The precise and timely diagnosis of brain tumors is essential for accelerating patient recovery and preserving lives. Brain tumors exhibit a variety of sizes, shapes, and visual characteristics, requiring individualized treatment strategies for each patient. Radiologists require considerable proficiency to manually detect brain malignancies. However, tumor recognition remains inefficient, imprecise, and labor-intensive in manual procedures, underscoring the need for automated methods. This study introduces an effective approach for identifying brain lesions in magnetic resonance imaging (MRI) images, minimizing dependence on manual intervention. The proposed method improves image clarity by combining guided filtering techniques with anisotropic Gaussian side windows (AGSW). A morphological analysis is conducted prior to segmentation to exclude non-tumor regions from the enhanced MRI images. Deep neural networks segment the images, extracting high-quality regions of interest (ROIs) and multiscale features. Identifying salient elements is essential and is accomplished through an attention module that isolates distinctive features while eliminating irrelevant information. An ensemble model is employed to classify brain tumors into different categories. The proposed technique achieves an overall accuracy of 99.94% and 99.67% on the publicly available brain tumor datasets BraTS2020 and Figshare, respectively. Furthermore, it surpasses existing technologies in terms of automation and robustness, thereby enhancing the entire diagnostic process.

Artificial intelligence (AI) is rapidly transforming radiology, with applications spanning disease detection, lesion segmentation, workflow optimization, and report generation. As these tools become more integrated into clinical practice, new concerns have emerged regarding their vulnerability to adversarial attacks. This review provides an in-depth overview of adversarial AI in radiology, a topic of growing relevance in both research and clinical domains. It begins by outlining the foundational concepts and model characteristics that make machine learning systems particularly susceptible to adversarial manipulation. A structured taxonomy of attack types is presented, including distinctions based on attacker knowledge, goals, timing, and computational frequency. The clinical implications of these attacks are then examined across key radiology tasks, with literature highlighting risks to disease classification, image segmentation and reconstruction, and report generation. Potential downstream consequences such as patient harm, operational disruption, and loss of trust are discussed. Current mitigation strategies are reviewed, spanning input-level defenses, model training modifications, and certified robustness approaches. In parallel, the role of broader lifecycle and safeguard strategies are considered. By consolidating current knowledge across technical and clinical domains, this review helps identify gaps, inform future research priorities, and guide the development of robust, trustworthy AI systems in radiology.

To develop an interpretable machine learning (ML) model based on cardiac magnetic resonance (CMR) multimodal parameters and clinical data to discriminate Takotsubo syndrome (TTS), acute myocardial infarction (AMI), and acute myocarditis (AM), and to further assess the diagnostic value of right ventricular (RV) strain in TTS. This study analyzed CMR and clinical data of 130 patients from three centers. Key features were selected using least absolute shrinkage and selection operator regression and random forest. Data were split into a training cohort and an internal testing cohort (ITC) in the ratio 7:3, with overfitting avoided using leave-one-out cross-validation and bootstrap methods. Nine ML models were evaluated using standard performance metrics, with Shapley additive explanations (SHAP) analysis used for model interpretation. A total of 11 key features were identified. The extreme gradient boosting model showed the best performance, with an area under the curve (AUC) value of 0.94 (95% CI: 0.85-0.97) in the ITC. Right ventricular basal circumferential strain (RVCS-basal) was the most important feature for identifying TTS. Its absolute value was significantly higher in TTS patients than in AMI and AM patients (-9.93%, -5.21%, and -6.18%, respectively, p < 0.001), with values above -6.55% contributing to a diagnosis of TTS. This study developed an interpretable ternary classification ML model for identifying TTS and used SHAP analysis to elucidate the significant value of RVCS-basal in TTS diagnosis. An online calculator (https://lsszxyy.shinyapps.io/XGboost/) based on this model was developed to provide immediate decision support for clinical use.

Breast cancer remains a leading cause of mortality among women worldwide, underscoring the need for accurate and timely diagnostic methods. Precise segmentation of nuclei in breast histopathology images is crucial for effective diagnosis and prognosis, offering critical insights into tumor characteristics and informing treatment strategies. This paper presents an enhanced U-Net architecture utilizing ResNet-34 as an advanced backbone, aimed at improving nuclei segmentation performance. The proposed model is evaluated and compared with standard U-Net and its other variants, including U-Net with VGG-16 and Inception-v3 backbones, using the BreCaHad dataset with nuclei masks generated through ImageJ software. The U-Net model with ResNet-34 backbone achieved superior performance, recording an Intersection over Union (IoU) score of 0.795, significantly outperforming the basic U-Net's IoU score of 0.725. The integration of advanced backbones and data augmentation techniques substantially improved segmentation accuracy, especially on limited medical imaging datasets. Comparative analysis demonstrated that ResNet-34 consistently surpassed other configurations across multiple metrics, including IoU, accuracy, precision, and F1 score. Further validation on the BNS and MoNuSeg-2018 datasets confirmed the robustness of the proposed model. This study highlights the potential of advanced deep learning architectures combined with augmentation methods to address challenges in nuclei segmentation, contributing to the development of more effective clinical diagnostic tools and improved patient care outcomes.

This study systematically examines the impact of training database size and the generalizability of deep learning models for synthetic medical image generation. Specifically, we employ a Cycle-Consistency Generative Adversarial Network (CycleGAN) with softly paired data to synthesize kilovoltage computed tomography (kVCT) images from megavoltage computed tomography (MVCT) scans. Unlike previous works, which were constrained by limited data availability, our study uses an extensive database comprising 4,000 patient CT scans, an order of magnitude larger than prior research, allowing for a more rigorous assessment of database size in medical image translation. We quantitatively evaluate the fidelity of the generated synthetic images using established image similarity metrics, including Mean Absolute Error (MAE) and Structural Similarity Index Measure (SSIM). Beyond assessing image quality, we investigate the model's capacity for generalization by analyzing its performance across diverse patient subgroups, considering factors such as sex, age, and anatomical region. This approach enables a more granular understanding of how dataset composition influences model robustness.

A brain tumor is an abnormal growth in the brain that disrupts its functionality and poses a significant threat to human life by damaging neurons. Early detection and classification of brain tumors are crucial to prevent complications and maintain good health. Recent advancements in deep learning techniques have shown immense potential in image classification and segmentation for tumor identification and classification. In this study, we present a platform, BrainView, for detection, and segmentation of brain tumors from Magnetic Resonance Images (MRI) using deep learning. We utilized EfficientNetB7 pre-trained model to design our proposed DeepBrainNet classification model for analyzing brain MRI images to classify its type. We also proposed a EfficinetNetB7 based image segmentation model, called the EffB7-UNet, for tumor localization. Experimental results show significantly high classification (99.96%) and segmentation (92.734%) accuracies for our proposed models. Finally, we discuss the contours of a cloud application for BrainView using Flask and Flutter to help researchers and clinicians use our machine learning models online for research purposes.

To develop an integrative radiopathomic model based on deep learning to predict overall survival (OS) in locally advanced nasopharyngeal carcinoma (LANPC) patients. A cohort of 343 LANPC patients with pretreatment MRI and whole slide image (WSI) were randomly divided into training (n = 202), validation (n = 91), and external test (n = 50) sets. For WSIs, a self-attention mechanism was employed to assess the significance of different patches for the prognostic task, aggregating them into a WSI-level representation. For MRI, a multilayer perceptron was used to encode the extracted radiomic features, resulting in an MRI-level representation. These were combined in a multimodal fusion model to produce prognostic predictions. Model performances were evaluated using the concordance index (C-index), and Kaplan-Meier curves were employed for risk stratification. To enhance model interpretability, attention-based and Integrated Gradients techniques were applied to explain how WSIs and MRI features contribute to prognosis predictions. The radiopathomics model achieved high predictive accuracy in predicting the OS, with a C-index of 0.755 (95 % CI: 0.673-0.838) and 0.744 (95 % CI: 0.623-0.808) in the training and validation sets, respectively, outperforming single-modality models (radiomic signature: 0.636, 95 % CI: 0.584-0.688; deep pathomic signature: 0.736, 95 % CI: 0.684-0.810). In the external test, similar findings were observed for the predictive performance of the radiopathomics, radiomic signature, and deep pathomic signature, with their C-indices being 0.735, 0.626, and 0.660 respectively. The radiopathomics model effectively stratified patients into high- and low-risk groups (P < 0.001). Additionally, attention heatmaps revealed that high-attention regions corresponded with tumor areas in both risk groups. n: The radiopathomics model holds promise for predicting clinical outcomes in LANPC patients, offering a potential tool for improving clinical decision-making.

This study aims to investigate the relationship between muscle activation variables assessed via ultrasound and the comprehensive assessment of geriatric patients, as well as to analyze ultrasound images to determine their correlation with morbimortality factors in frail patients. The present cohort study will be conducted in 500 older adults diagnosed with frailty. A multicenter study will be conducted among the day care centers and nursing homes. This will be achieved through the evaluation of frail older adults via instrumental and functional tests, along with specific ultrasound images to study sarcopenia and nutrition, followed by a detailed analysis of the correlation between all collected variables. This study aims to investigate the correlation between ultrasound-assessed muscle activation variables and the overall health of geriatric patients. It addresses the limitations of previous research by including a large sample size of 500 patients and measuring various muscle parameters beyond thickness. Additionally, it aims to analyze ultrasound images to identify markers associated with higher risk of complications in frail patients. The study involves frail older adults undergoing functional tests and specific ultrasound examinations. A comprehensive analysis of functional, ultrasound, and nutritional variables will be conducted to understand their correlation with overall health and risk of complications in frail older patients. The study was approved by the Research Ethics Committee of the Hospital Universitario Puerta de Hierro, Madrid, Spain (Act nº 18/2023). In addition, the study was registered with https://clinicaltrials.gov/ (NCT06218121).

We aimed to develop and validate multimodal models integrating computed tomography (CT) images, text and tabular clinical data to predict poor functional outcomes and in-hospital mortality in patients with intracerebral hemorrhage (ICH). These models were designed to assist non-specialists in emergency settings with limited access to stroke specialists. A retrospective analysis of 527 patients with ICH admitted to a Japanese tertiary hospital between April 2019 and February 2022 was conducted. Deep learning techniques were used to extract features from three-dimensional CT images and unstructured data, which were then combined with tabular data to develop an L1-regularized logistic regression model to predict poor functional outcomes (modified Rankin scale score 3-6) and in-hospital mortality. The model's performance was evaluated by assessing discrimination metrics, calibration plots, and decision curve analysis (DCA) using temporal validation data. The multimodal model utilizing both imaging and text data, such as medical interviews, exhibited the highest performance in predicting poor functional outcomes. In contrast, the model that combined imaging with tabular data, including physiological and laboratory results, demonstrated the best predictive performance for in-hospital mortality. These models exhibited high discriminative performance, with areas under the receiver operating curve (AUROCs) of 0.86 (95% CI: 0.79-0.92) and 0.91 (95% CI: 0.84-0.96) for poor functional outcomes and in-hospital mortality, respectively. Calibration was satisfactory for predicting poor functional outcomes, but requires refinement for mortality prediction. The models performed similar to or better than conventional risk scores, and DCA curves supported their clinical utility. Multimodal prediction models have the potential to aid non-specialists in making informed decisions regarding ICH cases in emergency departments as part of clinical decision support systems. Enhancing real-world data infrastructure and improving model calibration are essential for successful implementation in clinical practice.

Image segmentation plays a central role in computer vision applications such as medical imaging, industrial inspection, and environmental monitoring. However, evaluating segmentation performance can be particularly challenging when ground truth is not clearly defined, as is often the case in tasks involving subjective interpretation. These challenges are amplified by inter- and intra-observer variability, which complicates the use of human annotations as a reliable reference. To address this, we propose a novel validation framework-referred to as the three-blind validation strategy-that enables rigorous assessment of segmentation models in contexts where subjectivity and label variability are significant. The core idea is to have a third independent expert, blind to the labeler identities, assess a shuffled set of segmentations produced by multiple human annotators and/or automated models. This allows for the unbiased evaluation of model performance and helps uncover patterns of disagreement that may indicate systematic issues with either human or machine annotations. The primary objective of this study is to introduce and demonstrate this validation strategy as a generalizable framework for robust model evaluation in subjective segmentation tasks. We illustrate its practical implementation in a mammography use case involving dense tissue segmentation while emphasizing its potential applicability to a broad range of segmentation scenarios.