Improving Computer-Aided Thoracic Disease Diagnosis through Comparative Analysis Using Chest X-ray Images Taken at Different Times.

Medical professionals in thoracic medicine routinely analyze chest X-ray images, often comparing pairs of images taken at different times to detect lesions or anomalies in patients. This research aims to design a computer-aided diagnosis system that enhances the efficiency of thoracic physicians in comparing and diagnosing X-ray images, ultimately reducing misjudgments. The proposed system encompasses four key components: segmentation, alignment, comparison, and classification of lung X-ray images. Utilizing a public NIH Chest X-ray14 dataset and a local dataset gathered by the Chiayi Christian Hospital in Taiwan, the efficacy of both the traditional methods and deep-learning methods were compared. Experimental results indicate that, in both the segmentation and alignment stages, the deep-learning method outperforms the traditional method, achieving higher average IoU, detection rates, and significantly reduced processing time. In the comparison stage, we designed nonlinear transfer functions to highlight the differences between pre- and post-images through heat maps. In the classification stage, single-input and dual-input network architectures were proposed. The inclusion of difference information in single-input networks enhances AUC by approximately 1%, and dual-input networks achieve a 1.2-1.4% AUC increase, underscoring the importance of difference images in lung disease identification and classification based on chest X-ray images. While the proposed system is still in its early stages and far from clinical application, the results demonstrate potential steps forward in the development of a comprehensive computer-aided diagnostic system for comparative analysis of chest X-ray images.

Ultrasound in the Study of Thoracic Diseases: Innovative Aspects

Thoracic ultrasound (TU) has rapidly gained popularity over the past 10 years. This is in part because ultrasound equipment is available in many settings, more training programmes are educating trainees in this technique, and ultrasound can be done rapidly without exposure to radiation.The aim of this review is to present the most interesting and innovative aspects of the use of TU in the study of thoracic diseases.In pleural diseases, TU has been a real revolution. It helps to differentiate between different types of pleural effusions, guides the performance of pleural biopsies when necessary and is more cost-effective under these conditions, and assists in the decision to remove thoracic drainage after talc pleurodesis.With the advent of COVID19, the use of TU has increased for the study of lung involvement. Nowadays it helps in the diagnosis of pneumonias, tumours and interstitial diseases, and its use is becoming more and more widespread in the Pneumology ward.In recent years, TU guided biopsies have been shown to be highly cost-effective, with other advantages such as the absence of radiation and the possibility of being performed at bedside. The use of contrast in ultrasound to increase the cost-effectiveness of these biopsies is very promising.In the study of the mediastinum and peripheral pulmonary nodules, the introduction of echobronchoscopy has brought about a radical change. It is a fully established technique in the study of lung cancer patients. The introduction of elastography may help to further improve its cost-effectiveness.In critically-ill patients, diaphragmatic ultrasound helps in the assessment of withdrawal of mechanical ventilation, and is now an indispensable tool in the management of these patients. (AU)

Ultrasound in the Study of Thoracic Diseases: Innovative Aspects.

Thoracic ultrasound (TU) has rapidly gained popularity over the past 10 years. This is in part because ultrasound equipment is available in many settings, more training programmes are educating trainees in this technique, and ultrasound can be done rapidly without exposure to radiation. The aim of this review is to present the most interesting and innovative aspects of the use of TU in the study of thoracic diseases. In pleural diseases, TU has been a real revolution. It helps to differentiate between different types of pleural effusions, guides the performance of pleural biopsies when necessary and is more cost-effective under these conditions, and assists in the decision to remove thoracic drainage after talc pleurodesis. With the advent of COVID19, the use of TU has increased for the study of lung involvement. Nowadays it helps in the diagnosis of pneumonias, tumours and interstitial diseases, and its use is becoming more and more widespread in the Pneumology ward. In recent years, TU guided biopsies have been shown to be highly cost-effective, with other advantages such as the absence of radiation and the possibility of being performed at bedside. The use of contrast in ultrasound to increase the cost-effectiveness of these biopsies is very promising. In the study of the mediastinum and peripheral pulmonary nodules, the introduction of echobronchoscopy has brought about a radical change. It is a fully established technique in the study of lung cancer patients. The introduction of elastography may help to further improve its cost-effectiveness. In critically-ill patients, diaphragmatic ultrasound helps in the assessment of withdrawal of mechanical ventilation, and is now an indispensable tool in the management of these patients. In neuromuscular patients, ultrasound is a good predictor of impaired lung function. Currently, in Neuromuscular Disease Units, TU is an indispensable tool. Ultrasound study of the intercostal musculature is also effective in the study of respiratory function, and is widely used in Respiratory Rehabilitation. In Intermediate Care Units, thoracic ultrasound is indispensable for patient management. In these units there are ultrasound protocols for the management of patients with acute dyspnoea that have proven to be very effective.

Thoracic Applications of Spectral CT Scan.

TOPIC IMPORTANCE: Thoracic imaging with CT scan has become an essential component in the evaluation of respiratory and thoracic diseases. Providers have historically used conventional single-energy CT; however, prevalence of dual-energy CT (DECT) is increasing, and as such, it is important for thoracic physicians to recognize the utility and limitations of this technology. REVIEW FINDINGS: The technical aspects of DECT are presented, and practical approaches to using DECT are provided. Imaging at multiple energy spectra allows for postprocessing of the data and the possibility of creating multiple distinct image reconstructions based on the clinical question being asked. The data regarding utility of DECT in pulmonary vascular disorders, ventilatory defects, and thoracic oncology are presented. A pictorial essay is provided to give examples of the strengths associated with DECT. SUMMARY: DECT has been most heavily studied in chronic thromboembolic pulmonary hypertension; however, it is increasingly being used across a wide spectrum of thoracic diseases. DECT combines morphologic and functional assessments in a single imaging acquisition, providing clinicians with a powerful diagnostic tool. Its role in the evaluation and treatment of thoracic diseases will likely continue to expand in the coming years as clinicians become more experienced with the technology.

Interventional Pulmonology: Extending the Breadth of Thoracic Care.

Interventional pulmonary medicine has developed as a subspecialty focused on the management of patients with complex thoracic disease. Leveraging minimally invasive techniques, interventional pulmonologists diagnose and treat pathologies that previously required more invasive options such as surgery. By mitigating procedural risk, interventional pulmonologists have extended the reach of care to a wider pool of vulnerable patients who require therapy. Endoscopic innovations, including endobronchial ultrasound and robotic and electromagnetic bronchoscopy, have enhanced the ability to perform diagnostic procedures on an ambulatory basis. Therapeutic procedures for patients with symptomatic airway disease, pleural disease, and severe emphysema have provided the ability to palliate symptoms. The combination of medical and procedural expertise has made interventional pulmonologists an integral part of comprehensive care teams for patients with oncologic, airway, and pleural needs. This review surveys key areas in which interventional pulmonologists have impacted the care of thoracic disease through bronchoscopic intervention.

Quantitative imaging workshop XIX: Utilizing quantitative thoracic imaging to optimize population health final summary.

Lung cancer screening involves the use of thoracic CT for both detection and measurements of suspicious lung nodules to guide the screening management. Since lung cancer screening eligibility typically requires age over 50 years along with >20 pack-year tobacco exposure, thoracic CT scans also frequently reveal evidence for pulmonary emphysema as well as coronary artery calcification. These three thoracic diseases are collectively three of the leading causes of premature death across the world. Screening for the major thoracic diseases in this heavily tobacco-exposed cohort is broadening the focus of lung cancer screening to a more comprehensive health evaluation including discussing the relevance of screen-detected findings of the heart and lung parenchyma. The status and implications of these emerging issues were reviewed in a multidisciplinary workshop focused on the process of quantitative imaging in the lung cancer screening setting to guide the evolution of this important new area of public health.

MRChexNet: Multi-modal bridge and relational learning for thoracic disease recognition in chest X-rays.

While diagnosing multiple lesion regions in chest X-ray (CXR) images, radiologists usually apply pathological relationships in medicine before making decisions. Therefore, a comprehensive analysis of labeling relationships in different data modes is essential to improve the recognition performance of the model. However, most automated CXR diagnostic methods that consider pathological relationships treat different data modalities as independent learning objects, ignoring the alignment of pathological relationships among different data modalities. In addition, some methods that use undirected graphs to model pathological relationships ignore the directed information, making it difficult to model all pathological relationships accurately. In this paper, we propose a novel multi-label CXR classification model called MRChexNet that consists of three modules: a representation learning module (RLM), a multi-modal bridge module (MBM) and a pathology graph learning module (PGL). RLM captures specific pathological features at the image level. MBM performs cross-modal alignment of pathology relationships in different data modalities. PGL models directed relationships between disease occurrences as directed graphs. Finally, the designed graph learning block in PGL performs the integrated learning of pathology relationships in different data modalities. We evaluated MRChexNet on two large-scale CXR datasets (ChestX-Ray14 and CheXpert) and achieved state-of-the-art performance. The mean area under the curve (AUC) scores for the 14 pathologies were 0.8503 (ChestX-Ray14) and 0.8649 (CheXpert). MRChexNet effectively aligns pathology relationships in different modalities and learns more detailed correlations between pathologies. It demonstrates high accuracy and generalization compared to competing approaches. MRChexNet can contribute to thoracic disease recognition in CXR.

[Chest hemorrhage after left total pulmonary resection for secondary rifampin-resistant tuberculosis:a case report].

The patient had received five courses of anti-tuberculosis treatment for recurrent tuberculosis. The drug sensitivity test results of the first three courses showed drug-sensitive pulmonary tuberculosis, and the fourth diagnosis was rifampin-resistant tuberculosis (RR-TB), complicated by chronic obstructive pulmonary disease, type Ⅱ respiratory failure, pulmonary heart disease, and heart failure (grade Ⅲ). The patient stopped taking the anti-tuberculosis drugs on his own in the eighth month of receiving the resistant treatment. After admission, the symptoms improved temporarily after receiving oxygen therapy, anti-infection, and anti-tuberculosis treatment. Because of hemoptysis, the patient underwent arterial embolization by catheterization, but a large amount of hemoptysis occurred shortly thereafter. Emergency left total lung resection and gauze packing for hemostasis were performed. After surgery, the patient's vital signs were maintained with mechanical ventilation and vasopressors. Forty-eight hours after surgery, the gauze was removed, and the patient underwent tracheotomy, enteral nutrition, and anti-tuberculosis treatment. After discharge, the patient underwent rehabilitative exercise and anti-resistant tuberculosis therapy. The patient's condition remained stable for more than six months of follow-up.

A porcine model of postoperative hemi-diaphragmatic paresis to evaluate a unilateral diaphragmatic pacemaker.

Unilateral phrenic nerve damage is a dreaded complication in congenital heart surgery. It has deleterious effects in neonates and children with uni-ventricular circulation. Diaphragmatic palsy, caused by phrenic nerve damage, impairs respiratory function, especially in new-borns, because their respiration depends on diaphragmatic contractions. Furthermore, Fontan patients with passive pulmonary perfusion are seriously affected by phrenic nerve injury, because diaphragmatic contraction augments pulmonary blood flow. Diaphragmatic plication is currently employed to ameliorate the negative effects of diaphragmatic palsy on pulmonary perfusion and respiratory mechanics. This procedure attenuates pulmonary compression by the abdominal contents. However, there is no contraction of the plicated diaphragm and consequently no contribution to the pulmonary blood flow. Hence, we developed a porcine model of unilateral diaphragmatic palsy in order to evaluate a diaphragmatic pacemaker. Our illustrated step-by-step description of the model generation enables others to replicate and use our model for future studies. Thereby, it might contribute to investigation and advancement of potential improvements for these patients.

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.

[Three-dimensional and 4K Three-dimensional Uniportal Video-assisted Thoracoscopic Surgery for Mediastinal and Chest Wall Disease During Respiratory Surgery].

Uniportal video-assisted thoracoscopic surgery (VATS) lobectomy has recently been used with increasing frequency by thoracoscopic surgeons, even in Japan. However, few reports have previously described uniportal VATS for mediastinal and chest wall disease. From April 2008 to December 2022, 159 patients were treated for mediastinal and chest wall disease. We divided the patients into three groups based on the type of surgery:robot-assisted thoracoscopic surgery( RATS), n=21;multi-portal surgery (using a two-dimensional [2D] system), n=55;and uniportal surgery, n=83. Of the 83 cases in the uniportal surgery group, 49 underwent surgery with a three-dimensional( 3D) or 4K-3D system. The operation duration, blood loss, and postoperative stay duration were compared among the groups. A p-value of <0.05 was considered statistically significant. The operation duration, intraoperative blood loss, and postoperative stay duration were significantly lower in the uniportal group (3D, 4K-3D) than in the multi-portal group (2D), with respective p-values of 0.001, 0.034, and 0.005. The RATS group showed a reduced blood loss trend, but not to a significant degree. In conclusion, our findings suggest that a 3D system can optimize surgical performance compared to a 2D system. In particular, using a 4K-3D system with high-definition imaging and stereoscopic vision enables surgeons to perform less-invasive thoracoscopic surgery than would otherwise be feasible.

Enhancing thoracic disease detection using chest X-rays from PubMed Central Open Access.

Large chest X-rays (CXR) datasets have been collected to train deep learning models to detect thorax pathology on CXR. However, most CXR datasets are from single-center studies and the collected pathologies are often imbalanced. The aim of this study was to automatically construct a public, weakly-labeled CXR database from articles in PubMed Central Open Access (PMC-OA) and to assess model performance on CXR pathology classification by using this database as additional training data. Our framework includes text extraction, CXR pathology verification, subfigure separation, and image modality classification. We have extensively validated the utility of the automatically generated image database on thoracic disease detection tasks, including Hernia, Lung Lesion, Pneumonia, and pneumothorax. We pick these diseases due to their historically poor performance in existing datasets: the NIH-CXR dataset (112,120 CXR) and the MIMIC-CXR dataset (243,324 CXR). We find that classifiers fine-tuned with additional PMC-CXR extracted by the proposed framework consistently and significantly achieved better performance than those without (e.g., Hernia: 0.9335 vs 0.9154; Lung Lesion: 0.7394 vs. 0.7207; Pneumonia: 0.7074 vs. 0.6709; Pneumothorax 0.8185 vs. 0.7517, all in AUC with p< 0.0001) for CXR pathology detection. In contrast to previous approaches that manually submit the medical images to the repository, our framework can automatically collect figures and their accompanied figure legends. Compared to previous studies, the proposed framework improved subfigure segmentation and incorporates our advanced self-developed NLP technique for CXR pathology verification. We hope it complements existing resources and improves our ability to make biomedical image data findable, accessible, interoperable, and reusable.

PediCXR: An open, large-scale chest radiograph dataset for interpretation of common thoracic diseases in children.

Computer-aided diagnosis systems in adult chest radiography (CXR) have recently achieved great success thanks to the availability of large-scale, annotated datasets and the advent of high-performance supervised learning algorithms. However, the development of diagnostic models for detecting and diagnosing pediatric diseases in CXR scans is undertaken due to the lack of high-quality physician-annotated datasets. To overcome this challenge, we introduce and release PediCXR, a new pediatric CXR dataset of 9,125 studies retrospectively collected from a major pediatric hospital in Vietnam between 2020 and 2021. Each scan was manually annotated by a pediatric radiologist with more than ten years of experience. The dataset was labeled for the presence of 36 critical findings and 15 diseases. In particular, each abnormal finding was identified via a rectangle bounding box on the image. To the best of our knowledge, this is the first and largest pediatric CXR dataset containing lesion-level annotations and image-level labels for the detection of multiple findings and diseases. For algorithm development, the dataset was divided into a training set of 7,728 and a test set of 1,397. To encourage new advances in pediatric CXR interpretation using data-driven approaches, we provide a detailed description of the PediCXR data sample and make the dataset publicly available on https://physionet.org/content/vindr-pcxr/1.0.0/ .