JOSE EDUARDO KRIEGER
Projetos de Pesquisa
Unidades Organizacionais
Departamento de Cardio-Pneumologia, Faculdade de Medicina - Docente
Instituto do Coração, Hospital das Clínicas, Faculdade de Medicina
LIM/13 - Laboratório de Genética e Cardiologia Molecular, Hospital das Clínicas, Faculdade de Medicina - Líder
Instituto do Coração, Hospital das Clínicas, Faculdade de Medicina
LIM/13 - Laboratório de Genética e Cardiologia Molecular, Hospital das Clínicas, Faculdade de Medicina - Líder
8 resultados
Resultados de Busca
Agora exibindo 1 - 8 de 8
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
- CardioBERTpt: Transformer-based Models for Cardiology Language Representation in Portuguese(2023) SCHNEIDER, Elisa Terumi Rubel; GUMIEL, Yohan Bonescki; SOUZA, Joao Vitor Andrioli de; MUKAI, Lilian Mie; OLIVEIRA, Lucas Emanuel Silva e; REBELO, Marina de Sa; GUTIERREZ, Marco Antonio; KRIEGER, Jose Eduardo; TEODORO, Douglas; MORO, Claudia; PARAISO, Emerson CabreraContextual word embeddings and the Transformers architecture have reached state-of-the-art results in many natural language processing (NLP) tasks and improved the adaptation of models for multiple domains. Despite the improvement in the reuse and construction of models, few resources are still developed for the Portuguese language, especially in the health domain. Furthermore, the clinical models available for the language are not representative enough for all medical specialties. This work explores deep contextual embedding models for the Portuguese language to support clinical NLP tasks. We transferred learned information from electronic health records of a Brazilian tertiary hospital specialized in cardiology diseases and pre-trained multiple clinical BERT-based models. We evaluated the performance of these models in named entity recognition experiments, fine-tuning them in two annotated corpora containing clinical narratives. Our pre-trained models outperformed previous multilingual and Portuguese BERT-based models for cardiology and multi-specialty environments, reaching the state-of-the-art for analyzed corpora, with 5.5% F1 score improvement in TempClinBr (all entities) and 1.7% in SemClinBr (Disorder entity) corpora. Hence, we demonstrate that data representativeness and a high volume of training data can improve the results for clinical tasks, aligned with results for other languages.
- 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
- 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.
- A deep learning approach for COVID-19 screening and localization on Chest X-Ray images(2022) MARCOMINI, Karem Daiane; CARDENAS, Diego Armando Cardona; TRAINA, Agma Juci Machado; KRIEGER, Jose Eduardo; GUTIERREZ, Marco AntonioChest X-ray (CXR) images have a high potential in the monitoring and examination of various lung diseases, including COVID-19. However, the screening of a large number of patients with diagnostic hypothesis for COVID-19 poses a major challenge for physicians. In this paper, we propose a deep learning-based approach that can simultaneously suggest a diagnose and localize lung opacity areas in CXR images. We used a public dataset containing 5, 639 posteroanterior CXR images. Due to unbalanced classes (69.2% of the images are COVID-19 positive), data augmentation was applied only to images belonging to the normal category. We split the dataset into train and test sets with proportional rate at 90:10. To the classification task, we applied 5-fold cross-validation to the training set. The EfficientNetB4 architecture was used to perform this classification. We used a YOLOv5 pre-trained in COCO dataset to the detection task. Evaluations were based on accuracy and area under the ROC curve (AUROC) metrics to the classification task and mean average precision (mAP) to the detection task. The classification task achieved an average accuracy of 0.83 +/- 0.01 (95% CI [0.81, 0.84]) and AUC of 0.88 +/- 0.02 (95% CI [0.85, 0.89]) in 5-fold over the test dataset. The best result was reached in fold 3 (0.84 and 0.89 of accuracy and AUC, respectively). Positive results were evaluated by the opacity detector, which achieved a mAP of 59.51%. Thus, the good performance and rapid diagnostic prediction make the system a promising means to assist radiologists in decision making tasks.
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.