Chest X-ray Analysis Research Articles

Breaking the threshold: Developing multivariable models using computer-aided chest X-ray analysis for tuberculosis triage

ObjectivesComputer-aided detection (CAD) software packages quantify tuberculosis (TB)-compatible chest X-ray (CXR) abnormality as continuous scores. In practice, a threshold value is selected for binary CXR classification. We assessed the diagnostic accuracy of an alternative approach to applying CAD for TB triage: incorporating CAD scores in multivariable modeling. MethodsWe pooled individual patient data from four studies. Separately, for two commercial CAD, we used logistic regression to model microbiologically confirmed TB. Models included CAD score, study site, age, sex, human immunodeficiency virus status, and prior TB. We compared specificity at target sensitivities ≥90% between the multivariable model and the current threshold-based approach for CAD use. ResultsWe included 4,733/5,640 (84%) participants with complete covariate data (median age 36 years; 45% female; 22% with prior TB; 22% people living with human immunodeficiency virus). A total of 805 (17%) had TB. Multivariable models demonstrated excellent performance (areas under the receiver operating characteristic curve [95% confidence interval]: software A, 0.91 [0.90-0.93]; software B, 0.92 [0.91-0.93]). Compared with threshold scores, multivariable models increased specificity (e.g., at 90% sensitivity, threshold vs model specificity [95% confidence interval]: software A, 71% [68-74%] vs 75% [74-77%]; software B, 69% [63-75%] vs 75% [74-77%]). ConclusionUsing CAD scores in multivariable models outperformed the current practice of CAD-threshold-based CXR classification for TB diagnosis.

Evaluation of C-Reactive Protein and Computer-Aided Analysis of Chest X-rays as Tuberculosis Triage Tests at Health Facilities in Lesotho and South Africa.

To improve tuberculosis case-finding, rapid, non-sputum triage tests need to be developed according to the World Health Organization target product profile (TPP) (>90% sensitivity, >70% specificity). We prospectively evaluated and compared artificial intelligence-based, computer-aided detection software, CAD4TBv7, and C-reactive protein assay (CRP) as triage tests at health facilities in Lesotho and South Africa. Adults (≥18 years) presenting with ≥1 of the 4 cardinal tuberculosis symptoms were consecutively recruited between February 2021 and April 2022. After informed consent, each participant underwent a digital chest X-ray for CAD4TBv7 and a CRP test. Participants provided 1 sputum sample for Xpert MTB/RIF Ultra and Xpert MTB/RIF and 1 for liquid culture. Additionally, an expert radiologist read the chest X-rays via teleradiology. For primary analysis, a composite microbiological reference standard (ie, positive culture or Xpert Ultra) was used. We enrolled 1392 participants, 48% were people with HIV and 24% had previously tuberculosis. The receiver operating characteristic curve for CAD4TBv7 and CRP showed an area under the curve of .87 (95% CI: .84-.91) and .80 (95% CI: .76-.84), respectively. At thresholds corresponding to 90% sensitivity, specificity was 68.2% (95% CI: 65.4-71.0%) and 38.2% (95% CI: 35.3-41.1%) for CAD4TBv7 and CRP, respectively. CAD4TBv7 detected tuberculosis as well as an expert radiologist. CAD4TBv7 almost met the TPP criteria for tuberculosis triage. CAD4TBv7 is accurate as a triage test for patients with tuberculosis symptoms from areas with a high tuberculosis and HIV burden. The role of CRP in tuberculosis triage requires further research. Clinicaltrials.gov identifier: NCT04666311.

Impact of AI-assisted CXR analysis in detecting incidental lung nodules and lung cancers in non-respiratory outpatient clinics.

The use of artificial intelligence (AI) for chest X-ray (CXR) analysis is becoming increasingly prevalent in medical environments. This study aimed to determine whether AI in CXR can unexpectedly detect lung nodule detection and influence patient diagnosis and management in non-respiratory outpatient clinics. In this retrospective study, patients over 18 years of age, who underwent CXR at Yongin Severance Hospital outpatient clinics between March 2021 and January 2023 and were identified to have lung nodules through AI software, were included. Commercially available AI-based lesion detection software (Lunit INSIGHT CXR) was used to detect lung nodules. Out Of 56,802 radiographic procedures, 40,191 were from non-respiratory departments, with AI detecting lung nodules in 1,754 cases (4.4%). Excluding 139 patients with known lung lesions, 1,615 patients were included in the final analysis. Out of these, 30.7% (495/1,615) underwent respiratory consultation and 31.7% underwent chest CT scans (512/1,615). As a result of the CT scans, 71.5% (366 cases) were found to have true nodules. Among these, the final diagnoses included 36 lung cancers (7.0%, 36/512), 141 lung nodules requiring follow-up (27.5%, 141/512), 114 active pulmonary infections (22.3%, 114/512), and 75 old inflammatory sequelae (14.6%, 75/512). The mean AI nodule score for lung cancer was significantly higher than that for other nodules (56.72 vs. 33.44, p < 0.001). Additionally, active pulmonary infection had a higher consolidation score, and old inflammatory sequelae had the highest fibrosis score, demonstrating differences in the AI analysis among the final diagnosis groups. This study indicates that AI-detected incidental nodule abnormalities on CXR in non-respiratory outpatient clinics result in a substantial number of clinically significant diagnoses, emphasizing AI's role in detecting lung nodules and need for further evaluation and specialist consultation for proper diagnosis and management.

Artificial intelligence in tuberculosis diagnosis: Revolutionizing detection and treatment

Artificial intelligence (AI) is rapidly transforming tuberculosis (TB) diagnosis. It is addressing the longstanding challenges in accuracy, efficiency, and accessibility. Traditional diagnostic methods, while effective, often suffer from limitations such as variability in sensitivity and lengthy turnaround times. AI technologies, including machine learning and deep learning algorithms, offer innovative solutions by automating the analysis of chest X-rays, genomic data, and clinical parameters. These advancements promise improved diagnostic accuracy, expedited treatment initiation, and personalized medicine approaches. However, successful implementation requires overcoming challenges related to data quality, integration with healthcare systems, and ethical considerations. Moving forward, this paper sheds light on AI-driven TB diagnosis, which stands poised to enhance global healthcare outcomes through enhanced detection capabilities and optimized treatment strategies.

Artificial intelligence-based radiographic extent analysis to predict tuberculosis treatment outcomes: a multicenter cohort study

Predicting outcomes in pulmonary tuberculosis is challenging despite effective treatments. This study aimed to identify factors influencing treatment success and culture conversion, focusing on artificial intelligence (AI)-based chest X-ray analysis and Xpert MTB/RIF assay cycle threshold (Ct) values. In this retrospective study across six South Korean referral centers (January 1 to December 31, 2019), we included adults with rifampicin-susceptible pulmonary tuberculosis confirmed by Xpert assay from sputum samples. We analyzed patient characteristics, AI-based tuberculosis extent scores from chest X-rays, and Xpert Ct values. Of 230 patients, 206 (89.6%) achieved treatment success. The median age was 61 years, predominantly male (76.1%). AI-based radiographic tuberculosis extent scores (median 7.5) significantly correlated with treatment success (odds ratio [OR] 0.938, 95% confidence interval [CI] 0.895–0.983) and culture conversion at 8 weeks (liquid medium: OR 0.911, 95% CI 0.853–0.973; solid medium: OR 0.910, 95% CI 0.850–0.973). Sputum smear positivity was 49.6%, with a median Ct of 26.2. However, Ct values did not significantly correlate with major treatment outcomes. AI-based radiographic scoring at diagnosis is a significant predictor of treatment success and culture conversion in pulmonary tuberculosis, underscoring its potential in personalized patient management.

Enhancing surgical decision-making in NEC with ResNet18: a deep learning approach to predict the need for surgery through x-ray image analysis.

Necrotizing enterocolitis (NEC) is a severe neonatal intestinal disease, often occurring in preterm infants following the administration of hyperosmolar formula. It is one of the leading causes of neonatal mortality in the NICU, and currently, there are no clear standards for surgical intervention, which typically depends on the joint discretion of surgeons and neonatologists. In recent years, deep learning has been extensively applied in areas such as image segmentation, fracture and pneumonia classification, drug development, and pathological diagnosis. Investigating deep learning applications using bedside x-rays to help optimizing surgical decision-making in neonatal NEC. Through a retrospective analysis of anteroposterior bedside chest and abdominal x-rays from 263 infants diagnosed with NEC between January 2015 and April 2023, including a surgery group (94 cases) and a non-surgery group (169 cases), the infants were divided into a training set and a validation set in a 7:3 ratio. Models were built based on Resnet18, Densenet121, and SimpleViT to predict whether NEC patients required surgical intervention. Finally, the model's performance was tested using an additional 40 cases, including both surgical and non-surgical NEC cases, as a test group. To enhance the interpretability of the models, the study employed 2D-Grad-CAM technology to describe the models' focus on significant areas within the x-ray images. Resnet18 demonstrated outstanding performance in binary diagnostic capability, achieving an accuracy of 0.919 with its precise lesion imaging and interpretability particularly highlighted. Its precision, specificity, sensitivity, and F1 score were significantly high, proving its advantages in optimizing surgical decision-making for neonatal NEC. The Resnet18 deep learning model, constructed using bedside chest and abdominal imaging, effectively assists clinical physicians in determining whether infants with NEC require surgical intervention.

A comparative study on the protective effects of cuminaldehyde, thymoquinone, and gallic acid against carbon tetrachloride-induced pulmonary and renal toxicity in rats by affecting ROS and NF-κB signaling

CCl4 toxicity is a fatal condition that can cause numerous organ dysfunctions. We evaluated and compared the protective effects of cuminaldehyde (CuA), thymoquinone (TQ), and gallic acid (GA) on CCl4-induced pulmonary and renal toxicity in rats. The impacts of these compounds on CCl4-induced oxidative stress, inflammation, and morphological alterations were examined. The results showed that the compounds under investigation prevented CCl4 from significantly increasing pulmonary and renal lipid peroxidation and NO levels, as well as massively depleting GSH levels and GPX and SOD activities. Moreover, they suppressed the CCl4-induced increase in mucus secretion in the lung and upregulated the gene expression of pulmonary and renal NF-ҡB, iNOS, TNF-α, and COX-2. The heatmap cluster plots showed that GA and TQ had better protective potencies than CuA. The external organ morphology, histopathological results, and chest X-ray analysis confirmed the toxicity of CCl4 and the protective influences of the tested compounds in both the lungs and kidneys of rats. These compounds displayed predicted competitive inhibitory effects on iNOS activity and may block the IL-13α2 receptor, as revealed by molecular docking analysis. Thus, CuA, TQ, and GA, particularly the latter two, are prospective protective compounds against the pulmonary and renal toxicity caused by CCl4.

Robust Segmentation of COVID-19 Chest X-Ray Images: Analysis of Variant k-Means Based Clustering Algorithms

Computer aided diagnosis (CADx) become one the most famous method in diagnostic medical field due to the high reliability and efficiency. Recently, the coronavirus disease (COVID-19) has become severe global pandemic. Particularly, the Chest X-ray (CXR) imaging has become an essentiality in COVID-19 detection. As a result, the convergence of CADx technology with Chest X-ray analysis has achieved great efficiency in COVID-19 diagnosis. Therefore, the research value of CADx in COVID-19 diagnosis is exceptionally high. This study aims to evaluate different k-means based clustering algorithms and identifying the one with the highest overall accuracy. First of all, 150 COVID-19 CXR open-source images are acquired from Kaggle and Github. All the images will be unified into a same image size with 1000*1000 pixels and quality during the image pre-processing. Next, the resized images are enhanced by the Modified Global Contrast Stretching (MGCS) enhancement method to increase the quality of images. Then, the traditional k-means, k-medians, k-medoids and fast k-means clustering methods have been implemented in the image segmentation. At the same time, five different numbers &lt;2, 4, 6, 8, 10&gt; of clusters also tested out in this study. Lastly, all the segmented is proceeded to the segmentation performance based on sensitivity, specificity, accuracy, precision, recall and F-score. The result proves that the k-medoids clustering algorithm with 2 clusters archived the best overall segmentation performance as it obtained the highest sensitivity, accuracy, recall and F-score with 66.14%, 87.98%, 0.6614 and 0.7327.

Beyond pulmonary embolism: Alternative diagnosis and incidental findings on CT pulmonary angiography in sickle cell disease.

Sickle cell disease (SCD) is a genetic hematological disorder associated with severe complications, such as vaso-occlusive crises, acute chest syndrome (ACS), and an increased risk of thromboembolic events, including pulmonary embolism (PE). The diagnosis of PE in SCD patients presents challenges due to the overlapping symptoms with other pulmonary conditions. Our previous study revealed that nearly 96% of computed tomography pulmonary angiography (CTPA) scans in SCD patients were negative for PE, highlighting a gap in understanding the significance of CTPA findings when PE is absent. In this retrospective follow-up study conducted at the Salmaniya Medical Complex in Bahrain, we examined SCD patients with HbSS genotypes who underwent CTPA from January 1, 2018, to December 31, 2021, for suspected PE, but the results were negative. The aim of this study was to identify alternative diagnoses and incidental findings from CTPA scans. Experienced radiologists reviewed the CTPA images and reports to assess potential alternative diagnoses and incidental findings, incorporating an additional analysis of chest X-rays to evaluate the diagnostic value of CTPA. Incidental findings were classified based on their location and clinical significance. Among the 230 evaluated SCD patients (average age 39.7 years; 53% male) who were CTPA negative for PE, 142 (61.7%) had identifiable alternative diagnoses, primarily pneumonia (49.1%). Notably, 88.0% of these alternative diagnoses had been previously suggested by chest radiographs. Furthermore, incidental findings were noted in 164 (71.3%) patients, with 11.0% deemed clinically significant, necessitating immediate action, and 87.8% considered potentially significant, requiring further assessment. Notable incidental findings included thoracic abnormalities such as cardiomegaly (12.2%) and an enlarged pulmonary artery (11.3%), as well as upper abdominal pathologies such as hepatomegaly (19.6%), splenomegaly (20.9%), and gallstones (10.4%). This study underscores the limited additional diagnostic yield of CTPA for identifying alternative diagnoses to PE in SCD patients, with the majority of diagnoses, such as pneumonia, already suggested by chest radiographs. The frequent incidental findings, most of which necessitate further evaluation, highlight the need for a cautious and tailored approach to using CTPA in the SCD population.

CAFES: Chest X-ray Analysis using Federated Self-supervised Learning for Pediatric COVID-19 Detection.

Chest X-rays (CXRs) play a pivotal role in cost-effective clinical assessment of various heart and lung related conditions. The urgency of COVID-19 diagnosis prompted their use in identifying conditions like lung opacity, pneumonia, and acute respiratory distress syndrome in pediatric patients. We propose an AI-driven solution for binary COVID-19 versus non-COVID-19 classification in pediatric CXRs. We present a Federated Self-Supervised Learning (FSSL) framework to enhance Vision Transformer (ViT) performance for COVID-19 detection in pediatric CXRs. ViT's prowess in vision-related binary classification tasks, combined with self-supervised pre-training on adult CXR data, forms the basis of the FSSL approach. We implement our strategy on the Rhino Health Federated Computing Platform (FCP), which ensures privacy and scalability for distributed data. The chest X-ray analysis using the federated SSL (CAFES) model, utilizes the FSSL-pre-trained ViT weights and demonstrated gains in accurately detecting COVID-19 when compared with a fully supervised model. Our FSSL-pre-trained ViT showed an area under the precision-recall curve (AUPR) of 0.952, which is 0.231 points higher than the fully supervised model for COVID-19 diagnosis using pediatric data. Our contributions include leveraging vision transformers for effective COVID-19 diagnosis from pediatric CXRs, employing distributed federated learning-based self-supervised pre-training on adult data, and improving pediatric COVID-19 diagnosis performance. This privacy-conscious approach aligns with HIPAA guidelines, paving the way for broader medical imaging applications.

The role of imaging techniques in the diagnosis and treatment of neonatal pneumothorax: A comparative analysis of ultrasound and chest X-ray

The role of imaging techniques in the diagnosis and treatment of neonatal pneumothorax: A comparative analysis of ultrasound and chest X-ray

Advancing Sustainable COVID-19 Diagnosis: Integrating Artificial Intelligence with Bioinformatics in Chest X-ray Analysis

Responding to the critical health crisis triggered by respiratory illnesses, notably COVID-19, this study introduces an innovative and resource-conscious methodology for analyzing chest X-ray images. We unveil a cutting-edge technique that marries neural architecture search (NAS) with genetic algorithms (GA), aiming to refine the architecture of convolutional neural networks (CNNs) in a way that diminishes the usual demand for computational power. Leveraging transfer learning (TL), our approach efficiently navigates the hurdles posed by scarce data, optimizing both time and hardware utilization—a cornerstone for sustainable AI initiatives. The investigation leverages a curated dataset of 1184 COVID-positive and 1319 COVID-negative chest X-ray images, serving as the basis for model training, evaluation, and validation. Our methodology not only boosts the precision in diagnosing COVID-19 but also establishes a pioneering standard in the realm of eco-friendly and effective healthcare technologies. Through comprehensive comparative analyses against leading-edge models, our optimized solutions exhibit significant performance enhancements alongside a minimized ecological impact. This contribution marks a significant stride towards eco-sustainable medical imaging, presenting a paradigm that prioritizes environmental stewardship while adeptly addressing modern healthcare exigencies. We compare our approach to state-of-the-art architectures through multiple comparative studies.

Deep Convolutional Neural Networks with Augmentation for Chest X-Ray Classification

The recent release of large amounts of Chest radiographs (CXR) has prompted the research of automated analysis of Chest X-rays to improve health care services. DCNNs are well suited for image classification because they can learn to extract features from images that are relevant to the task at hand. However, class imbalance is a common problem in chest X-ray imaging, where the number of samples for some disease category is much lower than the number of samples in other categories. This can occur as a result of rarity of some diseases being studied or the fact that only a subset of patients with a particular disease may undergo imaging. Class imbalance can make it difficult for Deep Convolutional Neural networks (DCNNs) to learn and make accurate predictions on the minority classes. Obtaining more data for minority groups is not feasible in medical research. Therefore, there is a need for a suitable method that can address class imbalance. To address class imbalance in DCNNs, this study proposes, Deep Convolutional Neural Networks with Augmentation. The results show that data augmentation can be applied to imbalanced dataset to increase the representation of the minority class by generating new images that are a slight variation of the original CXR images. This study further evaluates identifiability and consistency of the proposed model.

Comparison of the Discrimination Performance of AI Scoring and the Brixia Score in Predicting COVID-19 Severity on Chest X-Ray Imaging: Diagnostic Accuracy Study.

The artificial intelligence (AI) analysis of chest x-rays can increase the precision of binary COVID-19 diagnosis. However, it is unknown if AI-based chest x-rays can predict who will develop severe COVID-19, especially in low- and middle-income countries. The study aims to compare the performance of human radiologist Brixia scores versus 2 AI scoring systems in predicting the severity of COVID-19 pneumonia. We performed a cross-sectional study of 300 patients suspected with and with confirmed COVID-19 infection in Jakarta, Indonesia. A total of 2 AI scores were generated using CAD4COVID x-ray software. The AI probability score had slightly lower discrimination (area under the curve [AUC] 0.787, 95% CI 0.722-0.852). The AI score for the affected lung area (AUC 0.857, 95% CI 0.809-0.905) was almost as good as the human Brixia score (AUC 0.863, 95% CI 0.818-0.908). The AI score for the affected lung area and the human radiologist Brixia score had similar and good discrimination performance in predicting COVID-19 severity. Our study demonstrated that using AI-based diagnostic tools is possible, even in low-resource settings. However, before it is widely adopted in daily practice, more studies with a larger scale and that are prospective in nature are needed to confirm our findings.

A Trustworthy and Explainable Framework for Benchmarking Hybrid Deep Learning Models Based on Chest X-Ray Analysis in CAD Systems

Evaluating the trustworthiness of deep learning-based computer-aided diagnosis (CAD) systems is challenging. There is a need to optimize trust and performance in model selection. A wide range of models based on evaluation metrics can make it challenging to choose the best one, especially for complex multi-criteria decision-making problems. In the case of COVID-19 diagnosis, using both physicians’ evaluation and AI techniques to establish trust is essential. In this study, 1551 chest X-rays were analyzed using deep transfer learning (DTL) with six models and four SVM kernels. This resulted in 24 hybrid DTL–SVM models. Seven metrics were evaluated using fuzzy multiple-criteria decision-making (MCDM), which uses a dynamic decision matrix to select the best model. This matrix incorporates fuzzy-weighted zero-inconsistency (FWZIC) for weight coefficients and Kriterijumska Optimizacija I Kompromisno Resenje (VIKOR) for benchmarking. The Grad-CAM technique compared the best model with 16 images, ensuring explainability. Top-performing models were identified, including SqueezeNet-SVM linear, VGG19-SVM linear, and VGG19-SVM. Sensitivity analysis was used to quantify the impact of changing weighted criteria values. A physician expert validated fuzzy MCDM through Grad-CAM analysis, a new aspect of this study. The framework presented in this study was benchmarked against seven other studies and achieved a perfect score in four crucial areas. Trustworthiness is essential for CAD systems, and this study effectively addresses trust and performance challenges in AI model-based CAD systems. The study systematically evaluated the requirements for trustworthiness, including accountability, fairness, robustness, accuracy, and reproducibility, and the results supported this.

Real-world testing of an artificial intelligence algorithm for the analysis of chest X-rays in primary care settings

Interpreting chest X-rays is a complex task, and artificial intelligence algorithms for this purpose are currently being developed. It is important to perform external validations of these algorithms in order to implement them. This study therefore aims to externally validate an AI algorithm’s diagnoses in real clinical practice, comparing them to a radiologist’s diagnoses. The aim is also to identify diagnoses the algorithm may not have been trained for. A prospective observational study for the external validation of the AI algorithm in a region of Catalonia, comparing the AI algorithm’s diagnosis with that of the reference radiologist, considered the gold standard. The external validation was performed with a sample of 278 images and reports, 51.8% of which showed no radiological abnormalities according to the radiologist's report. Analysing the validity of the AI algorithm, the average accuracy was 0.95 (95% CI 0.92; 0.98), the sensitivity was 0.48 (95% CI 0.30; 0.66) and the specificity was 0.98 (95% CI 0.97; 0.99). The conditions where the algorithm was most sensitive were external, upper abdominal and cardiac and/or valvular implants. On the other hand, the conditions where the algorithm was less sensitive were in the mediastinum, vessels and bone. The algorithm has been validated in the primary care setting and has proven to be useful when identifying images with or without conditions. However, in order to be a valuable tool to help and support experts, it requires additional real-world training to enhance its diagnostic capabilities for some of the conditions analysed. Our study emphasizes the need for continuous improvement to ensure the algorithm’s effectiveness in primary care.

Accuracy of digital chest x-ray analysis with artificial intelligence software as a triage and screening tool in hospitalized patients being evaluated for tuberculosis in Lima, Peru.

Tuberculosis (TB) transmission in healthcare facilities is common in high-incidence countries. Yet, the optimal approach for identifying inpatients who may have TB is unclear. We evaluated the diagnostic accuracy of qXR (Qure.ai, India) computer-aided detection (CAD) software versions 3.0 and 4.0 (v3 and v4) as a triage and screening tool within the FAST (Find cases Actively, Separate safely, and Treat effectively) transmission control strategy. We prospectively enrolled two cohorts of patients admitted to a tertiary hospital in Lima, Peru: one group had cough or TB risk factors (triage) and the other did not report cough or TB risk factors (screening). We evaluated the sensitivity and specificity of qXR for the diagnosis of pulmonary TB using culture and Xpert as primary and secondary reference standards, including stratified analyses based on risk factors. In the triage cohort (n = 387), qXR v4 sensitivity was 0.91 (59/65, 95% CI 0.81-0.97) and specificity was 0.32 (103/322, 95% CI 0.27-0.37) using culture as reference standard. There was no difference in the area under the receiver-operating-characteristic curve (AUC) between qXR v3 and qXR v4 with either a culture or Xpert reference standard. In the screening cohort (n = 191), only one patient had a positive Xpert result, but specificity in this cohort was high (>90%). A high prevalence of radiographic lung abnormalities, most notably opacities (81%), consolidation (62%), or nodules (58%), was detected by qXR on digital CXR images from the triage cohort. qXR had high sensitivity but low specificity as a triage in hospitalized patients with cough or TB risk factors. Screening patients without cough or risk factors in this setting had a low diagnostic yield. These findings further support the need for population and setting-specific thresholds for CAD programs.

Advanced Diagnostics of Respiratory Distress Syndrome in Premature Infants Treated with Surfactant and Budesonide through Computer-Assisted Chest X-ray Analysis.

This research addresses the respiratory distress syndrome (RDS) in preterm newborns caused by insufficient surfactant synthesis, which can lead to serious complications, including pneumothorax, pulmonary hypertension, and pulmonary hemorrhage, increasing the risk of a fatal outcome. By analyzing chest radiographs and blood gases, we specifically focus on the significant contributions of these parameters to the diagnosis and analysis of the recovery of patients with RDS. The study involved 32 preterm newborns, and the analysis of gas parameters before and after the administration of surfactants and inhalation corticosteroid therapy revealed statistically significant changes in values of parameters such as FiO2, pH, pCO2, HCO3, and BE (Sig. < 0.05), while the pO2 parameter showed a potential change (Sig. = 0.061). Parallel to this, the research emphasizes the development of a lung segmentation algorithm implemented in the MATLAB programming environment. The key steps of the algorithm include preprocessing, segmentation, and visualization for a more detailed understanding of the recovery dynamics after RDS. These algorithms have achieved promising results, with a global accuracy of 0.93 ± 0.06, precision of 0.81 ± 0.16, and an F-score of 0.82 ± 0.14. These results highlight the potential application of algorithms in the analysis and monitoring of recovery in newborns with RDS, also underscoring the need for further development of software solutions in medicine, particularly in neonatology, to enhance the diagnosis and treatment of preterm newborns with respiratory distress syndrome.

Revolutionizing COVID-19 Diagnosis: Advancements in Chest X-ray Analysis through Customized Convolutional Neural Networks and Image Fusion Data Augmentation

COVID-19 is produced by a new coronavirus called SARS-CoV-2, has wrought extensive damage. Globally, Patients present a wide range of challenges, which has forced medical professionals to actively seek out cutting-edge therapeutic approaches and technology advancements. Machine learning technologies have significantly enhanced the comprehension and control of the COVID-19 issue. Machine learning enables computers to emulate human-like behavior by efficiently recognizing patterns and extracting valuable insights. Cognitive capacity and aptitude for handling substantial quantities of data. Amidst the battle against COVID-19, firms have promptly employed machine-learning expertise in several ways, such as improving consumer communication, enhance comprehension of the COVID-19 transmission mechanism and expedite research and treatment. This work is centered around the utilization of deep learning techniques for predictive modeling. in individuals impacted with COVID-19. A data augmentation phase is included, utilizing multiexposure picture fusion techniques. Chest X-ray images of healthy individuals and COVID-19 patients make up our dataset.

AUCReshaping: improved sensitivity at high-specificity

The evaluation of deep-learning (DL) systems typically relies on the Area under the Receiver-Operating-Curve (AU-ROC) as a performance metric. However, AU-ROC, in its holistic form, does not sufficiently consider performance within specific ranges of sensitivity and specificity, which are critical for the intended operational context of the system. Consequently, two systems with identical AU-ROC values can exhibit significantly divergent real-world performance. This issue is particularly pronounced in the context of anomaly detection tasks, a commonly employed application of DL systems across various research domains, including medical imaging, industrial automation, manufacturing, cyber security, fraud detection, and drug research, among others. The challenge arises from the heavy class imbalance in training datasets, with the abnormality class often incurring a considerably higher misclassification cost compared to the normal class. Traditional DL systems address this by adjusting the weighting of the cost function or optimizing for specific points along the ROC curve. While these approaches yield reasonable results in many cases, they do not actively seek to maximize performance for the desired operating point. In this study, we introduce a novel technique known as AUCReshaping, designed to reshape the ROC curve exclusively within the specified sensitivity and specificity range, by optimizing sensitivity at a predetermined specificity level. This reshaping is achieved through an adaptive and iterative boosting mechanism that allows the network to focus on pertinent samples during the learning process. We primarily investigated the impact of AUCReshaping in the context of abnormality detection tasks, specifically in Chest X-Ray (CXR) analysis, followed by breast mammogram and credit card fraud detection tasks. The results reveal a substantial improvement, ranging from 2 to 40%, in sensitivity at high-specificity levels for binary classification tasks.