Use of fractals in determining the malignancy degree of lung nodules.

Introduction: A Computer-Assisted Detection (CAD) System for classification into malignant-benign classes using CT images is proposed. Methods: Two methods that use the fractal dimension (FD) as a measure of the lung nodule contour irregularities (Box counting and Power spectrum) were implemented. The LIDC-IDRI database was used for this study. Of these, 100 slices belonging to 100 patients were analyzed with both methods. Results: The performance between both methods was similar with an accuracy higher than 90%. Little overlap was obtained between FD ranges for the different malignancy grades with both methods, being slightly better in Power spectrum. Box counting had one more false positive than Power spectrum. Discussion: Both methods are able to establish a boundary between the high and low malignancy degree. To further validate these results and enhance the performance of the CAD system, additional studies will be necessary.

The Transdiagnostic Intervention for Sleep and Circadian Dysfunction (TranS-C) for serious mental illness in community mental health part 3: study protocol to evaluate sustainment in a hybrid type 2 effectiveness-implementation cluster-randomized trial.

BACKGROUND: Although research on the implementation of evidence-based psychological treatments (EBPTs) has advanced rapidly, research on the sustainment of implemented EBPTs remains limited. This is concerning, given that EBPT activities and benefits regularly decline post-implementation. To advance research on sustainment, the present protocol focuses on the third and final phase-the Sustainment Phase-of a hybrid type 2 cluster-randomized controlled trial investigating the implementation and sustainment of the Transdiagnostic Intervention for Sleep and Circadian Dysfunction (TranS-C) for patients with serious mental illness and sleep and circadian problems in community mental health centers (CMHCs). Prior to the first two phases of the trial-the Implementation Phase and Train-the-Trainer Phase-TranS-C was adapted to fit the CMHC context. Then, 10 CMHCs were cluster-randomized to implement Standard or Adapted TranS-C via facilitation and train-the-trainer. The primary goal of the Sustainment Phase is to investigate whether adapting TranS-C to fit the CMHC context predicts improved sustainment outcomes. METHODS: Data collection for the Sustainment Phase will commence at least three months after implementation efforts in partnering CMHCs have ended and may continue for up to one year. CMHC providers will be recruited to complete surveys (N = 154) and a semi-structured interview (N = 40) on sustainment outcomes and mechanisms. Aim 1 is to report the sustainment outcomes of TranS-C. Aim 2 is to evaluate whether manipulating EBPT fit to context (i.e., Standard versus Adapted TranS-C) predicts sustainment outcomes. Aim 3 is to test whether provider perceptions of fit mediate the relation between treatment condition (i.e., Standard versus Adapted TranS-C) and sustainment outcomes. Mixed methods will be used to analyze the data. DISCUSSION: The present study seeks to advance our understanding of sustainment predictors, mechanisms, and outcomes by investigating (a) whether the implementation strategy of adapting an EBPT (i.e., TranS-C) to the CMHC context predicts improved sustainment outcomes and (b) whether this relation is mediated by improved provider perceptions of treatment fit. Together, the findings may help inform more precise implementation efforts that contribute to lasting change. TRIAL REGISTRATION: ClinicalTrials.gov identifier: NCT05956678 . Registered on July 21, 2023.

The Transdiagnostic Intervention for Sleep and Circadian Dysfunction (TranS-C) for serious mental illness in community mental health part 3: Study protocol to evaluate sustainment in a hybrid type 2 effectiveness-implementation cluster-randomized trial.

treatments (EBPTs) has advanced rapidly, research on the sustainment of implemented EBPTs remains limited. This is concerning, given that EBPT activities and benefits regularly decline post-implementation. To advance research on sustainment, the present protocol focuses on the third and final phase - the Sustainment Phase - of a hybrid type 2 cluster-randomized controlled trial investigating the implementation and sustainment of the Transdiagnostic Intervention for Sleep and Circadian Dysfunction (TranS-C) for patients with serious mental illness and sleep and circadian problems in community mental health centers (CMHCs). Prior to the first two phases of the trial - the Implementation Phase and Train-the-Trainer Phase - TranS-C was adapted to fit the CMHC context. Then, 10 CMHCs were cluster-randomized to implement Standard or Adapted TranS-C via facilitation and train-the-trainer. The primary goal of the Sustainment Phase is to investigate whether adapting TranS-C to fit the CMHC context predicts improved sustainment outcomes. Methods: Data collection for the Sustainment Phase will commence at least three months after implementation efforts in partnering CMHCs have ended and may continue for up to one year. CMHC providers will be recruited to complete surveys (N = 154) and a semi-structured interview (N = 40) on sustainment outcomes and mechanisms. Aim 1 is to report the sustainment outcomes of TranS-C. Aim 2 is to evaluate whether manipulating EBPT fit to context (i.e., Standard versus Adapted TranS-C) predicts sustainment outcomes. Aim 3 is to test whether provider perceptions of fit mediate the relation between treatment condition (i.e., Standard versus Adapted TranS-C) and sustainment outcomes. Mixed methods will be used to analyze the data. Discussion: The present study seeks to advance our understanding of sustainment predictors, mechanisms, and outcomes by investigating (a) whether the implementation strategy of adapting an EBPT (i.e., TranS-C) to the CMHC context predicts improved sustainment outcomes and (b) whether this relation is mediated by improved provider perceptions of treatment fit. Together, the findings may help inform more precise implementation efforts that contribute to lasting change. Trial Registration: ClinicalTrials.gov identifier: NCT05956678. Registered on July 21, 2023. https://classic.clinicaltrials.gov/ct2/show/NCT05956678?term=NCT05956678&draw=2&rank=1.

The Transdiagnostic Intervention for Sleep and Circadian Dysfunction (TranS-C) for serious mental illness in community mental health part 2: study protocol for a hybrid type 2 effectiveness-implementation cluster-randomized trial using train-the-trainer.

BACKGROUND: Train-the-trainer (TTT) is a promising method for implementing evidence-based psychological treatments (EBPTs) in community mental health centers (CMHCs). In TTT, expert trainers train locally embedded individuals (i.e., Generation 1 providers) to deliver an EBPT, who then train others (i.e., Generation 2 providers). The present study will evaluate implementation and effectiveness outcomes of an EBPT for sleep and circadian dysfunction-the Transdiagnostic Intervention for Sleep and Circadian Dysfunction (TranS-C)-delivered to CMHC patients with serious mental illness by Generation 2 providers (i.e., trained and supervised within CMHCs via TTT). Specifically, we will investigate whether adapting TranS-C to fit CMHC contexts improves Generation 2 (a) patient outcomes and (b) providers' perceptions of fit. METHODS: TTT will be implemented in nine CMHCs in California, USA (N = 60 providers; N = 130 patients) via facilitation. CMHCs are cluster-randomized by county to Adapted TranS-C or Standard TranS-C. Within each CMHC, patients are randomized to immediate TranS-C or usual care followed by delayed treatment with TranS-C (UC-DT). Aim 1 will assess the effectiveness of TranS-C (combined Adapted and Standard), compared to UC-DT, on improvements in sleep and circadian problems, functional impairment, and psychiatric symptoms for Generation 2 patients. Aim 2 will evaluate whether Adapted TranS-C is superior to Standard TranS-C with respect to Generation 2 providers' perceptions of fit. Aim 3 will evaluate whether Generation 2 providers' perceived fit mediates the relation between TranS-C treatment condition and patient outcomes. Exploratory analyses will (1) evaluate whether the effectiveness of TranS-C for patient outcomes is moderated by generation, (2) compare Adapted and Standard TranS-C on patient perceptions of credibility/improvement and PhenX Toolkit outcomes (e.g., substance use, suicidality), and (3) evaluate other possible moderators. DISCUSSION: This trial has potential to (a) inform the process of embedding local trainers and supervisors to expand delivery of a promising transdiagnostic treatment for sleep and circadian dysfunction, (b) add to the growing body of TTT literature by evaluating TTT outcomes with a novel treatment and population, and (c) advance our understanding of providers' perceptions of EBPT "fit" across TTT generations. TRIAL REGISTRATION: ClinicalTrials.gov identifier NCT05805657 . Registered on April 10, 2023.

The Transdiagnostic Intervention for Sleep and Circadian Dysfunction (TranS-C) for serious mental illness in community mental health part 2: Study protocol for a hybrid type 2 effectiveness-implementation cluster- randomized trial using train-the-trainer.

Background: Train-the-trainer (TTT) is a promising method for implementing evidence-based psychological treatments (EBPTs) in community mental health centers (CMHCs). In TTT, expert trainers train locally embedded individuals (i.e., Generation 1 providers) to deliver an EBPT, who then train others (i.e., Generation 2 providers). The present study will evaluate implementation and effectiveness outcomes of an EBPT for sleep and circadian dysfunction-the Transdiagnostic Intervention for Sleep and Circadian Dysfunction (TranS-C)-delivered to CMHC patients with serious mental illness by Generation 2 providers (i.e., trained and supervised within CMHCs via TTT). Specifically, we will investigate whether adapting TranS-C to fit CMHC contexts improves Generation 2 (a) patient outcomes (b) providers' perceptions of fit. Methods: TTT will be implemented in nine CMHCs in California, United States (N= 60 providers; N= 130 patients) via facilitation. CMHCs are cluster-randomized by county to Adapted TranS-C or Standard TranS-C. Within each CMHC, patients are randomized to immediate TranS-C or usual care followed by delayed treatment with TranS-C (UC-DT). Aim 1 will assess the effectiveness of TranS-C (combined Adapted and Standard), compared to UC-DT, on improvements in sleep and circadian problems, functional impairment, and psychiatric symptoms for Generation 2 patients. Aim 2 will evaluate whether Adapted TranS-C is superior to Standard TranS-C with respect to Generation 2 providers' perceptions of fit. Aim 3 will evaluate whether Generation 2 providers' perceived fit mediates the relation between TranS-C treatment condition and patient outcomes. Exploratory analyses will: (1) evaluate whether the effectiveness of TranS-C for patient outcomes is moderated by generation, (2) compare Adapted and Standard TranS-C on patient perceptions of credibility/improvement and PhenX Toolkit outcomes (e.g., substance use, suicidality); and (3) evaluate other possible moderators. Discussion: This trial has potential to inform the process of (a) embedding local trainers and supervisors to expand delivery of a promising transdiagnostic treatment for sleep and circadian dysfunction, (b) adding to the growing body of TTT literature by evaluating TTT outcomes with a novel treatment and population, and (c) advancing our understanding of providers' perceptions of EBPT 'fit' across TTT generations. Trial registration: Clinicaltrials.gov identifier: NCT05805657. Registered on April 10, 2023. https://clinicaltrials.gov/ct2/show/NCT05805657.

The Transdiagnostic Intervention for Sleep and Circadian Dysfunction (TranS-C) for serious mental illness in community mental health part 1: study protocol for a hybrid type 2 effectiveness-implementation cluster-randomized trial.

BACKGROUND: Serious mental illness (SMI) can have devastating consequences. Unfortunately, many patients with SMI do not receive evidence-based psychological treatment (EBPTs) in routine practice settings. One barrier is poor "fit" between EBPTs and contexts in which they are implemented. The present study will evaluate implementation and effectiveness outcomes of the Transdiagnostic Intervention for Sleep and Circadian Dysfunction (TranS-C) implemented in community mental health centers (CMHCs). TranS-C was designed to target a range of SMI diagnoses by addressing a probable mechanism and predictor of SMI: sleep and circadian problems. We will investigate whether adapting TranS-C to fit CMHC contexts improves providers' perceptions of fit and patient outcomes. METHODS: TranS-C will be implemented in at least ten counties in California, USA (N = 96 providers; N = 576 clients), via facilitation. CMHC sites are cluster-randomized by county to Adapted TranS-C or Standard TranS-C. Within each county, patients are randomized to immediate TranS-C or usual care followed by delayed treatment with TranS-C (UC-DT). Aim 1 will compare TranS-C (combined Adapted and Standard) with UC-DT on improvements in sleep and circadian problems, functional impairment, and psychiatric symptoms. Sleep and circadian problems will also be tested as a mediator between treatment condition (combined TranS-C versus UC-DT) and functional impairment/psychiatric symptoms. Aim 2 will evaluate whether Adapted TranS-C is superior to Standard TranS-C with respect to provider perceptions of fit. Aim 3 will evaluate whether the relation between TranS-C treatment condition (Adapted versus Standard) and patient outcomes is mediated by better provider perceptions of fit in the Adapted condition. Exploratory analyses will (1) compare Adapted versus Standard TranS-C on patient perceptions of credibility/improvement and select PhenX Toolkit outcomes and (2) evaluate possible moderators. DISCUSSION: This trial has the potential to (a) expand support for TranS-C, a promising transdiagnostic treatment delivered to patients with SMI in CMHCs; (b) take steps toward addressing challenges faced by providers in delivering EBPTs (i.e., high caseloads, complex patients, poor fit); and (c) advance evidence on causal strategies (i.e., adapting treatments to fit context) in implementation science. TRIAL REGISTRATION: Clinicaltrials.gov NCT04154631. Registered on 6 November 2019. https://clinicaltrials.gov/ct2/show/NCT04154631.

New patch-based strategy for COVID-19 automatic identification using chest x-ray images.

Purpose: The development of a robust model for automatic identification of COVID-19 based on chest x-rays has been a widely addressed topic over the last couple of years; however, the scarcity of good quality images sets, and their limited size, have proven to be an important obstacle to obtain reliable models. In fact, models proposed so far have suffered from over-fitting erroneous features instead of learning lung features, a phenomenon known as shortcut learning. In this research, a new image classification methodology is proposed that attempts to mitigate this problem. Methods: To this end, annotation by expert radiologists of a set of images was performed. The lung region was then segmented and a new classification strategy based on a patch partitioning that improves the resolution of the convolution neural network is proposed. In addition, a set of native images, used as an external evaluation set, is released. Results: The best results were obtained for the 6-patch splitting variant with 0.887 accuracy, 0.85 recall and 0.848 F1score on the external validation set. Conclusion: The results show that the proposed new strategy maintains similar values between internal and external validation, which gives our model generalization power, making it available for use in hospital settings. Supplementary Information: The online version contains supplementary material available at 10.1007/s12553-022-00704-4.

Can integrating the Memory Support Intervention into cognitive therapy improve depression outcome? A randomized controlled trial.

We investigated if improving a patient's memory for the content of their treatment, via the Memory Support Intervention, improves illness course and functional outcomes. The platform for investigating this question was major depressive disorder (MDD) and cognitive therapy (CT). Adults diagnosed with MDD (N = 178) were randomly allocated to CT + Memory Support (n = 91) or CT-as-usual (n = 87). Both treatments were comprised of 20-26, 50-min sessions over 16 weeks. Blind assessments were conducted before and immediately following treatment (post-treatment) and 6 months later (6FU). Patient memory for treatment, assessed with a free recall task, was higher in CT + Memory Support for past session recall at post-treatment. Both treatment arms were associated with reductions in depressive symptoms and functional impairment except: CT + Memory Support exhibited lower depression severity at 6FU (b = -3.09, p = 0.050, d = -0.27), and greater reduction in unhealthy days from baseline to 6FU (b = -4.21, p = 0.010, d = -1.07), compared to CT-as-usual. While differences in illness course and functional outcomes between the two treatment arms were limited, it is possible that future analyses of the type of memory supports and longer follow-up may yield more encouraging outcomes. TRIAL REGISTRATION: ClinicalTrials.gov NCT01790919. Registered October 6, 2016.

Inteligencia artificial explicable, una perspectiva al problema de la clasificación automática de COVID-19 mediante radiografías de tórax

RESUMEN Esta investigación pretende dilucidar, a partir del análisis de técnicas de inteligencia artificial explicables, la robustez y el nivel de generalización de los métodos de visión por computadora propuestos para identificar COVID-19 utilizando imágenes de radiografías de tórax. Asimismo, alertar a los investigadores y revisores sobre el problema del aprendizaje por atajos. En este estudio se siguen recomendaciones para identificar si los modelos de clasificación automática de COVID-19 se ven afectados por el aprendizaje por atajos. Para ello, se revisaron los artículos que utilizan métodos de inteligencia artificial explicable en dicha tarea. Se evidenció que al utilizar la imagen de radiografía de tórax completa o el cuadro delimitador de los pulmones, las regiones de la imagen que más contribuyen a la clasificación aparecen fuera de la región pulmonar, algo que no tiene sentido. Los resultados indican que, hasta ahora, los modelos existentes presentan el problema de aprendizaje por atajos, lo cual los hace inapropiados para ser usados en entornos clínicos.

ABSTRACT This research aims to elucidate, from the analysis of explainable artificial intelligence techniques, the robustness and level of generalization of the proposed computer vision methods to identify COVID-19 using chest X-ray images. Also, alert researchers and reviewers about the problem of learning by shortcuts. In this study, recommendations are followed to identify if the automatic classification models of COVID-19 are affected by shortcut learning. To do this, articles that use explainable artificial intelligence methods were reviewed. It was shown that when using the full chest X-ray image or the bounding box of the lungs, the regions of the image that contribute the most to the classification appear outside the lung region, something that does not make sense. The results indicate that, so far, the existing models present the problem of learning by shortcuts, which makes them inappropriate to be used in clinical settings.

Segmentación del hígado en imágenes de tomografía computarizada

RESUMEN Fundamento: la segmentación del hígado utilizando datos de tomografía computarizada es el primer paso para el diagnóstico de enfermedades hepáticas. Actualmente la segmentación de estructuras y órganos, basado en imágenes, que se realiza en los hospitales del país, dista de tener los niveles de precisión que se obtienen de los modernos sistemas 3D, por lo que se requiere buscar alternativas viables utilizando el PDI sobre ordenador. Objetivo: determinar una variante eficaz y eficiente desde el punto de vista computacional en condiciones de rutina hospitalaria, para la segmentación de imágenes hepáticas con fines clínicos. Métodos: se compararon dos métodos modernos de segmentación (Graph Cut y EM/MPM) aplicándolos sobre imágenes de tomografía de hígado. Se realizó un análisis evaluativo y estadístico de los resultados obtenidos en la segmentación de las imágenes a partir de los coeficientes de Dice, Vinet y Jaccard. Resultados: con el método Graph Cut, en todos los casos, se segmentó la región deseada, incluso cuando la calidad de las imágenes era baja, se observó gran similitud entre la imagen segmentada y la máscara de referencia. El nivel de detalles visuales es bueno y la reproducción de bordes permanece fiel a la máscara de referencia. La segmentación de las imágenes por el método de EM/MPM, no siempre fue satisfactoria. Conclusiones: el método de segmentación Graph Cut obtuvo mayor precisión para segmentar imágenes de hígado.

ABSTRACT Background: liver segmentation using computed tomography data is the first step for the diagnosis of liver diseases. Currently, the segmentation of structures and organs, based on images, which is carried out in the country's hospitals, is far from having the levels of precision obtained from modern 3D systems, it is necessary to search for viable alternatives using the PDI on a computer. Objective: to determine an effective and efficient variant from the computational point of view in routine hospital conditions, for the segmentation of liver images for clinical purposes. Methods: Two modern segmentation methods (Graph Cut and EM/MPM) were compared by applying them to liver tomography images. An evaluative and statistical analysis of the results obtained in the segmentation of the images from the Dice, Vinet and Jaccard coefficients was carried out. Results: with the Graph Cut method, in all cases, the desired region was segmented, even when the quality of the images was low, great similarity was observed between the segmented image and the reference mask. The level of visual detail is good, and edge reproduction remains true to the reference skin. Image segmentation by the EM/MPM method was not always satisfactory. Conclusions: the Graph Cut segmentation method obtained greater precision to segment liver images.

Inteligencia artificial: una herramienta en la imagenología para los pacientes positivos a la COVID-19 / Artificial intelligence: an imaging tool for COVID-19 positive patients

RESUMEN Introducción: la enfermedad por SARS-Cov-2 refuerza la importancia del uso de las nuevas tecnologías de la información y las comunicaciones en función del desarrollo e implementación de sistemas de inteligencia artificial que favorecen el diagnóstico. Objetivo: describir la posibilidad del uso de la inteligencia artificial como una herramienta en la imagenología para los pacientes positivos a la COVID-19. Métodos: se realizó una revisión de fuentes bibliográficas en Infomed, SciELO, PubMed y Google Académico, comprendidas en los años 2015 al 2020 con el uso de palabras claves: coronavirus, COVID-19, neumonía, radiografía e inteligencia artificial. Se seleccionaron 28 documentos por su pertinencia en el estudio. Desarrollo: la creación de sistemas de inteligencia artificial que ayuden al diagnóstico médico requiere un enfoque interprofesional de la ciencia y constituye una de las líneas de trabajo en Cuba durante la pandemia. Una condición indispensable para la introducción de la inteligencia artificial en el diagnóstico radiológico es la capacitación que deben recibir los médicos para interactuar con ella, a través de un proceso formativo que incluya una evaluación y explicación de la calidad de los datos asociada tanto al aprendizaje como a las nuevas predicciones. Conclusiones: la utilización de inteligencia artificial mejorará el rendimiento del radiólogo para distinguir la COVID-19; la integración de estas tecnologías en el flujo de trabajo clínico de rutina puede ayudar a los radiólogos a diagnosticar con precisión.

ABSTRACT Introduction: SARS-Cov-2 disease reinforces the importance of the use of new information and communication technologies based on the development and implementation of artificial intelligence systems that favor diagnosis. Objective: to describe the possibility of using artificial intelligence as a tool in imaging for COVID-19 positive patients. Methods: a review of bibliographic sources was carried out in Infomed, SciELO, PubMed and Google Scholar, from 2015 to 2020 with the use of keywords: coronavirus, COVID-19, pneumonia, radiography and artificial intelligence. 28 documents were selected for their relevance in the study. Development: the creation of artificial intelligence systems that help medical diagnosis requires an interprofessional approach to science and constitutes one of the lines of work in Cuba during the pandemic. An essential condition for the introduction of artificial intelligence in radiological diagnosis is the training that doctors must receive to interact with it, through a training process that includes an evaluation and explanation of the quality of the data associated with both learning and to new predictions. Conclusions: the use of artificial intelligence will improve the radiologist's performance to distinguish COVID-19; integrating these technologies into routine clinical workflow can help radiologists diagnose accurately.

Current limitations to identify covid-19 using artificial intelligence with chest x-ray imaging (part ii). The shortcut learning problem.

Since the outbreak of the COVID-19 pandemic, computer vision researchers have been working on automatic identification of this disease using radiological images. The results achieved by automatic classification methods far exceed those of human specialists, with sensitivity as high as 100% being reported. However, prestigious radiology societies have stated that the use of this type of imaging alone is not recommended as a diagnostic method. According to some experts the patterns presented in these images are unspecific and subtle, overlapping with other viral pneumonias. This report seeks to evaluate the analysis the robustness and generalizability of different approaches using artificial intelligence, deep learning and computer vision to identify COVID-19 using chest X-rays images. We also seek to alert researchers and reviewers to the issue of "shortcut learning". Recommendations are presented to identify whether COVID-19 automatic classification models are being affected by shortcut learning. Firstly, papers using explainable artificial intelligence methods are reviewed. The results of applying external validation sets are evaluated to determine the generalizability of these methods. Finally, studies that apply traditional computer vision methods to perform the same task are considered. It is evident that using the whole chest X-Ray image or the bounding box of the lungs, the image regions that contribute most to the classification appear outside of the lung region, something that is not likely possible. In addition, although the investigations that evaluated their models on data sets external to the training set, the effectiveness of these models decreased significantly, it may provide a more realistic representation as how the model will perform in the clinic. The results indicate that, so far, the existing models often involve shortcut learning, which makes their use less appropriate in the clinical setting.

Caracterización clínica y radiológica de pacientes positivos a la COVID-19 / Clinical and radiological characterization of COVID-19 positive patients

Introducción: Desde el surgimiento de los primeros casos en la pandemia de la COVID-19, se ha desarrollado una carrera vertiginosa en crear un espacio de investigación para el diagnóstico, tratamiento y control de la enfermedad. Objetivo: Describir las características clínicas y radiológicas de los pacientes con la COVID-19. Métodos: Se realizó un estudio descriptivo, en el período comprendido de marzo a octubre del año 2020, se estudiaron 404 pacientes de todas las edades, ingresados, con diagnóstico confirmado con PCR en tiempo real. Las variables utilizadas fueron: edad, sexo, síntomas y radiografía del tórax. Resultados: El 54,5 por ciento de los pacientes fueron del sexo femenino y entre ellos asintomáticos el 55,9 por ciento; el 36,9 por ciento tenía entre 40 a 59 años de edad, en los menores de 20 años, el 64,9 por ciento no presentó síntomas de la enfermedad al ingreso. Estuvieron asintomáticos el 53,5 por ciento; el 76,6 por ciento de las radiografías positivas correspondieron a los sintomáticos, la tos fue el síntoma más frecuente. La mayor positividad en la radiografía del tórax se encontró en los pacientes mayores de 60 años, se observó como patrón más frecuente, la opacidad en velo, de distribución periférica. Conclusiones: Predominan los pacientes asintomáticos, la positividad de las radiografías es mayor en los ancianos(AU)

Introduction: Since the emergence of the first cases of COVID-19 pandemic, a dizzying race has developed in creating a research space for the diagnosis, treatment and control of the disease. Objective: To describe the clinical and radiological characteristics of patients with COVID-19. Methods: A descriptive study was carried out, in the period from March to October 2020, 404 patients of all ages, admitted, with confirmed diagnosis with real-time PCR, were studied. The variables used were: age, sex, symptoms and chest X-ray. Results: 54.5 percent of the patients were female and 55,9 percent of them were asymptomatic, 36,9 percent were between 40 and 59 years old, in those under 20 years 64,9 percent were not. They presented symptoms of the disease upon admission 53,5 percent were asymptomatic, 76,6 percent of the positive radiographs corresponded to the symptomatic ones, coughing was the most frequent symptom. The greatest positivity in the chest X-ray was found in patients older than 60 years, the most frequent pattern was the opacity in the peripheral distribution veil. Conclusions: Asymptomatic patients predominate, the positivity of radiographs is higher in the elderly(AU)

Inpainting-filtering for metal artifact reduction (IMIF-MAR) in computed tomography.

The reduction of metal artifacts remains a challenge in computed tomography because they decrease image quality, and consequently might affect the medical diagnosis. The objective of this study is to present a novel method to correct metal artifacts based solely on the CT-slices. The proposed method consists of four steps. First, metal implants in the original CT-slice are segmented using an entropy based method, producing a metal image. Second, a prior image is acquired using three transformations: Gaussian filter, Parisotto and Schoenlieb inpainting method with the Mumford-Shah image model and L0 Gradient Minimization method (L0GM). Next, based on the projections from the original CT-slice, prior image and metal image, the sinogram is corrected in the traces affected by metal in the process called normalization and denormalization. Finally, the reconstructed image is obtained by FBP and a Nonlocal Means (NLM) filtering. The efficacy of the algorithm is evaluated by comparing five image quality metrics of the images and by inspecting regions of interest (ROI). Phantom data as well as clinical datasets are included. The proposed method is compared with three established metal artifact reduction (MAR) methods. The results from a phantom and clinical dataset show the visible reduction of artifacts. The conclusion is that IMIF-MAR method can reduce streak metal artifacts effectively and avoid new artifacts around metal implants, while preserving the anatomical structures. Considering both clinical and phantom studies, the proposed MAR algorithm improves the quality of clinical images affected by metal artifacts, and could be integrated in clinical setting.

Current limitations to identify COVID-19 using artificial intelligence with chest X-ray imaging.

The scientific community has joined forces to mitigate the scope of the current COVID-19 pandemic. The early identification of the disease, as well as the evaluation of its evolution is a primary task for the timely application of medical protocols. The use of medical images of the chest provides valuable information to specialists. Specifically, chest X-ray images have been the focus of many investigations that apply artificial intelligence techniques for the automatic classification of this disease. The results achieved to date on the subject are promising. However, some results of these investigations contain errors that must be corrected to obtain appropriate models for clinical use. This research discusses some of the problems found in the current scientific literature on the application of artificial intelligence techniques in the automatic classification of COVID-19. It is evident that in most of the reviewed works an incorrect evaluation protocol is applied, which leads to overestimating the results.

Methods for Reducing Metal Artifacts in Computerized Tomography / Métodos para reducir los artefactos metálicos en la tomografía computarizada

Abstract Metal artifacts are common in clinical images. Many methods for artifact reduction have been published to overcome this problem. In this work, animage smoothing method for artifact reduction (ISMAR) is proposed for image quality improvement in patients with hip prosthesis and dental fillings, which caused metal artifacts. ISMAR was evaluated and compared with three well-known methods for metal artifact reduction (linear interpolation (LI), normalized metal artifact reduction (NMAR) and frequency split metal artifact reduction (FSMAR)). The new method is based on edge-preserving smoothing via L0 Gradient Minimization filter. Image quality was evaluated by two experienced radiologists completely blinded to the information about if the image was processed or not to suppress the artifacts. They graded image quality in a five points-scale, where zero is an index of clear artifact presence, and five, a whole artifact suppression. The new method had the best results and it was statistically significant respect to the other tested methods (p < 0.05). This new method has a better performance in artifact suppression and tissue feature preservation.

Resumen Los artefactos metálicos son comunes en las imágenes clínicas. Muchos métodos para la reducción de los artefactos han sido publicados para superar este problema. En el presente trabajo, un método de suavizado de imágenes para la reducción de artefactos (ISMAR) es propuesto para mejorar la calidad de la imagen en pacientes con prótesis de cadera y empastes dentales, los cuales causaron artefactos metálicos. ISMAR fue evaluado y comparado con otros tres métodos reconocidos por su desempeño en la reducción de los artefactos metálicos (Interpolación lineal (LI), reducción de artefactos de metal normalizados (NMAR) y reducción de artefactos de metal divididos en frecuencia (FSMAR)). El nuevo método se basa en el suavizado y conservación de bordes, utilizando para ello el filtro de minimización de gradiente L0. La calidad de la imagen fue evaluada por dos radiólogos experimentados completamente ciegos a la información sobre si la imagen fue procesada o no para suprimir los artefactos. Ellos calificaron la calidad de la imagen en una escala de cinco puntos, donde el cero indica la presencia de artefactos, y el cinco, una supresión total de los artefactos. El nuevo método tuvo los mejores resultados y fue estadísticamente significativo con respecto a los otros métodos probados (p < 0.05). Este nuevo método tiene un mejor rendimiento en la supresión de artefactos y en la conservación de las características de los tejidos.

The association of dosimetric quantities from computed tomography with operational factors: basis for optimization strategies / Asociación de magnitudes dosimétricas de tomografía computarizada con factores operacionales: bases para estrategias de optimización

Abstract Clinical Computed Tomography (CT) imaging is supported by a patient - technology - observers system. Such system involves dosimetric quantities associated with image quality descriptors, where operational factors are predictors. Knowledge of quantitative association between CT dosimetric and image quality quantities with systemic factors, provides the basis to devise scanner-specific optimization strategies. Kerma indexes were measured with a pencil ionization chamber free in air C a,100 and in phantom C pmma,x (x changes into c and p for center and periphery respectively). Polymethyl Methacrylate (PMMA) standard phantoms were used (diameters of 16 and 32 cm). Several operational factors of a Siemens Sensation 64 Cardiac were considered: estimated spectrums, tube potential F 8 (80 - 140 kV), tube current x time product F 1 (40 - 350 mAs) and total collimation at isocenter F 3 (2,7 - 19,2 mm). The water equivalent radius R w , an important factor for patient Size Specific Dose Estimators (SSDE), was estimated by taking into account the spectrums in each phantom. Average pixel noise was measured from Regions of Interest (ROIs) in water phantoms with radius of 2,5; 3; 6; 8 and 11,5 cm. A linear association was found between C pmma,p and C pmma,c . A dose reduction of C pmma,c = 2 mGy per tube rotation can be obtained from data analysis (head mode), with F 1 = 50 mAs, F 3 = 19,2 mm, resulting in average pixel noise of 20 Hounsfield Units (HU). Knowledge of noise association with C pmma,c provides a straightforward tool for quantitative optimization, considering a systemic approach, which includes patient - technology - observer factors.

Resumen La tomografía computarizada (TC) clínica se basa en un sistema paciente - tecnología - observador. Dicho sistema incluye magnitudes dosimétricas asociadas a descriptores de calidad, donde los factores operacionales son predictores. Conocer la asociación cuantitativa entre magnitudes dosimétricas y de calidad de imagen con factores sistémicos, provee la base para concebir estrategias de optimización específicas por tomógrafo. Se midieron índices de kerma en aire C a,100 y en maniquí C pmma,x (x cambia a c y p para centro y periferia respectivamente) con una cámara de ionización tipo lápiz. Se utilizaron maniquíes de Polimetil Metacrilato (PMMA) con diámetros de 16 y 32 cm. Se consideraron factores operacionales de un equipo Siemens Sensation 64 Cardiac: espectros estimados, tensión del tubo F 8 (80 - 140 kV), producto corriente x tiempo de exposición F 1 (40 - 350 mAs) y colimación total en isocentro F 3 (2,7 - 19,2 mm). El radio agua-equivalente R w es un factor importante para Estimadores de Dosis Específicos del paciente (SSDE), se estimó teniendo en cuenta el espectro en cada maniquí. El ruido promedio de píxel se midió en regiones de interés (ROIs) de imágenes de maniquíes de agua con radios de 2,5; 3; 6; 8 y 11,5 cm. Se encontró una asociación lineal entre C pmma,p y C pmma,c . Se describe una reducción de dosis a C pmma,c = 2 mGy por rotación del tubo mediante el análisis de datos (modo cabeza), con F 1 = 50 mAs, F 3 = 19,2 mm, resultando en un ruido promedio de píxel de 20 Unidades Hounsfield (UH).

Control externo indirecto de la calidad de las láminas de baciloscopia del programa de tuberculosis, Matanzas, 1997-2009 / Indirect external control of the quality of the sputum smear test slides of the tuberculosis program, Matanzas, 1997-2009

El control de calidad de la baciloscopía, es un sistema diseñado para mejorar la habilidad, eficiencia y el uso de la microscopía, como opción de diagnóstico y monitoreo, asegurando que la información generada por el mismo, sea exacta, fiable y reproducible. El objetivo del presente estudio fue evaluar los indicadores de calidad de la baciloscopía, según lo establecido en el programa de control de la tuberculosis en Cuba en los laboratorios de diagnóstico de tuberculosis de los centros municipales de higiene y epidemiología, de Matanzas. Se tomaron el 100 por ciento de las láminas de los casos de tuberculosis diagnosticados y el 10 por ciento de las láminas negativas de todos los centros de salud de la provincia donde se realiza baciloscopia de esputo. Se realizó el control de calidad a 27 481 láminas en el período de enero de 1997 hasta diciembre de 2009, según lo establecido en el Manual de procedimientos, del Programa Nacional de Control de Tuberculosis. De las láminas evaluadas, presentaron codificaciones concordantes 27 444 (99,9 por ciento); codificaciones discrepantes, 3 láminas (0,01 por ciento) y discordantes, 34 (0,12 por ciento). La tasa de error para todos falsos positivos fue de 0,12 por ciento; no se identificaron resultados falsos negativos. Estos resultados sugieren la calidad del personal que realiza la baciloscopía de tuberculosis en los laboratorios y recomendamos no descuidar las continuas supervisiones y mantener un programa de entrenamiento constante de los técnicos para continuar mejorando la calidad del diagnóstico baciloscópico en la provincia de Matanzas(AU)

The control of the sputum smear test quality is a system designed to improve the ability, efficiency and usage of the sputum smear test, as a diagnosis option and a monitoring, guarantying that the generated information is exact, reliable and reproducible. The objective of the current study was assessing the sputum smear test quality indicators in the laboratories of tuberculosis diagnosis at the municipal centers of hygiene and epidemiologic, of Matanzas, according to the parameters established in the program for controlling tuberculosis in Cuba. We took 100 per cent of the slides of the cases diagnosed with tuberculosis and 10 per cent of the negative slides in all the health institutions of the province where sputum smear tests are made. We carried out the quality control of 27 481 slides in the period from January 1997 to December 2009, according to the Handbook of procedures, of the National Program of Tuberculosis Control. Of the assessed slides, 27 444 (99,9 per cent) had concordant codifications, 3 slides (0.01) discrepant and 34 (0,12) discordant ones. The fault rate for all the false negative was 0,12 per cent; no false negative results were identified. These results suggest the quality of the staff making the tuberculosis sputum smear tests in the laboratories and we recommend do not neglect the continual supervision and maintaining a program of technicians constant training to continue improving the quality of the sputum smear test diagnosis in the province of Matanzas(AU)