RAMON ALFREDO MORENO
Índice h a partir de 2011
4
Projetos de Pesquisa
Unidades Organizacionais
Instituto do Coração, Hospital das Clínicas, Faculdade de Medicina
LIM/65, Hospital das Clínicas, Faculdade de Medicina
LIM/65, Hospital das Clínicas, Faculdade de Medicina
9 resultados
Resultados de Busca
Agora exibindo 1 - 9 de 9
conferenceObject Fully Automated Quantification of Cardiac Indices from Cine MRI Using a Combination of Convolution Neural Networks(2020) PEREIRA, Renato F.; REBELO, Marina S.; MORENO, Ramon A.; MARCO, Anderson G.; LIMA, Daniel M.; ARRUDA, Marcelo A. F.; KRIEGER, Jose E.; GUTIERREZ, Marco A.Cardiovascular magnetic resonance imaging (CMRI) is one of the most accurate non-invasive modalities for evaluation of cardiac function, especially the left ventricle (LV). In this modality, the manual or semi-automatic delineation of LV by experts is currently the standard clinical practice for chambers segmentation. Despite these efforts, global quantification of LV remains a challenge. In this work, a combination of two convolutional neural network (CNN) architectures for quantitative evaluation of the LV is described, which estimates the cavity and the myocardium areas, endocardial cavity dimensions in three directions, and the myocardium regional wall thickness in six radial directions. The method was validated in CMRI exams of 56 patients (LVQuan19 dataset) and evaluated by metrics Dice Index, Mean Absolute Error, and Correlation with superior performance compared to the state-of-the-art methods. The combination of the CNN architectures provided a simpler yet fully automated approach, requiring no specialist interaction.- IoT Medical Device Architecture to Estimate Non-invasive Arterial Blood Pressure(2022) MORENO, Ramon; DIAS, Felipe; ARRUDA, Marcelo; OLIVEIRA, Filipe; BULHOES, Thiago; KRIEGER, Jose; GUTIERREZ, MarcoHigh blood pressure (BP) is the leading cause of death worldwide. Besides being a treatable condition, alongside medication and a healthy diet, it requires regular BP measurements to assess whether a patient is properly responding to treatment. There have been many attempts to use the photoplethysmography (PPG) signal to estimate BP continuously, but there has yet to be an effective solution. This work presents our efforts to develop a new method for estimating BP from PPG and infrastructure to collect, process, and store this information. PPG signal is measured from a smartband; our App reads the data from the smartband to a smartphone, processes them using a machine learning method, and estimates BP, which is sent to a server that stores and displays the data
- Automated radiographic bone suppression with deep convolutional neural networks(2021) CARDENAS, Diego Armando Cardona; FERREIRA JUNIOR, Jose Raniery; MORENO, Ramon Alfredo; REBELO, Marina de Fatima de Sa; KRIEGER, Jose Eduardo; GUTIERREZ, Marco AntonioDual-energy subtraction (DES) is a technique that separates soft tissue from bones in a chest radiograph (CR). As DES requires specialized equipment, we propose an automatic method based on convolutional neural networks (CNNs) to generate virtual soft tissue images. A dataset comprising 35 pairs of CR and its soft-tissue version split in training (28 image pairs) and testing (7 image pairs) sets were used with data augmentation. We tested two types of images: the lung region's cropped image and the segmented lung image. The ribs suppression was treated as a local problem, so each image was divided into 784 patches. The U-Net architecture was used to perform bone suppression. We tested two types of loss functions: mean squared error (L-mse) and L-sm, which combines L-mse with the structural similarity index measure (SSIM). Due to the patches overlapping, it was necessary to interpolate the gray levels on the reconstructed image from the predicted patches. Evaluations were based on SSIM and root mean square error (RMSE) over the reconstructed lung area. The combination that presented the best results used the loss L-sm and the segmented lung image as input to the U-Net (SSIM of 0.858 and RMSE of 0.033). We observed that the U-Net has poor performance when trained with cropped images containing all information from the chest cavity and how the loss using local information can improve CR rib bone suppression. Our results suggest that it is possible removing the rib bones accurately in CR using CNN and a patch-based approach.y
- Artificial Intelligence-Driven Screening System for Rapid Image-Based Classification of 12-Lead ECG Exams: A Promising Solution for Emergency Room Prioritization(2023) DIAS, Felipe Meneguitti; RIBEIRO, Estela; MORENO, Ramon Alfredo; RIBEIRO, Adele Helena; SAMESIMA, Nelson; PASTORE, Carlos Alberto; KRIEGER, Jose Eduardo; GUTIERREZ, Marco AntonioThe 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 such as Emergency Rooms (ERs) can significantly enhance the chances of survival for high-risk patients. Despite the presence of numerous works on ECG classification, most of these studies have concentrated on one-dimensional ECG signals, which are commonly found in publicly available ECG datasets. Nevertheless, the practical relevance of such methods is limited in hospital settings, where ECG exams are usually stored as images. In this study, we have developed an artificial intelligence-driven screening system specifically designed to analyze 12-lead ECG images. Our proposed method has been trained on an extensive dataset comprising 99,746 12-lead ECG exams collected from the ambulatory section of a tertiary hospital. The primary goal was to precisely classify the exams into three classes: Normal (N), Atrial Fibrillation (AFib), and Other (O). The evaluation of our approach yielded AUROC scores of 93.2%, 99.2%, and 93.1% for N, AFib, and O, respectively. To further validate our approach, we conducted evaluations using the 2018 China Physiological Signal Challenge (CPSC) database. In this evaluation, we achieved AUROC scores of 91.8%, 97.5%, and 70.4% for the classes N, AFib, and O, respectively. Additionally, we assessed our method using 1,074 exams acquired in the ER and obtained AUROC values of 98.3%, 98.0%, and 97.7% for the classes N, AFib, and O, respectively. Furthermore, we developed and deployed a system with a trained model within the ER of a tertiary hospital for research purposes. This system automatically retrieves newly captured ECG chart images from the Picture Archiving and Communication System (PACS) within the ER. These images undergo necessary preprocessing steps and serve as input for our proposed classification method. This comprehensive approach established an efficient and versatile end-to-end framework for ECG classification. The results of our study highlight the potential of leveraging artificial intelligence in the screening of ECG exams, offering a promising solution for the rapid assessment and prioritization of patients in the ER.
- A general fully automated deep-learning method to detect cardiomegaly in chest x-rays(2021) FERREIRA-JUNIOR, Jose Raniery; CARDENAS, Diego Armando Cardona; MORENO, Ramon Alfredo; REBELO, Marina de Fdtima de Sa; KRIEGER, Jose Eduardo; GUTIERREZ, Marco AntonioCardiomegaly is a medical condition that leads to an increase in cardiac size. It can be manually assessed using the cardiothoracic ratio from chest x-rays (CXRs). However, as that task can be challenging in such limited examinations, we propose the fully automated cardiomegaly detection in CXR. For this, we first trained convolutional networks (ConvNets) to classify the CXR as positive or negative to cardiomegaly and then evaluated the generalization potential of the trained ConvNets on independent cohorts. This work used frontal CXR images from a public dataset for training/testing and another public and one private dataset to test the models' generalization externally. Training and testing were performed using images cropped with a previously developed U-Net model. Experiments were performed with five topologically different ConvNets, data augmentation techniques, and a 50-50 class-weighing strategy to improve performance and reduce the possibility of bias to the majority class. The receiver operating characteristic curve assessed the performance of the models. DenseNet yielded the highest area under the curve (AUC) on testing (0.818) and external validation (0.809) datasets. Moreover, DenseNet obtained the highest sensitivity overall, yielding up to 0.971 on the private dataset with patients from our hospital. Therefore, DenseNet had a statistically higher potential to identify cardiomegaly. The proposed models, especially those trained with DenseNet convolutional core, automatically detected cardiomegaly with high sensitivity. To the best of our knowledge, this was the first work to design a novel general model for classifying specific deep-learning patterns of cardiomegaly in CXRs.
- Novel Chest Radiographic Biomarkers for COVID-19 Using Radiomic Features Associated with Diagnostics and Outcomes(2021) FERREIRA JUNIOR, Jose Raniery; CARDENAS, Diego Armando Cardona; MORENO, Ramon Alfredo; REBELO, Marina de Fatima de Sa; KRIEGER, Jose Eduardo; GUTIERREZ, Marco AntonioCOVID-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 identifying quantitative/radiomic biomarkers for COVID-19 to support XR assessment of acute respiratory diseases. This retrospective study used different cohorts of 227 patients diagnosed with pneumonia; 49 of them had COVID-19. Automatically segmented images were characterized by 558 quantitative features, including gray-level histogram and matrices of co-occurrence, run-length, size zone, dependence, and neighboring gray-tone difference. Higher-order features were also calculated after applying square and wavelet transforms. Mann-Whitney U test assessed the diagnostic performance of the features, and the log-rank test assessed the prognostic value to predict Kaplan-Meier curves of overall and deterioration-free survival. Statistical analysis identified 51 independently validated radiomic features associated with COVID-19. Most of them were wavelet-transformed features; the highest performance was the small dependence matrix feature of ""low gray-level emphasis"" (area under the curve of 0.87, sensitivity of 0.85, p<0.001). Six features presented short-term prognostic value to predict overall and deterioration-free survival. The features of histogram ""mean absolute deviation"" and size zone matrix ""non-uniformity"" yielded the highest differences on Kaplan-Meier curves with a hazard ratio of 3.20 (p<0.05). The radiomic markers showed potential as quantitative measures correlated with the etiologic agent of acute infectious diseases and to stratify short-term risk of COVID-19 patients.
conferenceObject Multi-View Ensemble Convolutional Neural Network to Improve Classification of Pneumonia in Low Contrast Chest X-Ray Images(2020) FERREIRA JUNIOR, Jose Raniery; CARDENAS, Diego Armando Cardona; MORENO, Ramon Alfredo; REBELO, Marina de Fatima de Sa; KRIEGER, Jose Eduardo; GUTIERREZ, Marco AntonioPneumonia is one of the leading causes of childhood mortality worldwide. Chest x-ray (CXR) can aid the diagnosis of pneumonia, but in the case of low contrast images, it is important to include computational tools to aid specialists. Deep learning is an alternative because it can identify patterns automatically, even in low-resolution images. We propose herein a convolutional neural network (CNN) architecture with different training strategies towards detecting pneumonia on CXRs and distinguishing its subforms of bacteria and virus. We also evaluated different image pre-processing methods to improve the classification. This study used CXRs from pediatric patients from a public pneumonia CXR dataset. The pre-processing methods evaluated were image cropping and histogram equalization. To classify the images, we adopted the VGG16 CNN and replaced its fully-connected layers with a customized multilayer perceptron. With this architecture, we proposed and evaluated four different training strategies: original CXR image (baseline), chest-cavity-cropped image (A), and histogram-equalized segmented image (B). The last strategy method (C) implemented is based on ensemble between strategies A and B. The performance was assessed by the area under the ROC curve (AUC) with 95% confidence interval (CI), accuracy, sensitivity, specificity, and F1-score. The ensemble model C yielded the highest performances: AUC of 0.97 (CI: 0.96-0.99) to classify pneumonia vs. normal, and AUC of 0.91 (CI: 0.88-0.94) to classify bacterial vs. viral cases. All models that used pre-processed images showed higher AUC than baseline, which used the original CXR image. Image cropping and histogram equalization reduced irrelevant information from the exam, enhanced contrast, and was able to identify fine CXR texture details. The proposed ensemble model increased the representation of inflammatory patterns from bacteria and viruses with few epochs to train the deep CNNs.- 2D Image-Based Atrial Fibrillation Classification(2021) DIAS, Felipe M.; SAMESIMA, Nelson; RIBEIRO, Adele; MORENO, Ramon A.; PASTORE, Carlos A.; KRIEGER, Jose E.; GUTIERREZ, Marco A.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 wide use, especially with the rapid incorporation of telemedicine into the healthcare system. Here, we implement a machine learning method for AF classification using the region of interest (ROI) corresponding to the long DII lead automatically extracted from DICOM 12-lead ECG images. We observed 94.3%, 98.9%, 99.1%, and 92.2% for sensitivity, specificity, AUC, and F1 score, respectively. These results indicate that the proposed methodology performs similar to one-dimensional ECG signals as input, but does not require a dedicated software facilitating the integration into clinical practice, as ECGs are typically stored in PACS as 2D images.
- Blockchain-Based Architecture Design for Personal Health Record: Development and Usability Study(2022) COSTA, Thiago Bulhoes da Silva; SHINODA, Lucas; MORENO, Ramon Alfredo; KRIEGER, Jose E.; GUTIERREZ, MarcoBackground: 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 of the works focus more on describing how blockchain models can be used in a PHR scenario rather than whether these models are in fact feasible and robust enough to support a large number of users. Objective: To achieve a consistent, reproducible, and comparable PHR system, we build a novel ledger-oriented architecture out of a permissioned distributed network, providing patients with a manner to securely collect, store, share, and manage their health data. We also emphasize the importance of suitable ledgers and smart contracts to operate the blockchain network as well as discuss the necessity of standardizing evaluation metrics to compare related (net)works. Methods: We adopted the Hyperledger Fabric platform to implement our blockchain-based architecture design and the Hyperledger Caliper framework to provide a detailed assessment of our system: first, under workload, ranging from 100 to 2500 simultaneous record submissions, and second, increasing the network size from 3 to 13 peers. In both experiments, we used throughput and average latency as the primary metrics. We also created a health database, a cryptographic unit, and a server to complement the blockchain network. Results: With a 3-peer network, smart contracts that write on the ledger have throughputs, measured in transactions per second (tps) in an order of magnitude close to 102 tps, while those contracts that only read have rates close to 10(3) tps. Smart contracts that write also have latencies, measured in seconds, in an order of magnitude close to 10(1) seconds, while that only read have delays close to 10(0) seconds. In particular, smart contracts that retrieve, list, and view history have throughputs varying, respectively, from 1100 tps to 1300 tps, 650 tps to 750 tps, and 850 tps to 950 tps, impacting the overall system response if they are equally requested under the same workload. Varying the network size and applying an equal fixed load, in turn, writing throughputs go from 10(2) tps to 10(1) tps and latencies go from 101 seconds to 102 seconds, while reading ones maintain similar values. Conclusions: To the best of our knowledge, we are the first to evaluate, using Hyperledger Caliper, the performance of a PHR blockchain architecture and the first to evaluate each smart contract separately. Nevertheless, blockchain systems achieve performances far below what the traditional distributed databases achieve, indicating that the assessment of blockchain solutions for PHR is a major concern to be addressed before putting them into a real production.