A Lightweight, Rapid and Efficient Deep Convolutional Network for Chest X-Ray Tuberculosis Detection

Tuberculosis (TB) is still recognized as one of the leading causes of death worldwide. Recent advances in deep learning (DL) have shown to enhance radiologists' ability to interpret chest X-ray (CXR) images accurately and with fewer errors, leading to a better diagnosis of this disease. However, little work has been done to develop models capable of diagnosing TB that offer good performance while being efficient, fast and computationally inexpensive. In this work, we propose LightTBNet, a novel lightweight, fast and efficient deep convolutional network specially customized to detect TB from CXR images. Using a total of 800 frontal CXR images from two publicly available datasets, our solution yielded an accuracy, F1 and area under the ROC curve (AUC) of 0.906, 0.907 and 0.961, respectively, on an independent test subset. The proposed model demonstrates outstanding performance while delivering a rapid prediction, with minimal computational and memory requirements, making it highly suitable for deployment in handheld devices that can be used in low-resource areas with high TB prevalence. Code publicly available at https://github.com/dani-capellan/LightTBNet.Comment: 5 pages, 3 figures, 3 tables. This paper has been accepted at ISBI 202

Intrapericardial cardiosphere-derived cells hinder epicardial dense scar expansion and promote electrical homogeneity in a porcine post-infarction model

The arrhythmic substrate of ventricular tachycardias in many structural heart diseases is located in the epicardium, often resulting in poor outcomes with currently available therapies. Cardiosphere-derived cells (CDCs) have been shown to modify myocardial scarring. A total of 19 Large White pigs were infarcted by occlusion of the mid-left anterior descending coronary artery for 150 min. Baseline cardiac magnetic resonance (CMR) imaging with late gadolinium enhancement sequences was obtained 4 weeks post-infarction and pigs were randomized to a treatment group (intrapericardial administration of 300,000 allogeneic CDCs/kg), (n = 10) and to a control group (n = 9). A second CMR and high-density endocardial electroanatomical mapping were performed at 16 weeks post-infarction. After the electrophysiological study, pigs were sacrificed and epicardial optical mapping and histological studies of the heterogeneous tissue of the endocardial and epicardial scars were performed. In comparison with control conditions, intrapericardial CDCs reduced the growth of epicardial dense scar and epicardial electrical heterogeneity. The relative differences in conduction velocity and action potential duration between healthy myocardium and heterogeneous tissue were significantly smaller in the CDC-treated group than in the control group. The lower electrical heterogeneity coincides with heterogeneous tissue with less fibrosis, better cardiomyocyte viability, and a greater quantity and better polarity of connexin 43. At the endocardial level, no differences were detected between groups. Intrapericardial CDCs produce anatomical and functional changes in the epicardial arrhythmic substrate, which could have an anti-arrhythmic effect.This study was supported by the Instituto de Salud Carlos III, Madrid, Spain (PI18/01895 and DTS21/00064); Red de Terapia Celular from the Instituto de Salud Carlos III, Madrid, Spain (RD16/0011/0029); Ricors-Red de Investigación Cooperativa Orientada a Resultados en Salud-RICORS TERAV (RD21.0017.0002), European Union's H2020 Program under grant agreement No. 874827 (BRAVE), and the Sociedad Española de Cardiología, Madrid, Spain

QuantiDOPA: A Quantification Software for Dopaminergic Neurotransmission SPECT

Quantification of neurotransmission Single-Photon Emission Computed Tomography (SPECT) studies of the dopaminergic system can be used to track, stage and facilitate early diagnosis of the disease. The aim of this study was to implement QuantiDOPA, a semi-automatic quantification software of application in clinical routine to reconstruct and quantify neurotransmission SPECT studies using radioligands which bind the dopamine transporter (DAT). To this end, a workflow oriented framework for the biomedical imaging (GIMIAS) was employed. QuantiDOPA allows the user to perform a semiautomatic quantification of striatal uptake by following three stages: reconstruction, normalization and quantification. QuantiDOPA is a useful tool for semi-automatic quantification inDAT SPECT imaging and it has revealed simple and flexibl

Implementation and performance of automated software to compute the RV/LV diameter ratio from CT pulmonary angiography images

Objective: The aim of this study was to prospectively test the performance and potential for clinical integration of software that automatically calculates the right-to-left ventricular (RV/LV) diameter ratio from computed tomography pulmonary angiography images. Methods: Using 115 computed tomography pulmonary angiography images that were positive for acute pulmonary embolism, we prospectively evaluated RV/LV ratio measurements that were obtained as follows: (1) completely manual measurement (reference standard), (2) completely automated measurement using the software, and (3 and 4) using a customized software interface that allowed 2 independent radiologists to manually adjust the automatically positioned calipers. Results: Automated measurements underestimated (P < 0.001) the reference standard (1.09 [0.25] vs1.03 [0.35]). With manual correction of the automatically positioned calipers, the mean ratio became closer to the reference standard (1.06 [0.29] by read 1 and 1.07 [0.30] by read 2), and the correlation improved (r = 0.675 to 0.872 and 0.887). The mean time required for manual adjustment (37 [20] seconds) was significantly less than the time required to perform measurements entirely manually (100 [23] seconds). Conclusions: Automated CT RV/LV diameter ratio software shows promise for integration into the clinical workflow for patients with acute pulmonary embolism

Assessment of diastolic chamber properties of the right ventricle by global fitting of pressure-volume data and conformational analysis of 3D + T echocardiographic sequences

Assessment of diastolic chamber properties of the right ventricle by global fitting of pressure-volume data and conformational analysis of 3D + T echocardiographic sequence

Real-time incidence of travel-related symptoms through a smartphone-based app remote monitoring system: a pilot study

Trip Doctor(R), a Smartphone-based app monitoring system, was developed to detect infections among travelers in real-time. For testing, 106 participants were recruited (62.2% male, mean age 36 years (SD = 11)). Majority of trips were for tourism and main destinations were in South East Asia. Mean travel duration was 14 days (SD = 10). Diarrhea was the most frequently reported symptom (15.5%). The system demonstrated adequate usability and is ready to be used on a larger scale

Enhancing physicians’ radiology diagnostics of COVID-19’s effects on lung health by leveraging artificial intelligence

Introduction: This study aimed to develop an individualized artificial intelligence model to help radiologists assess the severity of COVID-19's effects on patients' lung health.Methods: Data was collected from medical records of 1103 patients diagnosed with COVID-19 using RT- qPCR between March and June 2020, in Hospital Madrid-Group (HM-Group, Spain). By using Convolutional Neural Networks, we determine the effects of COVID-19 in terms of lung area, opacities, and pulmonary air density. We then combine these variables with age and sex in a regression model to assess the severity of these conditions with respect to fatality risk (death or ICU).Results: Our model can predict high effect with an AUC of 0.736. Finally, we compare the performance of the model with respect to six physicians' diagnosis, and test for improvements on physicians' performance when using the prediction algorithm.Discussion: We find that the algorithm outperforms physicians (39.5% less error), and thus, physicians can significantly benefit from the information provided by the algorithm by reducing error by almost 30%

FocusDET: Herramienta multimodal para la localización del foco epileptógeno en la epilepsia farmacorresistente

Los pacientes epilépticos con crisis parciales complejas resistentes a tratamiento farmacológico son candidatos a la escisión de la región focal del cerebro que induce dichas crisis. La correcta localización del foco epileptógeno es esencial para considerar la cirugía como posible tratamiento. El objetivo de este trabajo es el desarrollo de una aplicación médica para la localización del foco epileptógeno a partir de datos multimodales. Para el desarrollo de esta nueva herramienta se utiliza GIMIAS, una plataforma de software para la implementación y prototipado de aplicaciones médicas. La nueva herramienta desarrollada, FocusDET, permite llevar a cabo la técnica SISCOM y el análisis de datos EEG-RM f ictal, de imágenes PET y de distintas modalidades de imagen de RM. FocusDET, gracias a su interfaz amigable y a su rapidez de procesamiento, puede ser adecuada para la rutina clínica

Deep learning-based lesion subtyping and prediction of clinical outcomes in COVID-19 pneumonia using chest CT

The main objective of this work is to develop and evaluate an artificial intelligence system based on deep learning capable of automatically identifying, quantifying, and characterizing COVID-19 pneumonia patterns in order to assess disease severity and predict clinical outcomes, and to compare the prediction performance with respect to human reader severity assessment and whole lung radiomics. We propose a deep learning based scheme to automatically segment the different lesion subtypes in nonenhanced CT scans. The automatic lesion quantification was used to predict clinical outcomes. The proposed technique has been independently tested in a multicentric cohort of 103 patients, retrospectively collected between March and July of 2020. Segmentation of lesion subtypes was evaluated using both overlapping (Dice) and distance-based (Hausdorff and average surface) metrics, while the proposed system to predict clinically relevant outcomes was assessed using the area under the curve (AUC). Additionally, other metrics including sensitivity, specificity, positive predictive value and negative predictive value were estimated. 95% confidence intervals were properly calculated. The agreement between the automatic estimate of parenchymal damage (%) and the radiologists' severity scoring was strong, with a Spearman correlation coefficient (R) of 0.83. The automatic quantification of lesion subtypes was able to predict patient mortality, admission to the Intensive Care Units (ICU) and need for mechanical ventilation with an AUC of 0.87, 0.73 and 0.68 respectively. The proposed artificial intelligence system enabled a better prediction of those clinically relevant outcomes when compared to the radiologists' interpretation and to whole lung radiomics. In conclusion, deep learning lesion subtyping in COVID-19 pneumonia from noncontrast chest CT enables quantitative assessment of disease severity and better prediction of clinical outcomes with respect to whole lung radiomics or radiologists' severity score

Enhancing physicians’ radiology diagnostics of COVID-19’s effects on lung health by leveraging artificial intelligence

Introduction: This study aimed to develop an individualized artificial intelligence model to help radiologists assess the severity of COVID-19’s effects on patients’ lung health.Methods: Data was collected from medical records of 1103 patients diagnosed with COVID-19 using RT- qPCR between March and June 2020, in Hospital Madrid-Group (HM-Group, Spain). By using Convolutional Neural Networks, we determine the effects of COVID-19 in terms of lung area, opacities, and pulmonary air density. We then combine these variables with age and sex in a regression model to assess the severity of these conditions with respect to fatality risk (death or ICU).Results: Our model can predict high effect with an AUC of 0.736. Finally, we compare the performance of the model with respect to six physicians’ diagnosis, and test for improvements on physicians’ performance when using the prediction algorithm.Discussion: We find that the algorithm outperforms physicians (39.5% less error), and thus, physicians can significantly benefit from the information provided by the algorithm by reducing error by almost 30%