DIEGO ARMANDO CARDONA CARDENAS
Índice h a partir de 2011
3
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
3 resultados
Resultados de Busca
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- 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.
- Chronic lung lesions in COVID-19 survivors: predictive clinical model(2022) CARVALHO, Carlos Roberto Ribeiro; CHATE, Rodrigo Caruso; SAWAMURA, Marcio Valente Yamada; GARCIA, Michelle Louvaes; LAMAS, Celina Almeida; CARDENAS, Diego Armando Cardona; LIMA, Daniel Mario; SCUDELLER, Paula Gobi; SALGE, Joao Marcos; NOMURA, Cesar Higa; GUTIERREZ, Marco AntonioObjective 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 using the modified Medical Research Council (mMRC) dyspnoea scale, oximetry (SpO(2)), spirometry (forced vital capacity (FVC)) and chest X-ray (CXR) during an in-person consultation. Patients with abnormalities in at least one of these parameters underwent chest CT. mMRC scale, SpO(2), FVC and CXR findings were used to build a machine learning model for lung lesion detection on CT. Setting A tertiary hospital in Sao Paulo, Brazil. Participants 749 eligible RT-PCR-confirmed SARS-CoV-2-infected patients aged >= 18 years. Primary outcome measure A predictive clinical model for lung lesion detection on chest CT. Results There were 470 patients (63%) that had at least one sign of pulmonary involvement and were eligible for CT. Almost half of them (48%) had significant pulmonary abnormalities, including ground-glass opacities, parenchymal bands, reticulation, traction bronchiectasis and architectural distortion. The machine learning model, including the results of 257 patients with complete data on mMRC, SpO(2), FVC, CXR and CT, accurately detected pulmonary lesions by the joint data of CXR, mMRC scale, SpO(2) and FVC (sensitivity, 0.85 +/- 0.08; specificity, 0.70 +/- 0.06; F1-score, 0.79 +/- 0.06 and area under the curve, 0.80 +/- 0.07). Conclusion A predictive clinical model based on CXR, mMRC, oximetry and spirometry data can accurately screen patients with lung lesions after SARS-CoV-2 infection. Given that these examinations are highly accessible and low cost, this protocol can be automated and implemented in different countries for early detection of COVID-19 sequelae.
- 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.