Thorax Diseases Research Articles

Bedaquiline versus injectable containing regimens for rifampicin-resistant and multidrug-resistant tuberculosis in a reference center in Brazil – a real-world evidence study using a retrospective design

BackgroundDrug resistance (DR) is one of the several challenges to global tuberculosis (TB) control. The implementation of bedaquiline (BED) for DR-TB after more than 40 years was expected to improve treatment outcomes as well as microbiologic conversion and adverse events (AE) occurrence.MethodsRetrospective cohort study based on secondary data of patients with rifampicin-resistant (RR) or multidrug-resistant (MDR) TB reported to the Outpatient Clinic of Mycobacterial Diseases of the Thorax Diseases Institute – Federal University of Rio de Janeiro - Brazil, between 2016 and 2023. We aimed to evaluate microbiologic conversion, AE and TB treatment outcomes and compare them according to the treatment regimen used for RR/MDR-TB patients under routine conditions [Injectable Containing Regimens (ICR) versus BED Containing Regimens (BCR)]. Logistic regression and survival analysis using Cox regression and Kaplan Meier curve were used for statistical analysis.ResultsOf the 463 DR-TB patients notified during the study period, 297 (64.1%) were included for analysis (ICR = 197 and BCR = 100). Overall AEs were more frequent (83.7 vs. 16.3%, p < 0.001) and occurred earlier in the ICR group (15 days vs. 65 days, p = 0.003). There were no cases of cardiotoxicity requiring interruption of BED treatment. None of the regimens of treatment tested were associated with smear or culture conversion on Cox regression analysis (p = 0.60 and 0.88, respectively). BED-containing regimens were also associated with favorable outcomes in multivariable logistic regression [adjusted odds ratio (aOR) = 2.63, 95% confidence interval (CI)1.36–5.07, p = 0.004], as higher years of schooling, primary drug resistance, and no previous TB treatment. In the survival analysis, BCR was inversely associated with the occurrence of AE during treatment follow-up (aHR 0.24, 95% CI 0.14–0.41, p < 0.001). In addition, TB treatment regimens with BED were also associated with favorable outcomes (aHR 2.41, 95% CI 1.62–3.57, p < 0.001), along with no illicit drug use and primary drug resistance.ConclusionsThe implementation of a fully oral treatment for RR/MDR-TB in a reference center in Brazil was safe and associated with favorable outcomes under routine conditions, despite social, demographic, and behavioral factors that may influence TB treatment completion.

Harnessing the wisdom of a radiologist: Texture-aware curriculum self-supervised learning for thorax disease classification

Harnessing the wisdom of a radiologist: Texture-aware curriculum self-supervised learning for thorax disease classification

Benchmarking a Foundation Large Language Model on its Ability to Relabel Structure Names in Accordance With the American Association of Physicists in Medicine Task Group-263 Report

To introduce the concept of using large language models (LLMs) to relabel structure names in accordance with the American Association of Physicists in Medicine Task Group-263 standard and to establish a benchmark for future studies to reference. Generative Pretrained Transformer (GPT)-4 was implemented within a Digital Imaging and Communications in Medicine server. Upon receiving a structure-set Digital Imaging and Communications in Medicine file, the server prompts GPT-4 to relabel the structure names according to the American Association of Physicists in Medicine Task Group-263 report. The results were evaluated for 3 disease sites: prostate, head and neck, and thorax. For each disease site, 150 patients were randomly selected for manually tuning the instructions prompt (in batches of 50), and 50 patients were randomly selected for evaluation. Structure names considered were those that were most likely to be relevant for studies using structure contours for many patients. The per-patient accuracy was 97.2%, 98.3%, and 97.1% for prostate, head and neck, and thorax disease sites, respectively. On a per-structure basis, the clinical target volume was relabeled correctly in 100%, 95.3%, and 92.9% of cases, respectively. Given the accuracy of GPT-4 in relabeling structure names as presented in this work, LLMs are poised to become an important method for standardizing structure names in radiation oncology, especially considering the rapid advancements in LLM capabilities that are likely to continue.

Three-Stage Framework for Accurate Pediatric Chest X-ray Diagnosis Using Self-Supervision and Transfer Learning on Small Datasets.

Pediatric respiratory disease diagnosis and subsequent treatment require accurate and interpretable analysis. A chest X-ray is the most cost-effective and rapid method for identifying and monitoring various thoracic diseases in children. Recent developments in self-supervised and transfer learning have shown their potential in medical imaging, including chest X-ray areas. In this article, we propose a three-stage framework with knowledge transfer from adult chest X-rays to aid the diagnosis and interpretation of pediatric thorax diseases. We conducted comprehensive experiments with different pre-training and fine-tuning strategies to develop transformer or convolutional neural network models and then evaluate them qualitatively and quantitatively. The ViT-Base/16 model, fine-tuned with the CheXpert dataset, a large chest X-ray dataset, emerged as the most effective, achieving a mean AUC of 0.761 (95% CI: 0.759-0.763) across six disease categories and demonstrating a high sensitivity (average 0.639) and specificity (average 0.683), which are indicative of its strong discriminative ability. The baseline models, ViT-Small/16 and ViT-Base/16, when directly trained on the Pediatric CXR dataset, only achieved mean AUC scores of 0.646 (95% CI: 0.641-0.651) and 0.654 (95% CI: 0.648-0.660), respectively. Qualitatively, our model excels in localizing diseased regions, outperforming models pre-trained on ImageNet and other fine-tuning approaches, thus providing superior explanations. The source code is available online and the data can be obtained from PhysioNet.

Towards long-tailed, multi-label disease classification from chest X-ray: Overview of the CXR-LT challenge

Many real-world image recognition problems, such as diagnostic medical imaging exams, are “long-tailed” – there are a few common findings followed by many more relatively rare conditions. In chest radiography, diagnosis is both a long-tailed and multi-label problem, as patients often present with multiple findings simultaneously. While researchers have begun to study the problem of long-tailed learning in medical image recognition, few have studied the interaction of label imbalance and label co-occurrence posed by long-tailed, multi-label disease classification. To engage with the research community on this emerging topic, we conducted an open challenge, CXR-LT, on long-tailed, multi-label thorax disease classification from chest X-rays (CXRs). We publicly release a large-scale benchmark dataset of over 350,000 CXRs, each labeled with at least one of 26 clinical findings following a long-tailed distribution. We synthesize common themes of top-performing solutions, providing practical recommendations for long-tailed, multi-label medical image classification. Finally, we use these insights to propose a path forward involving vision-language foundation models for few- and zero-shot disease classification.

Automated thorax disease diagnosis using multi-branch residual attention network

Chest X-ray (CXR) is an extensively utilized radiological modality for supporting the diagnosis of chest diseases. However, existing research approaches suffer from limitations in effectively integrating multi-scale CXR image features and are also hindered by imbalanced datasets. Therefore, there is a pressing need for further advancement in computer-aided diagnosis (CAD) of thoracic diseases. To tackle these challenges, we propose a multi-branch residual attention network (MBRANet) for thoracic disease diagnosis. MBRANet comprises three components. Firstly, to address the issue of inadequate extraction of spatial and positional information by the convolutional layer, a novel residual structure incorporating a coordinate attention (CA) module is proposed to extract features at multiple scales. Next, based on the concept of a Feature Pyramid Network (FPN), we perform multi-scale feature fusion in the following manner. Thirdly, we propose a novel Multi-Branch Feature Classifier (MFC) approach, which leverages the class-specific residual attention (CSRA) module for classification instead of relying solely on the fully connected layer. In addition, the designed BCEWithLabelSmoothing loss function improves the generalization ability and mitigates the problem of class imbalance by introducing a smoothing factor. We evaluated MBRANet on the ChestX-Ray14, CheXpert, MIMIC-CXR, and IU X-Ray datasets and achieved average AUCs of 0.841, 0.895, 0.805, and 0.745, respectively. Our method outperformed state-of-the-art baselines on these benchmark datasets.

Modified fuzzy based neuro networks for the prediction of common thorax diseases

Modified fuzzy based neuro networks for the prediction of common thorax diseases

Evaluating the quality of visual explanations on chest X-ray images for thorax diseases classification

AbstractDeep learning models are extensively used but often lack transparency due to their complex internal mechanics. To bridge this gap, the field of explainable AI (XAI) strives to make these models more interpretable. However, a significant obstacle in XAI is the absence of quantifiable metrics for evaluating explanation quality. Existing techniques, reliant on manual assessment or inadequate metrics, face limitations in scalability, reproducibility, and trustworthiness. Recognizing these issues, the current study specifically addresses the quality assessment of visual explanations in medical imaging, where interpretability profoundly influences diagnostic accuracy and trust in AI-assisted decisions. Introducing novel criteria such as informativeness, localization, coverage, multi-target capturing, and proportionality, this work presents a comprehensive method for the objective assessment of various explainability algorithms. These newly introduced criteria aid in identifying optimal evaluation metrics. The study expands the domain’s analytical toolkit by examining existing metrics, which have been prevalent in recent works for similar applications, and proposing new ones. Rigorous analysis led to selecting Jensen–Shannon divergence (JS_DIV) as the most effective metric for visual explanation quality. Applied to the multi-label, multi-class diagnosis of thoracic diseases using a trained classifier on the CheXpert dataset, local interpretable model-agnostic explanations (LIME) with diverse segmentation strategies interpret the classifier’s decisions. A qualitative analysis on an unseen subset of the VinDr-CXR dataset evaluates these metrics, confirming JS_DIV’s superiority. The subsequent quantitative analysis optimizes LIME’s hyper-parameters and benchmarks its performance across various segmentation algorithms, underscoring the utility of an objective assessment metric in practical applications.

Efficient Thorax Disease Classification and Localization Using DCNN and Chest X-ray Images.

Thorax disease is a life-threatening disease caused by bacterial infections that occur in the lungs. It could be deadly if not treated at the right time, so early diagnosis of thoracic diseases is vital. The suggested study can assist radiologists in more swiftly diagnosing thorax disorders and in the rapid airport screening of patients with a thorax disease, such as pneumonia. This paper focuses on automatically detecting and localizing thorax disease using chest X-ray images. It provides accurate detection and localization using DenseNet-121 which is foundation of our proposed framework, called Z-Net. The proposed framework utilizes the weighted cross-entropy loss function (W-CEL) that manages class imbalance issue in the ChestX-ray14 dataset, which helped in achieving the highest performance as compared to the previous models. The 112,120 images contained in the ChestX-ray14 dataset (60,412 images are normal, and the rest contain thorax diseases) were preprocessed and then trained for classification and localization. This work uses computer-aided diagnosis (CAD) system that supports development of highly accurate and precise computer-aided systems. We aim to develop a CAD system using a deep learning approach. Our quantitative results show high AUC scores in comparison with the latest research works. The proposed approach achieved the highest mean AUC score of 85.8%. This is the highest accuracy documented in the literature for any related model.

Deep reinforcement learning framework for thoracic diseases classification via prior knowledge guidance.

The chest X-ray is commonly employed in the diagnosis of thoracic diseases. Over the years, numerous approaches have been proposed to address the issue of automatic diagnosis based on chest X-rays. However, the limited availability of labeled data for related diseases remains a significant challenge in achieving accurate diagnoses. This paper focuses on the diagnostic problem of thorax diseases and presents a novel deep reinforcement learning framework. This framework incorporates prior knowledge to guide the learning process of diagnostic agents, and the model parameters can be continually updated as more data becomes available, mimicking a person's learning process. Specifically, our approach offers two key contributions: (1) prior knowledge can be acquired from pre-trained models using old data or similar data from other domains, effectively reducing the dependence on target domain data; and (2) the reinforcement learning framework enables the diagnostic agent to be as exploratory as a human, leading to improved diagnostic accuracy through continuous exploration. Moreover, this method effectively addresses the challenge of learning models with limited data, enhancing the model's generalization capability. We evaluate the performance of our approach using the well-known NIH ChestX-ray 14 and CheXpert datasets, and achieve competitive results. More importantly, in clinical application, we make considerable progress. The source code for our approach can be accessed at the following URL: https://github.com/NeaseZ/MARL.

A Hard Knowledge Regularization Method with Probability Difference in Thorax Disease Images

The computer-aided thorax disease diagnosis suffers from the existing noisy labels in large-scale datasets. Especially, the fine-grained thorax images also show high inter-similarity, which leads to the classification task being more challenging. Previous works select the small-loss samples to optimize the deep networks but neglect the knowledge of the hard positive examples with large losses. To address the above problems, in this paper, we propose a novel Probability Difference Regularization method (PDR) to explore more knowledge from the hard positive examples for thorax disease diagnosis. PDR regularizes the deep network with the predicted probability discrepancy between the ground truth and the other classes. We first define a Probability Difference score as the difference between the predicted probability corresponding to the ground truth and the largest probability according to the other categories. Then the PDR regularizes the network by the negative of the PD score. Consequently, PDR benefits the classification in two folds. First, it could enlarge the predicted probability gap between the ground truth and other classes. Second, it enhances the learning of hard examples by introducing extra penalties for incorrect predictions during training. PDR is complimented for both multi-class and multi-label classification problems. We conduct experiments to validate the effectiveness of the proposed method on the ChestX-ray2017 dataset (which easily introduces noisy labels) and the ChestX-ray14 dataset (a real noisy dataset). Experimental results demonstrate that PDR consistently improves the performance of multi-class and multi-label thorax disease classification tasks.

Survey on deep learning techniques for thorax disease classification task

In recent years, medical image analysis has been widely studied, largely because it provides useful tools to support daily clinical routines. Thorax disease classification (TDC) is an important one among these tools since Chest X-ray (CXR) has become prevalent in daily clinical examinations. It aims to distinguish between normal and abnormal CXR images according to different diseases. To this aim, many thorax disease classification systems have been proposed to increase performance. Most of them adopt deep learning techniques to train a deep neural network (DNN) by using labeled CXR datasets. The TDC performance has been continually refined. Aiming to provide future researchers with the work being done on TDC to date, we review the DNN models for this task. A brief review of each method along with their evaluations on a set of benchmark datasets is included. Moreover, we give a detailed comparison of these methods as well as a conclusion.

Novel Approach for Thorax Disease Classification using Deep Learning

The task of thorax disease diagnosis on chest X- ray (CXR) pictures is discussed in this work. The majority of available approaches learn a network using global images as input. Thorax disease, on the other hand, frequently occurs in disease-specific (small) localized areas. As a result, the (excessive) irrelevant noisy areas may impact CNN training utilizing a global image. Furthermore, the presence of uneven borders hampered network performance due to poor alignment of some CXR pictures. In this research, we suggest using two-branch architecture called ConsultNet to train discriminative features and satisfy both of these goals at the same time. ConsultNet is made up of two parts. 1) a feature selector bound by an information bottleneck retrieves key disease-specific features based on their relevance. 2) a feature integrator based on spatial and channel encoding improves the latent semantic dependencies in the feature space. ConsultNet combines these distinguishing characteristics to improve thoracic illness categorization in CXRs. Experiments on the ChestX-ray14 and CheXpert datasets have shown that the proposed strategy is effective.

Centralized contrastive loss with weakly supervised progressive feature extraction for fine-grained common thorax disease retrieval in chest x-ray.

Medical images have already become an essential tool for the diagnosis of many diseases. Thus a large number of medical images are being generated due to the daily routine inspection. An efficient image-based disease retrieval system will not only make full use of existing data, but also help physicians to prognosis the diseases. Medical image retrieval is represented by the classification and localization of common thorax diseases in x-ray images. Although extensive efforts have been put into this field, there are still manychallenges. Most of the existing fine-grained image research methods just apply existing deep learning frameworks in extracting the image features. However, these high-level features mainly focus on the global representations of the object, rather than simultaneously considering the local ones. It requires fine-grained details to classify the images with similar lesion areas. Thus, it is necessary to combine the global features and local ones to make the features more discriminative. On the other hand, training CNN models based on current existing strategies have a high time complexity, and is hard to get the discriminative features mentioned above. In addition, the visual retrieval method of fine-grained medical images still has the problem of insufficient sample data with accurate annotationinformation. To address above challenges, we introduced a novel fine-grained medical images retrieval method. First, a centralized contrastive loss (CCLoss) is proposed as our metric learning loss function. Parameters are updated by using the center point, which not only improves the distinguishing performance of features, but also effectively reduces the time complexity of the algorithm. In addition, a weakly supervised progressive feature extraction method is proposed to gradually extract the combined features. And the attention mechanism module is applied to screen the target information after the initial positioning for fine refinement, so as to separate the features with a high degree of discrimination. The retrieval of 14 different chest diseases is evaluated on the chest x-raydatasets. Compared with the existing research methods, the proposed method shows a better retrieval result for Recall@8 by 2.26 and achieves a very efficient training speed which is 100 times faster than the pair-wise loss-based training strategy. We also assessed the effects of Recall@k (k = 2, 4, 6, 8) for progressive features extracted from different steps to obtain a model with the best retrievalperformance. The proposed model is capable of learning discriminative representations from chest x-ray datasets, and it achieves better performance compared with other state-of-the-art methods. Therefore, the developed model would be useful in the diagnosis of common thorax disease or unknown chestdisease.

How Does Pruning Impact Long-Tailed Multi-label Medical Image Classifiers?

Pruning has emerged as a powerful technique for compressing deep neural networks, reducing memory usage and inference time without significantly affecting overall performance. However, the nuanced ways in which pruning impacts model behavior are not well understood, particularly for long-tailed, multi-label datasets commonly found in clinical settings. This knowledge gap could have dangerous implications when deploying a pruned model for diagnosis, where unexpected model behavior could impact patient well-being. To fill this gap, we perform the first analysis of pruning's effect on neural networks trained to diagnose thorax diseases from chest X-rays (CXRs). On two large CXR datasets, we examine which diseases are most affected by pruning and characterize class "forgettability" based on disease frequency and co-occurrence behavior. Further, we identify individual CXRs where uncompressed and heavily pruned models disagree, known as pruning-identified exemplars (PIEs), and conduct a human reader study to evaluate their unifying qualities. We find that radiologists perceive PIEs as having more label noise, lower image quality, and higher diagnosis difficulty. This work represents a first step toward understanding the impact of pruning on model behavior in deep long-tailed, multi-label medical image classification. All code, model weights, and data access instructions can be found at https://github.com/VITA-Group/PruneCXR.

Common Thorax Diseases Recognition Using Zero-Shot Learning With Ontology in the Multi-Labeled ChestX-ray14 Data Set

Disease detection/recognition with limited data sets and labels in the medical image domain is a very costly and greatest challenge. Although open image data sets have increased recently, researches on this problem still need to be developed. Researches to diversify data sets are both costly and face the problem of subjectivity. Unseen classes can be trained with the Zero-Shot Learning (ZSL) in order to overcome this problem. In this paper, we aimed to strengthen ZSL by using ontology as an auxiliary information for class embeddings. In our approach, ZSL is supported by the image embeddings and class embeddings of the multi-labelled ChestX-ray14 data set, as well as the semantic data from DBpedia. In this paper, which we believe will be pioneering in the medical image domain, the Cosine, Hamming and Euclidean distances were taken into account in order to maximize the similarities.We trained ResNet50 neural network with different parameters on the multi-labelled ChestX-ray14 data set. 23.25% precision value in one-to-one matching and 29.59% precision value in at least one matching were obtained. We think that this paper will make a significant contribution to the medical image domain by detecting/recognizing unseen disease images.

A Binary Multi Class and Multi Level Classification with Dual Priority Labelling Model for COVID-19 and Other Thorax Disease Detection

A Binary Multi Class and Multi Level Classification with Dual Priority Labelling Model for COVID-19 and Other Thorax Disease Detection

A Dual-Branch Network for Diagnosis of Thorax Diseases from Chest X-rays.

Automated chest X-ray analysis has a great potential for diagnosing thorax diseases since errors in diagnosis have always been a concern among radiologists. Being a multi-label classification problem, achieving accurate classification still remains challenging. Several studies have focused on accurately segmenting the lung regions from the chest X-rays to deal with the challenges involved. The features extracted from the lung regions typically provide precise clues for diseases like nodules. However, such methods ignore the features outside the lung regions, which have been shown to be crucial for diagnosing conditions like cardiomegaly. Therefore, in this work, we explore a dual-branch network-based framework that relies on features extracted from the lung regions as well as the entire chest X-rays. The proposed framework uses a novel network named R-I UNet for segmenting the lung regions. The dual-branch network in the proposed framework employs two pre-trained AlexNet models to extract discriminative features, forming two feature vectors. Each of these feature vectors is fed into a recurrent neural network consisting of a stack of gated recurrent units with skip connections. Finally, the resulting feature vectors are concatenated for classification. The R-I UNet has been evaluated on the JSRT and Montgomery (MC) datasets, while the dual-branch classification network has been evaluated on the NIH ChestXray14 dataset. The proposed models achieve state-of-the-art performance for both segmentation and classification tasks on the above benchmark datasets. Specifically, our lung segmentation model achieves a 5-fold cross-validation accuracy of 98.18 % and 99.14 % on MC and JSRT datasets. For classification, the proposed approach achieves state-of-the-art AUC for 9 out of 14 diseases with a mean AUC of 0.842 on NIH ChestXray14 dataset. The source code is available at https://github.com/JigneshChowdary/CXR_Classificationhttps: //github.com / JigneshChowdary /CXR_Classification.

Chest Disease Detection and Classification

Chest diseases &amp; conditions such as Atelectasis, Cardiomegaly, Lung consolidation, Hernia, and Fibrosis becoming increasingly prevalent in the Asia-Pacific region. The Asia-Pacific Burden of Respiratory Diseases study examined the disease and economic burden of lung diseases across the Asia-Pacific and more specifically India. The objective is to use a deep learning model to diagnose pathologies from Chest X-Rays. ML approaches on CT and Xray images aided incorrectly in identifying lung diseases. Respiratory diseases range from mild and self-limiting, such as the common cold, influenza, and pharyngitis to life-threatening diseases such as bacterial pneumonia, pleural thickening, hernia, and severe acute respiratory syndromes, such as COVID-19. Authorities &amp; Doctors will be able to deal with the effects more efficiently if such illnesses can be detected speedily and accurately with little human intervention in the future. In addition, various additional elements, such as environmental influences and commonalities among the most afflicted places, should be considered to slow the spread of lung diseases and precautions should be taken. Chest X-ray exam is one of the most frequent and cost-effective medical imaging examination. However clinical diagnosis of chest X-ray can be challenging, and sometimes believed to be harder than diagnosis via chest CT imaging. Even some promising work have been reported in the past, and especially in recent deep learning work on Tuberculosis (TB) classification. To achieve clinically relevant computer-aided detection and diagnosis (CAD) in real world medical sites on all data settings of chest X-rays is still very difficult, if not impossible when only several thousands of images are employed for study. This is evident from [2] where the performance deep neural networks for thorax disease recognition is severely limited by the availability of only 4143 frontal view images [3].

Learning to diagnose common thorax diseases on chest radiographs from radiology reports in Vietnamese.

Deep learning, in recent times, has made remarkable strides when it comes to impressive performance for many tasks, including medical image processing. One of the contributing factors to these advancements is the emergence of large medical image datasets. However, it is exceedingly expensive and time-consuming to construct a large and trustworthy medical dataset; hence, there has been multiple research leveraging medical reports to automatically extract labels for data. The majority of this labor, however, is performed in English. In this work, we propose a data collecting and annotation pipeline that extracts information from Vietnamese radiology reports to provide accurate labels for chest X-ray (CXR) images. This can benefit Vietnamese radiologists and clinicians by annotating data that closely match their endemic diagnosis categories which may vary from country to country. To assess the efficacy of the proposed labeling technique, we built a CXR dataset containing 9,752 studies and evaluated our pipeline using a subset of this dataset. With an F1-score of at least 0.9923, the evaluation demonstrates that our labeling tool performs precisely and consistently across all classes. After building the dataset, we train deep learning models that leverage knowledge transferred from large public CXR datasets. We employ a variety of loss functions to overcome the curse of imbalanced multi-label datasets and conduct experiments with various model architectures to select the one that delivers the best performance. Our best model (CheXpert-pretrained EfficientNet-B2) yields an F1-score of 0.6989 (95% CI 0.6740, 0.7240), AUC of 0.7912, sensitivity of 0.7064 and specificity of 0.8760 for the abnormal diagnosis in general. Finally, we demonstrate that our coarse classification (based on five specific locations of abnormalities) yields comparable results to fine classification (twelve pathologies) on the benchmark CheXpert dataset for general anomaly detection while delivering better performance in terms of the average performance of all classes.