<b>Background:</b> Chest radiographs play a crucial role in tuberculosis screening in high-prevalence regions, although widespread radiographic screening requires expertise that may be unavailable in settings with limited medical resources. <b>Objectives:</b> To evaluate a multimodal generative artificial intelligence (AI) model for detecting tuberculosis-associated abnormalities on chest radiography in patients undergoing tuberculosis screening. <b>Methods:</b> This retrospective study evaluated 800 chest radiographs obtained from two public datasets originating from tuberculosis screening programs. A generative AI model was used to create free-text reports for the radiographs. AI-generated reports were classified in terms of presence versus absence and laterality of tuberculosis-related abnormalities. Two radiologists independently reviewed the radiographs for tuberculosis presence and laterality in separate sessions, without and with use of AI-generated reports and recorded if they would accept the report without modification. Two additional radiologists reviewed radiographs and clinical readings from the datasets to determine the reference standard. <b>Results:</b> By the reference standard, 422/800 radiographs were positive for tuberculosis-related abnormalities. For detection of tuberculosis-related abnormalities, sensitivity, specificity, and accuracy were 95.2%, 86.7%, and 90.8% for AI-generated reports; 93.1%, 93.6%, and 93.4% for reader 1 without AI-generated reports; 93.1%, 95.0%, and 94.1% for reader 1 with AI-generated reports; 95.8%, 87.2%, and 91.3% for reader 2 without AI-generated reports; and 95.8%, 91.5%, and 93.5% for reader 2 with AI-generated reports. Accuracy was significantly lower for AI-generated reports than for both readers alone (p<.001), but significantly higher with than without AI-generated reports for one reader (reader 1: p=.47; reader 2: p=.47). Localization performance was significantly lower (p<.001) for AI-generated reports (63.3%) than for reader 1 (79.9%) and reader 2 (77.9%) without AI-generated reports and did not significantly change for either reader with AI-generated reports (reader 1: 78.7%, p=.71; reader 2: 81.5%, p=.23). Among normal and abnormal radiographs, reader 1 accepted 91.7% and 52.4%, while reader 2 accepted 83.2% and 37.0%, respectively, of AI-generated reports. <b>Conclusion:</b> While AI-generated reports may augment radiologists' diagnostic assessments, the current model requires human oversight given inferior standalone performance. <b>Clinical Impact:</b> The generative AI model could have potential application to aid tuberculosis screening programs in medically underserved regions, although technical improvements remain required.

The limited availability of bronchoscopy images makes image synthesis particularly interesting for training deep learning models. Robust image translation across different domains -- virtual bronchoscopy, phantom as well as in-vivo and ex-vivo image data -- is pivotal for clinical applications. This paper proposes BronchoGAN introducing anatomical constraints for image-to-image translation being integrated into a conditional GAN. In particular, we force bronchial orifices to match across input and output images. We further propose to use foundation model-generated depth images as intermediate representation ensuring robustness across a variety of input domains establishing models with substantially less reliance on individual training datasets. Moreover our intermediate depth image representation allows to easily construct paired image data for training. Our experiments showed that input images from different domains (e.g. virtual bronchoscopy, phantoms) can be successfully translated to images mimicking realistic human airway appearance. We demonstrated that anatomical settings (i.e. bronchial orifices) can be robustly preserved with our approach which is shown qualitatively and quantitatively by means of improved FID, SSIM and dice coefficients scores. Our anatomical constraints enabled an improvement in the Dice coefficient of up to 0.43 for synthetic images. Through foundation models for intermediate depth representations, bronchial orifice segmentation integrated as anatomical constraints into conditional GANs we are able to robustly translate images from different bronchoscopy input domains. BronchoGAN allows to incorporate public CT scan data (virtual bronchoscopy) in order to generate large-scale bronchoscopy image datasets with realistic appearance. BronchoGAN enables to bridge the gap of missing public bronchoscopy images.

Cerebrovascular morphology plays a critical role in brain health, influencing cerebral blood flow (CBF) and contributing to the pathogenesis of various neurological diseases. This review examines the anatomical structure of the cerebrovascular network and its variations in healthy and diseased populations and highlights age-related changes and their implications in various neurological conditions. Normal variations, including the completeness and anatomical anomalies of the Circle of Willis and collateral circulation, are discussed in relation to their impact on CBF and susceptibility to ischemic events. Age-related changes in the cerebrovascular system, such as alterations in vessel geometry and density, are explored for their contributions to age-related neurological disorders, including Alzheimer's disease and vascular dementia. Advances in medical imaging and computational methods have enabled automatic quantitative assessment of cerebrovascular structures, facilitating the identification of pathological changes in both acute and chronic cerebrovascular disorders. Emerging technologies, including machine learning and computational fluid dynamics, offer new tools for predicting disease risk and patient outcomes based on vascular morphology. This review underscores the importance of understanding cerebrovascular remodeling for early diagnosis and the development of novel therapeutic approaches in brain diseases.

This study aims to explore the application of ultrasound technology in triple-negative breast cancer (TNBC) using bibliometric methods. It presents a visual knowledge map to exhibit global research dynamics and elucidates the research directions, hotspots, trends, and frontiers in this field. The Web of Science Core Collection database was used, and CiteSpace and VOSviewer software were employed to visualize the annual publication volume, collaborative networks (including countries, institutions, and authors), citation characteristics (such as references, co-citations, and publications), as well as keywords (including emergence and clustering) related to ultrasound applications in TNBC over the past 15 years. A total of 310 papers were included. The first paper was published in 2010, and after that, publications in this field really took off, especially after 2020. China emerged as the leading country in terms of publication volume, while Shanghai Jiao Tong University had the highest output among institutions. Memorial Sloan Kettering Cancer Center was recognized as a key research institution within this domain. Adrada BE was the most prolific author in terms of publication count. Ko Es held the highest citation frequency among authors. Co-occurrence analysis of keywords revealed that the top three keywords by frequency were "triple-negative breast cancer," "breast cancer," and "sonography." The timeline visualization indicated a strong temporal continuity in the clusters of "breast cancer," "recommendations," "biopsy," "estrogen receptor," and "radiomics." The keyword with the highest emergence value was "neoplasms" (6.80). Trend analysis of emerging terms indicated a growing focus on "machine learning approaches," "prognosis," and "molecular subtypes," with "machine learning approach" emerging as a significant keyword currently. This study provided a systematic analysis of the current state of ultrasound technology applications in TNBC. It highlighted that "machine learning methods" have emerged as a central focus and frontier in this research area, both presently and for the foreseeable future. The findings offer valuable theoretical insights for the application of ultrasound technology in TNBC diagnosis and treatment and establish a solid foundation for further advancements in medical imaging research related to TNBC.

Vascular cognitive impairment (VCI) is an umbrella term for diseases associated with cognitive decline induced by substantive brain damage following pathological changes in the cerebrovascular system. The primary clinical manifestations include behavioral abnormalities and diminished learning and memory cognitive functions. If the location and extent of brain injury are not identified early and therapeutic interventions are not promptly administered, it may lead to irreversible cognitive impairment. Therefore, the early diagnosis of VCI is crucial for its prevention and treatment. Prior to the onset of cognitive impairment in VCI, magnetic resonance imaging (MRI) radiomics can be utilized for early assessment and diagnosis, thereby guiding clinicians in providing precise treatment for patients, which holds significant potential for development. This article reviews the classification of VCI, the concept of radiomics, the application of MRI radiomics in VCI, and the limitations of radiomics in the context of advancements in its application within the central nervous system. CRITICAL RELEVANCE STATEMENT: This article explores how MRI radiomics can be used to detect VCI early, enhancing clinical radiology practice by offering a reliable method for prediction, diagnosis, and identification, which also promotes standardization in research and integration of disciplines. KEY POINTS: MRI radiomics can predict VCI early. MRI radiomics can diagnose VCI. MRI radiomics distinguishes VCI from Alzheimer's disease.

Large-scale generative models have garnered significant attention in the field of medical imaging, particularly for image editing utilizing diffusion models. However, current research has predominantly concentrated on pathological editing involving single or a limited number of labels, making it challenging to achieve precise modifications. Inaccurate alterations may lead to substantial discrepancies between the generated and original images, thereby impacting the clinical applicability of these models. This paper presents a diffusion model with untangling capabilities applied to chest X-ray image editing, incorporating a mask-based mechanism for bone and organ information. We successfully perform multi-label pathological editing of chest X-ray images without compromising the integrity of the original thoracic structure. The proposed technology comprises a chest X-ray image classifier and an intricate organ mask; the classifier supplies essential feature labels that require untangling for the stabilized diffusion model, while the complex organ mask facilitates directed and controllable edits to chest X-rays. We assessed the outcomes of our proposed algorithm, named Chest X-rays_Mpe, using MS-SSIM and CLIP scores alongside qualitative evaluations conducted by radiology experts. The results indicate that our approach surpasses existing algorithms across both quantitative and qualitative metrics.

Segmenting abnormalities is a leading problem in medical imaging. Using machine learning for segmentation generally requires manually annotated segmentations, demanding extensive time and resources from radiologists. We propose a weakly supervised approach that utilizes binary image-level labels, which are much simpler to acquire, rather than manual annotations to segment brain tumors on magnetic resonance images. The proposed method generates healthy variants of cancerous images for use as priors when training the segmentation model. However, using weakly supervised segmentations for downstream tasks such as classification can be challenging due to occasional unreliable segmentations. To address this, we propose using the generated non-cancerous variants to identify the most effective segmentations without requiring ground truths. Our proposed method generates segmentations that achieve Dice coefficients of 79.27% on the Multimodal Brain Tumor Segmentation (BraTS) 2020 dataset and 73.58% on an internal dataset of pediatric low-grade glioma (pLGG), which increase to 88.69% and 80.29%, respectively, when removing suboptimal segmentations identified using the proposed method. Using the segmentations for tumor classification results with Area Under the Characteristic Operating Curve (AUC) of 93.54% and 83.74% on the BraTS and pLGG datasets, respectively. These are comparable to using manual annotations which achieve AUCs of 95.80% and 83.03% on the BraTS and pLGG datasets, respectively.

Brain tumor causes life-threatening consequences due to which its timely detection and accurate classification are critical for determining appropriate treatment plans while focusing on the improved patient outcomes. However, conventional approaches of brain tumor diagnosis, such as Magnetic Resonance Imaging (MRI) and Computed Tomography (CT) scans, are often labor-intensive, prone to human error, and completely reliable on expertise of radiologists.Thus, the integration of advanced techniques such as Machine Learning (ML) and Deep Learning (DL) has brought revolution in the healthcare sector due to their supporting features or properties having ability to analyze medical images in recent years, demonstrating great potential for achieving accurate and improved outcomes but also resulted in a few drawbacks due to their black-box nature. As understanding reasoning behind their predictions is still a great challenge for the healthcare professionals and raised a great concern about their trustworthiness, interpretability and transparency in clinical settings. Thus, an advanced algorithm of explainable artificial intelligence (XAI) has been synergized with hybrid model comprising of DenseNet201 network for extracting the most important features based on the input Magnetic resonance imaging (MRI) data following supervised algorithm, support vector machine (SVM) to distinguish distinct types of brain scans. To overcome this, an explainable hybrid framework has been proposed that integrates DenseNet201 for deep feature extraction with a Support Vector Machine (SVM) classifier for robust binary classification. A region-adaptive preprocessing pipeline is used to enhance tumor visibility and feature clarity. To address the need for interpretability, multiple XAI techniques-Grad-CAM, Integrated Gradients (IG), and Layer-wise Relevance Propagation (LRP) have been incorporated. Our comparative evaluation shows that LRP achieves the highest performance across all explainability metrics, with 98.64% accuracy, 0.74 F1-score, and 0.78 IoU. The proposed model provides transparent and highly accurate diagnostic predictions, offering a reliable clinical decision support tool. It achieves 0.9801 accuracy, 0.9223 sensitivity, 0.9909 specificity, 0.9154 precision, and 0.9360 F1-score, demonstrating strong potential for real-world brain tumor diagnosis and personalized treatment strategies.

Radiographic imaging is a non-invasive technique of considerable importance for evaluating tumor treatment response. However, redundancy in CT data and the lack of labeled data make it challenging to accurately assess the response of locally advanced rectal cancer (LARC) patients to neoadjuvant chemoradiotherapy (nCRT) using current imaging indicators. In this study, we propose a novel learning framework to automatically predict the response of LARC patients to nCRT. Specifically, we develop a deep learning network called the Expand Intensive Attention Network (EIA-Net), which enhances the network's feature extraction capability through cascaded 3D convolutions and coordinate attention. Instance-oriented collaborative self-supervised learning (IOC-SSL) is proposed to leverage unlabeled data for training, reducing the reliance on labeled data. In a dataset consisting of 1,575 volumes, the proposed method achieves an AUC score of 0.8562. The dataset includes two distinct parts: the self-supervised dataset containing 1,394 volumes and the supervised dataset comprising 195 volumes. Analysis of the lifetime predictions reveals that patients with pathological complete response (pCR) predicted by EIA-Net exhibit better overall survival (OS) compared to non-pCR patients with LARC. The retrospective study demonstrates that imaging-based pCR prediction for patients with low rectal cancer can assist clinicians in making informed decisions regarding the need for Miles operation, thereby improving the likelihood of anal preservation, with an AUC of 0.8222. These results underscore the potential of our method to enhance clinical decision-making, offering a promising tool for personalized treatment and improved patient outcomes in LARC management.

Brain tumors are a significant contributor to cancer-related deaths worldwide. Accurate and prompt detection is crucial to reduce mortality rates and improve patient survival prospects. Magnetic Resonance Imaging (MRI) is crucial for diagnosis, but manual analysis is resource-intensive and error-prone, highlighting the need for robust Computer-Aided Diagnosis (CAD) systems. This paper proposes a novel hybrid model combining Transfer Learning (TL) and attention mechanisms to enhance brain tumor classification accuracy. Leveraging features from the pre-trained DenseNet201 Convolutional Neural Networks (CNN) model and integrating a Transformer-based architecture, our approach overcomes challenges like computational intensity, detail detection, and noise sensitivity. We also evaluated five additional pre-trained models-VGG19, InceptionV3, Xception, MobileNetV2, and ResNet50V2 and incorporated Multi-Head Self-Attention (MHSA) and Squeeze-and-Excitation Attention (SEA) blocks individually to improve feature representation. Using the Br35H dataset of 3,000 MRI images, our proposed DenseTransformer model achieved a consistent accuracy of 99.41%, demonstrating its reliability as a diagnostic tool. Statistical analysis using Z-test based on Cohen's Kappa Score, DeLong's test based on AUC Score and McNemar's test based on F1-score confirms the model's reliability. Additionally, Explainable AI (XAI) techniques like Gradient-weighted Class Activation Mapping (Grad-CAM) and Local Interpretable Model-agnostic Explanations (LIME) enhanced model transparency and interpretability. This study underscores the potential of hybrid Deep Learning (DL) models in advancing brain tumor diagnosis and improving patient outcomes.