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).

Background: Chest X-rays (CXRs) are pivotal in clinical diagnostics, particularly in assessing cardiomegaly through the cardiothoracic ratio (CTR). This systematic review and meta-analysis evaluate the efficacy of artificial intelligence (AI) in automating CTR determination to enhance patient care and streamline diagnostic processes. They are concentrated on comparing the performance of AI models in determining the CTR against human assessments, identifying the most effective models for potential clinical implementation. This study was registered with PROSPERO (no. CRD42023437459). No funding was received. Methods: A comprehensive search of medical databases was conducted in June 2023. The search strategy adhered to the PICO framework. Inclusion criteria encompassed original articles from the last decade focusing on AI-assisted CTR assessment from standing-position CXRs. Exclusion criteria included systematic reviews, meta-analyses, conference abstracts, paediatric studies, non-original articles, and studies using imaging techniques other than X-rays. After initial screening, 117 articles were reviewed, with 14 studies meeting the final inclusion criteria. Data extraction was performed by three independent investigators, and quality assessment followed PRISMA 2020 guidelines, using tools such as the JBI Checklist, AMSTAR 2, and CASP Diagnostic Study Checklist. Risk of bias was assessed according to the Cochrane Handbook guidelines. Results: Fourteen studies, comprising a total of 70,472 CXR images, met the inclusion criteria. Various AI models were evaluated, with differences in dataset characteristics and AI technology used. Common preprocessing techniques included resizing and normalization. The pooled AUC for cardiomegaly detection was 0.959 (95% CI 0.944–0.975). The pooled standardized mean difference for CTR measurement was 0.0353 (95% CI 0.147–0.0760). Significant heterogeneity was found between studies (I² 89.97%, p < 0.0001), with no publication bias detected. Conclusions: Standardizing methodologies is crucial to avoid interpretational errors and advance AI in medical imaging diagnostics. Uniform reporting standards are essential for the further development of AI in CTR measurement and broader medical imaging applications.

Introduction: The development of Artificial Intelligence (AI) and attempts to use it in medicine are increasingly becoming the subject of more scientific research. The aim of this article is to present the effectiveness of the advanced language model, ChatGPT-3.5 in the context of the pass rate...

Background/Objectives: Head Computed Tomography (CT) is an essential diagnostic tool for identifying brain pathologies and visualizing blood vessels. However, CT exposes patients to ionizing radiation, making it necessary to establish local diagnostic reference levels (DRLs) to ensure patient safety. This study aimed to establish DRLs for head CT scans and assess the influence of patient characteristics on radiation dose. Methods: A retrospective analysis was conducted on 2043 non-contrast and 488 contrast-enhanced head CT scans performed between 1 July 2023 and 31 March 2024 using a SIEMENS SOMATOM Definition Edge machine. Computed Tomography Dose Index (CTDIvol) and Dose-Length Product (DLP) values were analyzed, with DRLs set at the 75th percentile. The influence of gender, height, and weight on radiation dose was also evaluated. Results: The DRL for both non-contrast and contrast-enhanced scans was 58.18 mGy for CTDIvol and 1018.11 mGy·cm for DLP per acquisition. Total DLP was 2046.09 mGy·cm for contrast-enhanced and 1027.99 mGy·cm for non-contrast scans. No significant correlation was found between patient characteristics and radiation dose, allowing for a uniform DRL to be established. Conclusions: Uniform DRLs were successfully established for head CT scans, ensuring safe radiation doses for both non-contrast and contrast-enhanced studies. The lack of correlation between patient-specific factors and dose supports the use of standardized DRLs, contributing to optimized radiation safety in head CT diagnostics.

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.

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.