myeloid leukemia has been introduced to first year medical students and discussed in horizontal integration with all themes taught during a molecular and cell biology course. Cell structure and components, protein, chromosomes, gene organization, proliferation, cell cycle, apoptosis, signaling and so on are all themes approached during this course. At the end of every topic approached, the students prepare in advance the corresponding topic of clinical cases chosen randomly during the class, which are then presented by them. During the final class, a paper regarding mutations in the abl gene that cause resistance to tyrosine kinase inhibitors is discussed. After each class, three tests are solved in an interactive evaluation. The course has been successful since its beginning, 13 years ago. Great motivation of those who participated in the course was observed. There were less than 20% absences in the classes. At least three (and as many as nine) students every year were interested in starting research training in the field of hematology. At the end of each class, an interactive evaluation was performed and more than 70% of the answers were correct in each evaluation. Moreover, for the final evaluation, the students summarized, in a written report, the molecular and therapeutic basis of chronic myeloid leukemia, with scores ranging from 0 to 10. Considering all 13 years, a median of 78% of the class scored above 5 (min 74%-max 85%), and a median of 67% scored above 7. Chronic myeloid leukemia is an excellent example of a disease that can be used for clinical basic integration as this disorder involves well known protein, cytogenetic and cell function abnormalities, has well-defined diagnostic strategies and a target oriented therapy.

Mentors, students, and the undergraduate medical course: a virtuous circle

BACKGROUND: Mentoring Programs have been developed in several medical schools, but few studies have investigated the mentors'perspective. PURPOSES: To explore mentors'perceptions regarding their experience. METHODS: Mentors at a medical school were invited to participate in an in-depth interview including questions on satisfaction, difficulties, and perception of changes resulting from the program. RESULTS: Mentors' satisfaction and difficulties are strongly associated with students'involvement in the activity. Mentors believe changes observed in students were more related to life issues; for some mentors, there is no recognition or awareness of the program. However, most of the mentors acknowledged important changes in relation to themselves: as teachers, faculty members, and individuals. CONCLUSION: Attendance is crucial for both the mentoring relationship and strengthening of the program. Students involved in the activity motivate mentors in teaching and curriculum development, thereby creating a virtuous circle and benefiting undergraduate medical education as a whole.

Drug consumption among medical students in São Paulo, Brazil: influences of gender and academic year

OBJECTIVE: To analyze alcohol, tobacco and other drug use among medical students. METHOD: Over a five-year period (1996-2001), we evaluated 457 students at the Universidade de São Paulo School of Medicine, located in São Paulo, Brazil. The students participated by filling out an anonymous questionnaire on drug use (lifetime, previous 12 months and previous 30 days). The influence that gender and academic year have on drug use was also analyzed. RESULTS: During the study period, there was an increase in the use of illicit drugs, especially inhalants and amphetamines, among the medical students evaluated. Drug use (except that of marijuana and inhalants) was comparable between the genders, and academic year was an important influencing factor. DISCUSSION: Increased inhalant use was observed among the medical students, especially among males and students in the early undergraduate years. This is suggestive of a specific behavioral pattern among medical students. Our findings corroborate those of previous studies. CONCLUSION: Inhalant use is on the rise among medical students at the Universidade de São Paulo School of Medicine. Because of the negative health effects of illicit drug use, further studies are needed in order to deepen the understanding of this phenomenon and to facilitate the development of preventive measures.

Oral Health Promotion Through an Online Training Program for Medical Students

The objective of this study was to compare the impact on knowledge and counseling skills of face-to-face and Internet-based oral health training programs on medical students. Participants consisted of 148 (82 percent) of the 180 invited students attending their fifth academic year at the Faculty of Medicine, University of Sao Paulo, Brasil, in 2007. The interventions took place during a three-month training period in the clinical Center for Health Promotion, which comprised part of a clerkship in Internal Medicine. The students were divided into four groups: 1) Control Group (Control), with basic intervention; 2) Brochure Group (Br), with basic intervention plus complete brochure with oral health themes; 3) Cybertutor Group (Cy), with basic intervention plus access to an Internet-based training program about oral health themes; and 4) Cybertutor + Contact Group (Cy+C), the same as Cy plus brief proactive contact with a tutor. The impact of these interventions on student knowledge was measured with pre- and post assessments, and student skills in asking and counseling about oral health were assessed with an objective structured clinical examination (OSCE). Multivariate logistic regression models were applied to identify the odds ratios of scoring above Control's medians on the final assessment and the OSCE. In the results, Cy+C performed significantly better than Control on both the final assessment (OR 9.4; 95% CI 2.7-32.8) and the OSCE (OR 5.6; 95% CI 1.9-16.3) and outperformed all the other groups. The Cy+C group showed the most significant increase in knowledge and the best skills in asking and counseling about oral health.

The characteristics of depressive symptoms in medical students during medical education and training: a cross-sectional study

Background: Medical education and training can contribute to the development of depressive symptoms that might lead to possible academic and professional consequences. We aimed to investigate the characteristics of depressive symptoms among 481 medical students (79.8% of the total who matriculated). Methods: The Beck Depression Inventory (BDI) and cluster analyses were used in order to better describe the characteristics of depressive symptoms. Medical education and training in Brazil is divided into basic (1(st) and 2(nd) years), intermediate (3(rd) and 4(th) years), and internship (5(th) and 6(th) years) periods. The study organized each item from the BDI into the following three clusters: affective, cognitive, and somatic. Statistical analyses were performed using analysis of variance (ANOVA) with post-hoc Tukey corrected for multiple comparisons. Results: There were 184 (38.2%) students with depressive symptoms (BDI > 9). The internship period resulted in the highest BDI scores in comparison to both the basic (p < .001) and intermediate (p < .001) periods. Affective, cognitive, and somatic clusters were significantly higher in the internship period. An exploratory analysis of possible risk factors showed that females (p = .020) not having a parent who practiced medicine (p = .016), and the internship period (p = .001) were factors for the development of depressive symptoms. Conclusion: There is a high prevalence towards depressive symptoms among medical students, particularly females, in the internship level, mainly involving the somatic and affective clusters, and not having a parent who practiced medicine. The active assessment of these students in evaluating their depressive symptoms is important in order to prevent the development of co-morbidities and suicide risk.

Genetic Background of Patients from a University Medical Center in Manhattan: Implications for Personalized Medicine

Background: The rapid progress currently being made in genomic science has created interest in potential clinical applications; however, formal translational research has been limited thus far. Studies of population genetics have demonstrated substantial variation in allele frequencies and haplotype structure at loci of medical relevance and the genetic background of patient cohorts may often be complex. Methods and Findings: To describe the heterogeneity in an unselected clinical sample we used the Affymetrix 6.0 gene array chip to genotype self-identified European Americans (N = 326), African Americans (N = 324) and Hispanics (N = 327) from the medical practice of Mount Sinai Medical Center in Manhattan, NY. Additional data from US minority groups and Brazil were used for external comparison. Substantial variation in ancestral origin was observed for both African Americans and Hispanics; data from the latter group overlapped with both Mexican Americans and Brazilians in the external data sets. A pooled analysis of the African Americans and Hispanics from NY demonstrated a broad continuum of ancestral origin making classification by race/ethnicity uninformative. Selected loci harboring variants associated with medical traits and drug response confirmed substantial within-and between-group heterogeneity. Conclusion: As a consequence of these complementary levels of heterogeneity group labels offered no guidance at the individual level. These findings demonstrate the complexity involved in clinical translation of the results from genome-wide association studies and suggest that in the genomic era conventional racial/ethnic labels are of little value.

Effects of meal size and proximal-distal segmentation on gastric activity

AIM: To evaluate the effects of meal size and three segmentations on intragastric distribution of the meal and gastric motility, by scintigraphy. METHODS: Twelve healthy volunteers were randomly assessed, twice, by scintigraphy. The test meal consisted of 60 or 180 mL of yogurt labeled with 64 MBq (99m)Tc-tin colloid. Anterior and posterior dynamic frames were simultaneously acquired for 18 min and all data were analyzed in MatLab. Three proximal-distal segmentations using regions of interest were adopted for both meals. RESULTS: Intragastric distribution of the meal between the proximal and distal compartments was strongly influenced by the way in which the stomach was divided, showing greater proximal retention after the 180 mL. An important finding was that both dominant frequencies (1 and 3 cpm) were simultaneously recorded in the proximal and distal stomach; however, the power ratio of those dominant frequencies varied in agreement with the segmentation adopted and was independent of the meal size. CONCLUSION: It was possible to simultaneously evaluate the static intragastric distribution and phasic contractility from the same recording using our scintigraphic approach. (C) 2010 Baishideng. All rights reserved.

Gene Expression Noise in Spatial Patterning: hunchback Promoter Structure Affects Noise Amplitude and Distribution in Drosophila Segmentation

Positional information in developing embryos is specified by spatial gradients of transcriptional regulators. One of the classic systems for studying this is the activation of the hunchback (hb) gene in early fruit fly (Drosophila) segmentation by the maternally-derived gradient of the Bicoid (Bcd) protein. Gene regulation is subject to intrinsic noise which can produce variable expression. This variability must be constrained in the highly reproducible and coordinated events of development. We identify means by which noise is controlled during gene expression by characterizing the dependence of hb mRNA and protein output noise on hb promoter structure and transcriptional dynamics. We use a stochastic model of the hb promoter in which the number and strength of Bcd and Hb (self-regulatory) binding sites can be varied. Model parameters are fit to data from WT embryos, the self-regulation mutant hb(14F), and lacZ reporter constructs using different portions of the hb promoter. We have corroborated model noise predictions experimentally. The results indicate that WT (self-regulatory) Hb output noise is predominantly dependent on the transcription and translation dynamics of its own expression, rather than on Bcd fluctuations. The constructs and mutant, which lack self-regulation, indicate that the multiple Bcd binding sites in the hb promoter (and their strengths) also play a role in buffering noise. The model is robust to the variation in Bcd binding site number across a number of fly species. This study identifies particular ways in which promoter structure and regulatory dynamics reduce hb output noise. Insofar as many of these are common features of genes (e. g. multiple regulatory sites, cooperativity, self-feedback), the current results contribute to the general understanding of the reproducibility and determinacy of spatial patterning in early development.

Improvements on ICA mixture models for image pre-processing and segmentation

Today several different unsupervised classification algorithms are commonly used to cluster similar patterns in a data set based only on its statistical properties. Specially in image data applications, self-organizing methods for unsupervised classification have been successfully applied for clustering pixels or group of pixels in order to perform segmentation tasks. The first important contribution of this paper refers to the development of a self-organizing method for data classification, named Enhanced Independent Component Analysis Mixture Model (EICAMM), which was built by proposing some modifications in the Independent Component Analysis Mixture Model (ICAMM). Such improvements were proposed by considering some of the model limitations as well as by analyzing how it should be improved in order to become more efficient. Moreover, a pre-processing methodology was also proposed, which is based on combining the Sparse Code Shrinkage (SCS) for image denoising and the Sobel edge detector. In the experiments of this work, the EICAMM and other self-organizing models were applied for segmenting images in their original and pre-processed versions. A comparative analysis showed satisfactory and competitive image segmentation results obtained by the proposals presented herein. (C) 2008 Published by Elsevier B.V.

Use of Medical Subject Headings (MeSH) in Portuguese for categorizing web-based healthcare content

Introduction: Internet users are increasingly using the worldwide web to search for information relating to their health. This situation makes it necessary to create specialized tools capable of supporting users in their searches. Objective: To apply and compare strategies that were developed to investigate the use of the Portuguese version of Medical Subject Headings (MeSH) for constructing an automated classifier for Brazilian Portuguese-language web-based content within or outside of the field of healthcare, focusing on the lay public. Methods: 3658 Brazilian web pages were used to train the classifier and 606 Brazilian web pages were used to validate it. The strategies proposed were constructed using content-based vector methods for text classification, such that Naive Bayes was used for the task of classifying vector patterns with characteristics obtained through the proposed strategies. Results: A strategy named InDeCS was developed specifically to adapt MeSH for the problem that was put forward. This approach achieved better accuracy for this pattern classification task (0.94 sensitivity, specificity and area under the ROC curve). Conclusions: Because of the significant results achieved by InDeCS, this tool has been successfully applied to the Brazilian healthcare search portal known as Busca Saude. Furthermore, it could be shown that MeSH presents important results when used for the task of classifying web-based content focusing on the lay public. It was also possible to show from this study that MeSH was able to map out mutable non-deterministic characteristics of the web. (c) 2010 Elsevier Inc. All rights reserved.

Building a Open Source Framework for Virtual Medical Training

This paper presents a framework to build medical training applications by using virtual reality and a tool that helps the class instantiation of this framework. The main purpose is to make easier the building of virtual reality applications in the medical training area, considering systems to simulate biopsy exams and make available deformation, collision detection, and stereoscopy functionalities. The instantiation of the classes allows quick implementation of the tools for such a purpose, thus reducing errors and offering low cost due to the use of open source tools. Using the instantiation tool, the process of building applications is fast and easy. Therefore, computer programmers can obtain an initial application and adapt it to their needs. This tool allows the user to include, delete, and edit parameters in the functionalities chosen as well as storing these parameters for future use. In order to verify the efficiency of the framework, some case studies are presented.