Iga Paszkiewicz

Accurate placement of the endotracheal tube (ETT) is critical for ensuring optimal care for patients requiring mechanical ventilation and preventing potential complications. ETT positioning can be assessed using several methods, with chest X-ray (CXR) being the most precise. Radiologists evaluate whether the ETT requires adjustment by measuring the distance between the distal tip of the ETT and the tracheal carina. This study presents the development of a machine learning model to detect and measure ETT position on adult CXRs and evaluates its performance. Six physicians annotated ETT and trachea locations on a dataset of 3856 CXRs. The U-Net-based model was then trained to generate trachea and ETT segmentations. After post-processing steps, an estimate of the distance between the distal tip of the ETT and the tracheal carina was found. It was demonstrated that the trained model is capable of estimating the position of the ETT and calculating the distance from the tube tip to the tracheal carina. The Dice index for the segmentations on the external validation subset for the trachea and ETT was 89.2% ± 9.0% and 87.8% ± 16.9%, respectively. The estimated absolute error on the external validation subset was 4.72 mm. This model represents a promising tool to support clinicians, particularly in Intensive Care Units, where correct intubation and effective ventilation are critical. It may also be integrated into clinical workflows to facilitate patient management and enhance patient safety.

Background: Atherosclerosis is a degenerative-proliferative disease that leads to lesions primarily in the tunica intima and media of the arteries. Atherosclerotic plaques undergo progressive calcification, and the hydroxyapatite deposited within them absorbs X-rays. The coronary artery calcification score (CAC-score) can be assessed using computed tomography. Intima–media thickness (IMT) and endothelial function, evaluated by flow-mediated dilatation (FMD) of the brachial artery, can be measured using ultrasound. This study aimed to assess the relationship between CAC-score, atherosclerosis risk factors, IMT, and FMD in women, with particular emphasis on the comparison of IMT measurement sites. Methods: The study included 124 women divided into three groups based on CAC-score. The following parameters were evaluated: risk factors for coronary artery disease (CAD), FMD, and IMT. CAD risk factors included age, BMI, smoking status, hypertension, diabetes, and lipid disorders, which were obtained from medical history. Results: A significant positive correlation was observed between CAC-score and IMT of the common carotid artery in women. IMT measured at the carotid bifurcation showed the strongest correlation with CAC-score. No correlation was found between CAC-score and endothelial dysfunction assessed by FMD. Conclusions: IMT, particularly when measured at the carotid bifurcation, was associated with CAC-score in women without diagnosed CAD, whereas no association was observed between FMD and CAC-score in this population.

Introduction: Due to the growing problem of high mortality and incidence of civilization diseases, including metabolic syndrome and obesity, there is an urgent need to find effective ways to prevent it. The purpose of this study was to review the effects of a ketogenic diet on particular systems and organs, as well as to analyze possible associated risks. Methods: Systematic review and current research about the impact of Ketogenic Diet to different systems was made. Authors wanted to highlight pros and cons of conveying this type of nourishment. Results: Consequently, there has been an increased focus on a healthy diet. Mass media allow access to abundant information on the purported beneficial effects of various diets, whether it is a weight loss diet, a cleansing diet, or a diet aimed at supporting the functioning of individual organs. The Ketogenic Diet is one of the most frequently used nowadays. It significantly reduces carbohydrate intake to just 5-10% of the total calorie consumption in favor of approximately 55% to 60% fat intake. According to previous research, we should emphasize the fact that conducting this type of diet can reveal many side effects. Conclusion: Despite the advantages of the diet for people suffering from obesity and diabetes mellitus type 2, for most individuals it results only in temporary improvements and an increase of unfavorable effects. Overall, according to our research, the risks of the ketogenic diet might outweigh its benefits. The Ketogenic Diet is an example of a nutrition trend that should not be used on such a large scale.

Background/Objectives: Aortic-arch calcification (AAC) is a robust predictor of cardiovascular events often overlooked on routine chest radiographs (CXR). This systematic review aimed to evaluate the diagnostic accuracy of artificial intelligence (AI) models for detecting AAC on CXR and assess their potential for clinical implementation. Methods: The review followed PRISMA 2020 guidelines (PROSPERO: CRD420251208627). A search of Embase, PubMed, Scopus, and Web of Science was conducted (Jan 2020–Oct 2025) for studies evaluating AI models detecting AAC in adults. Bias was assessed using QUADAS-2. Due to methodological heterogeneity, a narrative synthesis was performed instead of a meta-analysis. Results: Out of 115 records, three retrospective studies (2022–2024) utilizing CNNs across ~2.7 million images were included. Models demonstrated high diagnostic discrimination (AUROC 0.81–0.99), though performance estimates were often attenuated in external cohorts. Pronounced sensitivity–specificity trade-offs occurred: one model achieved 95.9% recall, while another exhibited near-perfect specificity (0.99) despite markedly low sensitivity (0.22). Although the risk of bias was predominantly low, the overall GRADE certainty remained low due to methodological heterogeneity and the absence of cross-sectional imaging reference standards. Conclusions: Deep learning-based models reliably detect AAC on routine CXR, offering a scalable tool for opportunistic cardiovascular risk stratification. However, significant heterogeneity in model architectures and validation strategies currently limits broad comparability. Future research requires standardized annotation protocols and external validation to ensure clinical generalizability.

Objectives: The purpose of this study is to evaluate the performance of our deep learning algorithm in calculating cardiothoracic ratio (CTR) and thus in the assessment of cardiomegaly or pericardial effusion occurrences on chest radiography (CXR). Methods: From a database of 8000 CXRs, 13 folders with a comparable number of images were created. Then, 1020 images were chosen randomly, in proportion to the number of images in each folder. Afterward, CTR was calculated using RadiAnt Digital Imaging and Communications in Medicine (DICOM) Viewer software (2023.1). Next, heart and lung anatomical areas were marked in 3D Slicer. From these data, we trained an AI model which segmented heart and lung anatomy and determined the CTR value. Results: Our model achieved an Intersection over Union metric of 88.28% for the augmented training subset and 83.06% for the validation subset. F1-score for subsets were accordingly 90.22% and 90.67%. In the comparative analysis of artificial intelligence (AI) vs. humans, significantly lower transverse thoracic diameter (TTD) (p < 0.001), transverse cardiac diameter (TCD) (p < 0.001), and CTR (p < 0.001) values obtained using the neural network were observed. Conclusions: Results confirm that there is a significant correlation between the measurements made by human observers and the neural network. After validation in clinical conditions, our method may be used as a screening test or advisory tool when a specialist is not available, especially on Intensive Care Units (ICUs) or Emergency Departments (ERs) where time plays a key role.

In recent years, deep neural networks have enabled countless innovations in the field of image classification. Encouraged by success in this field, researchers worldwide have demonstrated how to use Convolutional Neural Network techniques in medical imaging problems. In this article, the results were obtained through the use of the EfficientNet in the task of classifying 14 different diseases based on chest X-ray images coming from the NIH (National Institutes of Health) ChestX-ray14 dataset. The approach addresses dataset imbalances by introducing a custom split to ensure fair representation. Binary cross entropy loss is utilized to handle the multi-label difficulty. The model architecture comprises an EfficientNet backbone for feature extraction, succeeded by sequential layers including GlobalAveragePooling, Dense, and BatchNormalization. The main contribution of this paper is a proposed solution that outperforms previous state-of-the-art deep learning models average area under the receiver operating characteristic curve—AUC-ROC (score: 84.28%). The usage of the transfer-learning technique and traditional deep learning engineering techniques was shown to enable us to obtain such results on consumer-class GPUs (graphics processing units).

Diagnostic imaging has become an integral part of the healthcare system. In recent years, scientists around the world have been working on artificial intelligence-based tools that help in achieving better and faster diagnoses. Their accuracy is crucial for successful treatment, especially for imaging diagnostics. This study used a deep convolutional neural network to detect four categories of objects on digital chest X-ray images. The data were obtained from the publicly available National Institutes of Health (NIH) Chest X-ray (CXR) Dataset. In total, 112,120 CXRs from 30,805 patients were manually checked for foreign objects: vascular port, shoulder endoprosthesis, necklace, and implantable cardioverter-defibrillator (ICD). Then, they were annotated with the use of a computer program, and the necessary image preprocessing was performed, such as resizing, normalization, and cropping. The object detection model was trained using the You Only Look Once v8 architecture and the Ultralytics framework. The results showed not only that the obtained average precision of foreign object detection on the CXR was 0.815 but also that the model can be useful in detecting foreign objects on the CXR images. Models of this type may be used as a tool for specialists, in particular, with the growing popularity of radiology comes an increasing workload. We are optimistic that it could accelerate and facilitate the work to provide a faster diagnosis.

Machine learning (ML), artificial neural networks (ANNs), and deep learning (DL) are all topics that fall under the heading of artificial intelligence (AI) and have gained popularity in recent years. ML involves the application of algorithms to automate decision-making processes using models that have not been manually programmed but have been trained on data. ANNs that are a part of ML aim to simulate the structure and function of the human brain. DL, on the other hand, uses multiple layers of interconnected neurons. This enables the processing and analysis of large and complex databases. In medicine, these techniques are being introduced to improve the speed and efficiency of disease diagnosis and treatment. Each of the AI techniques presented in the paper is supported with an example of a possible medical application. Given the rapid development of technology, the use of AI in medicine shows promising results in the context of patient care. It is particularly important to keep a close eye on this issue and conduct further research in order to fully explore the potential of ML, ANNs, and DL, and bring further applications into clinical use in the future.

Background: we aimed to characterize the financial needs expressed through online crowdfunding for urologic cancers. Methods: the data used in this study came from the online crowdfunding platform GoFundMe.com. Using an automated software method, we extracted data for campaigns related to urologic cancers. Subsequently, four independent investigators reviewed all extracted data on prostate, bladder, kidney and testicular cancer. We analyzed campaigns’ basic characteristics, goals, fundraising, type of treatment and factors associated with successful campaigns. Results: in total, we identified 2126 individual campaigns, which were related to direct treatment costs (34%), living expenses (17%) or both (48%). Median fundraising amounts were greatest for testicular cancer. Campaigns for both complementary and alternative medicine (CAM) (median $11,000) or CAM alone (median $8527) achieved higher fundraising totals compared with those for conventional treatments alone (median $5362) (p < 0.01). The number of social media shares was independently associated with campaign success and highest quartile of fundraising. Conclusions: using an automated web-based approach, we identified and characterized online crowdfunding for urologic cancer care. These findings indicated a diverse range of patient needs related to urologic care and factors related to campaigns’ success.