Prediction of multidrug-resistant TB from CT pulmonary images based on deep learning techniques

While tuberculosis (TB) disease was discovered more than a century ago, it has not been eradicated yet. Quite contrary, at present, TB constitutes one of top 10 causes of death and has shown signs of increasing. To complement conventional diagnostic procedure of applying microbiological culture that takes several weeks and remains expensive, high resolution computer tomography (CT) of pulmonary images has been resorted to not only for aiding clinicians to expedite the process of diagnosis but also for monitoring prognosis when administrating antibiotic drugs. This research undertakes the investigation of predicting multi-drug resistant (MDR) patients from drug sensitive (DS) ones based on CT lung images to monitor the effectiveness of treatment. To contend with smaller datasets (i.e. in hundreds) and the characteristics of CT TB images with limited regions capturing abnormities, patch-based deep convolutional neural network (CNN) allied to support vector machine (SVM) classifier is implemented on a collection of datasets from 230 patients obtained from ImageCLEF 2017 competition. As a result, the proposed architecture of CNN+SVM+patch performs the best with classification accuracy rate at 91.11% (79.80% in terms of patches). In addition, hand-crafted SIFT based approach accomplishes 88.88% in terms of subject and 83.56% with reference to patches, the highest in this study, which can be explained away by the fact that the datasets are in small numbers. Significantly, during the Tuberculosis Competition at ImageCLEF 2017, the authors took part in the task of classification of 5 types of TB disease and achieved top one with regard to averaged classification accuracy (i.e. ACC = 0.4067), which is also premised on the approach of CNN+SVM+patch. On the other hand, when the whole slices of 3D TB datasets are applied to train a CNN network, the best result is achieved through the application of CNN coupled with orderless pooling and SVM at 64.71% accuracy rate

Analysis of tuberculosis severity levels from CT pulmonary images based on enhanced residual deep learning architecture

This research investigates the application of CT pulmonary images to the detection and characterisation of TB at five levels of severity, in order to monitor the efficacy of treatment. To contend with smaller datasets (i.e. in hundreds) and the characteristics of CT TB images in which abnormalities occupy only limited regions, a 3D block-based residual deep learning network (ResNet) coupled with injection of depth information (depth-Resnet) at each layer was implemented. Progress in evaluation has been accomplished in two ways. One is to assess the proposed depth-Resnet in prediction of severity scores and another is to analyse the probability of high severity of TB. For the former, delivered results are of 92.70 ± 5.97% and 67.15 ± 1.69% for proposed depth-Resnet and ResNet-50 respectively. For the latter, two additional measures are put forward, which are calculated using (1) the overall severity (1 to 5) probability, and (2) separate probabilities of both high severity (scores of 1 to 3) and low severity (scores of 4 and 5) respectively, when scores of 1 to 5 are mapped into initial probabilities of (0.9, 0.7, 0.5, 0.3, 0.2) respectively. As a result, these measures achieve the averaged accuracies of 75.88% and 85.29% for both methods respectively

Prediction of multidrug-resistant TB from CT pulmonary images based on deep learning techniques

While tuberculosis (TB) disease was discovered more than a century ago, it has not been eradicated yet. Quite contrary, at present, TB constitutes one of top 10 causes of death and has shown signs of increasing. To complement conventional diagnostic procedure of applying microbiological culture that takes several weeks and remains expensive, high resolution computer tomography (CT) of pulmonary images has been resorted to not only for aiding clinicians to expedite the process of diagnosis but also for monitoring prognosis when administrating antibiotic drugs. This research undertakes the investigation of predicting multi-drug resistant (MDR) patients from drug sensitive (DS) ones based on CT lung images to monitor the effectiveness of treatment. To contend with smaller datasets (i.e. in hundreds) and the characteristics of CT TB images with limited regions capturing abnormities, patch-based deep convolutional neural network (CNN) allied to support vector machine (SVM) classifier is implemented on a collection of datasets from 230 patients obtained from ImageCLEF 2017 competition. As a result, the proposed architecture of CNN+SVM+patch performs the best with classification accuracy rate at 91.11% (79.80% in terms of patches). In addition, hand-crafted SIFT based approach accomplishes 88.88% in terms of subject and 83.56% with reference to patches, the highest in this study, which can be explained away by the fact that the datasets are in small numbers. Significantly, during the Tuberculosis Competition at ImageCLEF 2017, the authors took part in the task of classification of 5 types of TB disease and achieved top one with regard to averaged classification accuracy (i.e. ACC = 0.4067), which is also premised on the approach of CNN+SVM+patch. On the other hand, when the whole slices of 3D TB datasets are applied to train a CNN network, the best result is achieved through the application of CNN coupled with orderless pooling and SVM at 64.71% accuracy rate

Artificial Intelligence in Image-Based Screening, Diagnostics, and Clinical Care of Cardiopulmonary Diseases

Cardiothoracic and pulmonary diseases are a significant cause of mortality and morbidity worldwide. The COVID-19 pandemic has highlighted the lack of access to clinical care, the overburdened medical system, and the potential of artificial intelligence (AI) in improving medicine. There are a variety of diseases affecting the cardiopulmonary system including lung cancers, heart disease, tuberculosis (TB), etc., in addition to COVID-19-related diseases. Screening, diagnosis, and management of cardiopulmonary diseases has become difficult owing to the limited availability of diagnostic tools and experts, particularly in resource-limited regions. Early screening, accurate diagnosis and staging of these diseases could play a crucial role in treatment and care, and potentially aid in reducing mortality. Radiographic imaging methods such as computed tomography (CT), chest X-rays (CXRs), and echo ultrasound (US) are widely used in screening and diagnosis. Research on using image-based AI and machine learning (ML) methods can help in rapid assessment, serve as surrogates for expert assessment, and reduce variability in human performance. In this Special Issue, “Artificial Intelligence in Image-Based Screening, Diagnostics, and Clinical Care of Cardiopulmonary Diseases”, we have highlighted exemplary primary research studies and literature reviews focusing on novel AI/ML methods and their application in image-based screening, diagnosis, and clinical management of cardiopulmonary diseases. We hope that these articles will help establish the advancements in AI

Intelligent Biosignal Processing in Wearable and Implantable Sensors

This reprint provides a collection of papers illustrating the state-of-the-art of smart processing of data coming from wearable, implantable or portable sensors. Each paper presents the design, databases used, methodological background, obtained results, and their interpretation for biomedical applications. Revealing examples are brain–machine interfaces for medical rehabilitation, the evaluation of sympathetic nerve activity, a novel automated diagnostic tool based on ECG data to diagnose COVID-19, machine learning-based hypertension risk assessment by means of photoplethysmography and electrocardiography signals, Parkinsonian gait assessment using machine learning tools, thorough analysis of compressive sensing of ECG signals, development of a nanotechnology application for decoding vagus-nerve activity, detection of liver dysfunction using a wearable electronic nose system, prosthetic hand control using surface electromyography, epileptic seizure detection using a CNN, and premature ventricular contraction detection using deep metric learning. Thus, this reprint presents significant clinical applications as well as valuable new research issues, providing current illustrations of this new field of research by addressing the promises, challenges, and hurdles associated with the synergy of biosignal processing and AI through 16 different pertinent studies. Covering a wide range of research and application areas, this book is an excellent resource for researchers, physicians, academics, and PhD or master students working on (bio)signal and image processing, AI, biomaterials, biomechanics, and biotechnology with applications in medicine

Disease diagnosis in smart healthcare: Innovation, technologies and applications

To promote sustainable development, the smart city implies a global vision that merges artificial intelligence, big data, decision making, information and communication technology (ICT), and the internet-of-things (IoT). The ageing issue is an aspect that researchers, companies and government should devote efforts in developing smart healthcare innovative technology and applications. In this paper, the topic of disease diagnosis in smart healthcare is reviewed. Typical emerging optimization algorithms and machine learning algorithms are summarized. Evolutionary optimization, stochastic optimization and combinatorial optimization are covered. Owning to the fact that there are plenty of applications in healthcare, four applications in the field of diseases diagnosis (which also list in the top 10 causes of global death in 2015), namely cardiovascular diseases, diabetes mellitus, Alzheimer’s disease and other forms of dementia, and tuberculosis, are considered. In addition, challenges in the deployment of disease diagnosis in healthcare have been discussed

Comparison of Artificial Intelligence based approaches to cell function prediction

Predicting Retinal Pigment Epithelium (RPE) cell functions in stem cell implants using non-invasive bright field microscopy imaging is a critical task for clinical deployment of stem cell therapies. Such cell function predictions can be carried out using Artificial Intelligence (AI) based models. In this paper we used Traditional Machine Learning (TML) and Deep Learning (DL) based AI models for cell function prediction tasks. TML models depend on feature engineering and DL models perform feature engineering automatically but have higher modeling complexity. This work aims at exploring the tradeoffs between three approaches using TML and DL based models for RPE cell function prediction from microscopy images and at understanding the accuracy relationship between pixel-, cell feature-, and implant label-level accuracies of models. Among the three compared approaches to cell function prediction, the direct approach to cell function prediction from images is slightly more accurate in comparison to indirect approaches using intermediate segmentation and/or feature engineering steps. We also evaluated accuracy variations with respect to model selections (five TML models and two DL models) and model configurations (with and without transfer learning). Finally, we quantified the relationships between segmentation accuracy and the number of samples used for training a model, segmentation accuracy and cell feature error, and cell feature error and accuracy of implant labels. We concluded that for the RPE cell data set, there is a monotonic relationship between the number of training samples and image segmentation accuracy, and between segmentation accuracy and cell feature error, but there is no such a relationship between segmentation accuracy and accuracy of RPE implant labels

Prediction of malignancy in lung cancer using several strategies for the fusion of multi-channel pyradiomics images

Aquest treball mostra el proces de generació i el posterior estudi de l'espai de representació que obtenim extraient característiques de textura GLCM de tomografies assistides per ordinador (CT) dels noduls pulmonars (PN). El flux del treball se centra en l'extracció de característiques mitjançant Pyradiomics i la xarxa neuronal convolucional (CNN) VGG16. L'estudi té com a objectiu valorar si les dades aconseguides impacten de manera positiva en el diagnòstic de càncer pulmonar (LC). Per dissenyar un mètode d'entrenament de models d'aprenentatge automàtic (ML) que permet generalització, entrenem models SMV amb diferents divisions de dades, valorant el rendiment de la diagnosi mitjançant mètriques definides a nivell de tall i de nòdul. Per aquesta tasca, s'han utilitzat dades de 92 pacients de l'Hospital Universitari Germans Trias i Pujol.Este estudio presenta el proceso de generación y posterior estudio del espacio de representación obtenido mediante la extracción de características de textura GLCM de tomografías computarizadas (TC) de nódulos pulmonares (NP). El flujo de trabajo del estudio se centra en la extracción de características utilizando Pyradiomics y la red neuronal convolucional (CNN) VGG16. El objetivo del estudio es evaluar si los datos obtenidos tienen un impacto positivo en el diagnóstico del cáncer de pulmón (CP). Para diseñar un método de entrenamiento de modelos de aprendizaje automático (ML) que permita la generalización, entrenamos modelos SVM con diferentes subdivisiones de datos, evaluando el rendimiento diagnóstico utilizando métricas definidas tanto a nivel de corte como de nódulo. Para esta tarea, se han utilizado datos de 92 pacientes del Hospital Universitari Germans Trias i Pujol.This study presents the process of generation and subsequent study of the representation space obtained by extracting GLCM texture features from computed tomography (CT) of pulmonary nodules (PN). The workflow of the study focuses on feature extraction using Pyradiomics and the VGG16 convolutional neural network (CNN). The objective of the study is to assess whether the obtained data have a positive impact on the diagnosis of lung cancer (LC). To design a method for training machine learning (ML) models that enables generalization, we train SVM models with different data splits, evaluating the diagnostic performance using metrics defined at both the slice and nodule levels. For this task, data from 92 patients from the Hospital Universitari Germans Trias i Pujol have been used