Marco Gutierrez

University of São Paulo | USP · Instituto do Coração (InCor)

Objetivos: A desidentificação de narrativas clínicas é essencial para proteger a privacidade dos pacientes e garantir a conformidade com as regulamentações. No entanto, é uma tarefa complexa devido aos distintos tipos de entidades a serem desidentificadas e à necessidade de processar os textos localmente, por questões de segurança e privacidade. Mé...

Given a set of Electronic Health Records (EHRs), how can we semantically model the available concepts and provide tools for data analysis? EHRs following a common data model (CDM) usually provide meaningful organization and vocabulary to health-related databases, prompting data interoperability. However, hidden relationships among attributes within...

Accurate sleep stage classification is crucial for diagnosing sleep disorders and evaluating sleep quality. While polysomnography (PSG) remains the gold standard, photoplethysmography (PPG) is more practical due to its afford-ability and widespread use in wearable devices. However, state-of-the-art sleep staging methods often require prolonged cont...

Aim: Aortic elongation can result from age-related changes, congenital factors, aneurysms, or conditions affecting blood vessel elasticity. It is associated with cardiovascular diseases and severe complications like aortic aneurysms and dissection. We assess qualitatively and quantitatively explainable methods to understand the decisions of a deep...

Diabetes is a chronic condition which prevention and control is done mostly by minimally invasive devices. In this work, we propose a non-invasive method based on photoplethysmography (PPG) for cost-effective and discomfort-free diabetes detection and prevention. We used PPG signal features and patient metadata from a public dataset for classifying...

Sleep is a crucial aspect to overall health, impacting mental and physical well-being. The classification of sleep stages is an important step to assess sleep quality, and Photoplethysmography (PPG) has been demonstrated to be an effective signal for this task. Recent works in this area usually employ complex methods that may be unfeasible to be de...

Hypertension, a leading contributor to cardiovascular morbidity, underscores the need for accurate and continuous blood pressure (BP) monitoring. While traditional cuff-based methods offer periodic measurements, they fall short of providing real-time BP monitoring, driving the demand for innovative, non-invasive solutions. Photoplethysmography (PPG...

Background COVID-19 lung sequelae can impact the course of patient lives. We investigated the evolution of pulmonary abnormalities in post-COVID-19 patients 18–24 months after hospital discharge. Methods A cohort of COVID-19 patients admitted to the Hospital das Clínicas da Faculdade de Medicina da USP in São Paulo, Brazil, between March and Augus...

In the domain of ECG arrhythmia classification tasks, deep neural networks have emerged as the leading approach. However, their lack of transparency presents challenges in understanding their decision-making processes, which may potentially mask biases and other flaws within datasets and model performance. Moreover, the question of whether decision...

The electronic health record (EHR) data, widely used by hospitals and healthcare professionals, contain valuable information about the patient and treatments and has become increasingly relevant to clinical natural language processing (NLP) tasks. Although the growing number of EHR systems, these medical data contain sensitive information and canno...

Aortic Elongation can result from age-related changes, congenital factors, aneurysms, or conditions affecting blood vessel elasticity. It is associated with cardiovascular diseases and severe complications like aortic aneurysms and dissection. In this work, we evaluated the performance of deep learning models (DenseNet and EfficientNet) for aortic...

The electrocardiogram (ECG) serves as a valuable diagnostic tool, providing crucial information about life-threatening cardiac conditions such as Atrial Fibrillation and Myocardial Infarction.A prompt and efficient assessment of ECG exams in environments like emergency rooms (ERs) can significantly improve the chances of survival for high-risk pati...

p>The electrocardiogram (ECG) serves as a valuable diagnostic tool, providing crucial information about life-threatening cardiac conditions such as Atrial Fibrillation and Myocardial Infarction. A prompt and efficient assessment of ECG exams in environments like emergency rooms (ERs) can significantly improve the chances of survival for high-risk p...

This study investigates the use of standard 12-lead ECG records from six largest PhysioNet CinC Challenge 2021 databases and a private database to differentiate Atrial Fibrillation from Atrial Flutter. Image-based and one-dimensional-based Deep Learning models were considered to perform the classification using different 1D and 2D Convolutional Neu...

The resting 12-lead electrocardiogram (ECG) is a widely used diagnostic tool in modern medicine, providing crucial insights into various heart conditions. Recently, the application of Artificial Intelligence (AI) to infer health-related information from the 12-lead ECG has gained significant interest. In this study, we propose a deep-learning appro...

Strain represents the quantification of regional tissue deformation within a given area. Myocardial strain has demonstrated considerable utility as an indicator for the assessment of cardiac function. Notably, it exhibits greater sensitivity in detecting subtle myocardial abnormalities compared to conventional cardiac function indices, like left ve...

Sleep is a crucial aspect of our overall health and well-being. It plays a vital role in regulating our mental and physical health, impacting our mood, memory, and cognitive function to our physical resilience and immune system. The classification of sleep stages is a mandatory step to assess sleep quality, providing the metrics to estimate the qua...

Diabetes is a prevalent chronic condition that compromises the health of millions of people worldwide. Minimally invasive methods are needed to prevent and control diabetes but most devices for measuring glucose levels are invasive and not amenable for continuous monitoring. Here, we present an alternative method to overcome these shortcomings base...

The coronavirus disease (COVID-19) pandemic leveraged telemedicine worldwide mainly due to the need for social distancing, patient safety, and infection prevention. The Hospital das Clínicas da Faculdade de Medicina da Universidade de São Paulo (HCFMUSP) was a key reference site in the treatment of COVID-19 severe cases in the country. To continue...

Photoplethysmography (PPG) is a non-invasive technology that measures changes in blood volume in the microvascular bed of tissue. It is commonly used in medical devices such as pulse oximeters and wrist worn heart rate monitors to monitor cardiovascular hemodynamics. PPG allows for the assessment of parameters (e.g., heart rate, pulse waveform, and...

Cardiomegaly is a medical disorder characterized by an enlargement of the heart. Many works propose to automatically detect cardiomegaly through chest X-rays. However, most of them are based on deep learning models, known for their lack of interpretability. This work propose a deep learning model for the detection of cardiomegaly based on chest x-r...

This study explores the application of image-based deep learning techniques to distinguish between Atrial Fibrillation (AFib) and Atrial Flutter (AFlut) using images of standard 12-lead ECG exams from a private database. By implementing a MobileNet Convolutional Neural Network architecture, we achieve a high classification performance, with an accu...

Cardiovascular diseases are the leading causes of death, and blood pressure (BP) monitoring is essential for prevention, diagnosis, assessment, and treatment. Photoplethysmography (PPG) is a low-cost opto-electronic technique for BP measurement that allows the acquisition of a modulated light signal highly correlated with BP. There are several repo...

p>Cardiovascular diseases are the leading causes of death in humans, and blood pressure (BP) monitoring is an important procedure to prevent, diagnose, assess, and treat these diseases, thereby avoiding more severe conditions. The most reliable and common techniques of measuring BP use sphygmomanometers, but they are more suited to single measureme...

Background Coronavirus disease (COVID-19) survivors exhibit multisystemic alterations after hospitalization. Little is known about long-term imaging and pulmonary function of hospitalized patients intensive care unit (ICU) who survive COVID-19. We aimed to investigate long-term consequences of COVID-19 on the respiratory system of patients discharg...

Continuous rhythm monitoring using wearable devices is a potential tool for early identification of atrial fibril-lation (AF), the most frequent cardiac arrhythmia (with 0,51% worldwide prevalence, increasing with time), and is also a tool for remote monitoring patients after cardiac surgery. However, AF detection directly through wearable devices...

Blood pressure (BP) monitoring is a basic procedure for the physiological measurement of the cardiovascular system, especially because high BP, although preventable, is a major risk for stroke, heart failure, and other serious conditions. Photoplethysmography (PPG) is a promising technology developed to allow non-invasive, regular, or even continuo...

p>Cardiovascular diseases are the leading causes of death in humans, and blood pressure (BP) monitoring is an important procedure to prevent, diagnose, assess, and treat these diseases, thereby avoiding more severe conditions. The most reliable and common techniques of measuring BP use sphygmomanometers, but they are more suited to single measureme...

Cardiovascular diseases are the leading causes of death in humans, and blood pressure (BP) monitoring is an important procedure to prevent, diagnose, assess, and treat these diseases, thereby avoiding more severe conditions. The most reliable and common techniques of measuring BP use sphygmomanometers, but they are more suited to single measurement...

Hepatocellular carcinoma (HCC) has become the 4th leading cause of cancer-related deaths, with high social, economical and health implications. Imaging techniques such as multiphase computed tomography (CT) have been successfully used for diagnosis of liver tumors such as HCC in a feasible and accurate way and its interpretation relies mainly on co...

The amount of data daily generated by different sources grows exponentially and brings new challenges to the information technology experts. The recorded data usually include heterogeneous attribute types, such as the traditional date, numerical, textual, and categorical information, as well as complex ones, such as images, videos, and multidimensi...

Real-world applications generate large amounts of images every day. With the generalized use of social media, users frequently share images acquired by smartphones. Also, hospitals, clinics, exhibits, factories, and other facilities generate images with potential use for many applications. Processing the generated images usually requires feature ex...

Accurate quantification of myocardium strain in magnetic resonance images is important to correctly diagnose and monitor cardiac diseases. Currently, available methods to estimate motion are based on tracking brightness pattern differences between images. In cine-MR images, the myocardium interior presents an inhered homogeneity, which reduces the...

Objective This study aimed to propose a simple, accessible and low-cost predictive clinical model to detect lung lesions due to COVID-19 infection. Design This prospective cohort study included COVID-19 survivors hospitalised between 30 March 2020 and 31 August 2020 followed-up 6 months after hospital discharge. The pulmonary function was assessed...

Physicians work at a very tight schedule and need decision-making support tools to help on improving and doing their work in a timely and dependable manner. Examining piles of sheets with test results and using systems with little visualization support to provide diagnostics is daunting, but that is still the usual way for the physicians' daily pro...

Physicians work at a very tight schedule and need decision-making support tools to help on improving and doing their work in a timely and dependable manner. Examining piles of sheets with test results and using systems with little visualization support to provide diagnostics is daunting, but that is still the usual way for the physicians' daily pro...

Magnetic resonance imaging (MRI) is a widely known medical imaging technique used to assess the heart function. Deep learning (DL) models perform several tasks in cardiac MRI (CMR) images with good efficacy, such as segmentation, estimation, and detection of diseases. Many DL models based on convolutional neural networks (CNN) were improved by dete...

Background: The importance of blockchain-based architectures for personal health record (PHR) lies in the fact that they are thought and developed to allow patients to control and at least partly collect their health data. Ideally, these systems should provide the full control of such data to the respective owner. In spite of this importance, most...

Avanços recentes na área de inteligência artificial, especialmente em aprendizagem profunda, levaram a um desempenho promissor em muitas tarefas de análise e proces- samento de imagens médicas. Como exame radiológico mais comumente realizado, a radiografia de tórax é uma modalidade particularmente importante para a qual uma variedade de métodos e a...

Objective. Evaluate a platform for daily survey of COVID-19 signs and symptoms in health employees. The platform was developed to indicate the need of additional individual diagnostic procedures, assist institutional planning to prevent the spread of the virus and sustain the hospital operations during the pandemic. Methods. We used information fro...

Conference slides

With the high demand in data science, the organization and preparation of databases became critical activities, consuming more than 80% of the project effort. In the medical domain, many hospitals already use a myriad of technologies and information systems for medical records and images, but they do not always adopt standards of uniform and intero...

BACKGROUND The importance of blockchain-based architectures for personal health record (PHR) lies in the fact that they are thought and developed to allow patients to control and at least partly collect their health data. Ideally, these systems should provide the full control of such data for the respective owner. In spite of this importance, most...

CNN (Convolutional Neural Network) models have been successfully used for segmentation of the left ventricle (LV) in cardiac MRI (Magnetic Resonance Imaging), providing clinical measurements. In practice, two questions arise with deployment of CNNs: (1) when is it better to use a shallow model instead of a deeper one? (2) how the size of a dataset...

In this paper, we present FeatSet, a compilation of visual features extracted from open image datasets reported in the literature. FeatSet has a collection of 11 visual features, consisting of color, texture, and shape representations of the images acquired from 13 datasets. We organized the available features in a standard collection, including th...

With the COVID-19 pandemic, many hospitals have collected Electronic Health Records (EHRs) from patients and shared them publicly. EHRs include heterogeneous attribute types, such as image exams, numerical, textual, and categorical information. Simply posing similarity queries over EHRs can underestimate the semantics and potential information of p...

Atrial fibrillation (AF) is a common arrhythmia (0.5% worldwide prevalence) associated with an increased risk of various cardiovascular disorders, including stroke. Automated routine AF detection by Electrocardiogram (ECG) is based on the analysis of one-dimensional ECG signals and requires dedicated software for each type of device, limiting its w...

Magnetic resonance imaging (MRI) is a widely known medical imaging technique used to assess the heart function. Deep learning (DL) models perform several tasks in cardiac MRI (CMR) images with good efficacy, such as segmentation, estimation, and detection of diseases. Many DL models based on convolutional neural networks (CNN) were improved by dete...

CNN (Convolutional Neural Network) models have been successfully used for segmentation of the left ventricle (LV) in cardiac MRI (Magnetic Resonance Imaging), providing clinical measurements.In practice, two questions arise with deployment of CNNs: 1) when is it better to use a shallow model instead of a deeper one? 2) how the size of a dataset mig...

Computerized Tomography is very important for lung disease diagnostics, including computer assisted methods. Lung segmentation is usually a first step in further sophisticated methods of diagnosis. If in one hand, deep learning methods have state-of-the-art performance, they aren't as simple to apply compared to classical methods, sometimes requiri...

The current COVID-19 pandemic has promoted the periodic release of several health databases aimed at discovering relationships in the data, detecting similar problems in patients, and studying the evolution of the disease. A way to exploit the data is to use visualization techniques, which can lead to the discovery of insights and patterns, as well...

Objective: To describe the implementation of a Tele-ICU program during the COVID-19 pandemic, as well as to describe and analyze the results of the first four months of operation of the program. Methods: This was a descriptive observational study of the implementation of a Tele-ICU program, followed by a retrospective analysis of clinical data o...

COVID-19 is a highly contagious disease that can cause severe pneumonia. Patients with pneumonia undergo chest X-rays (XR) to assess infiltrates that identify the infection. However, the radiographic characteristics of COVID-19 are similar to the other acute respiratory syndromes, hindering the imaging diagnosis. In this work, we proposed identifyi...

Background: COVID-19 patients may have long-term pulmonary consequences. This study developed a simple and accessible screening protocol to detect chronic lung lesions due to COVID-19 infection. Methods: This prospective cohort study included COVID-19 survivors hospitalized between March 30 and August 31, 2020, and re-examined 6 months after admis...

Objective: To develop a platform for daily survey of COVID-19 signs and symptoms in health employees to indicatethe need of additional individual diagnostic procedures and to assist institutional planning to prevent the spread ofthe virus and sustain the hospital operations during the pandemic. Methods: We used information from a recentmeta-analysi...