Brain Tumours are highly complex, particularly when it comes to their initial and accurate diagnosis, as this determines patient prognosis. Conventional methods rely on MRI and CT scans and employ generic machine learning techniques, which are heavily dependent on feature extraction and require human intervention. These methods may fail in complex cases and do not produce human-interpretable results, making it difficult for clinicians to trust the model's predictions. Such limitations prolong the diagnostic process and can negatively impact the quality of treatment. The advent of deep learning has made it a powerful tool for complex image analysis tasks, such as detecting brain Tumours, by learning advanced patterns from images. However, deep learning models are often considered "black box" systems, where the reasoning behind predictions remains unclear. To address this issue, the present study applies Explainable AI (XAI) alongside deep learning for accurate and interpretable brain Tumour prediction. XAI enhances model interpretability by identifying key features such as Tumour size, location, and texture, which are crucial for clinicians. This helps build their confidence in the model and enables them to make better-informed decisions. In this research, a deep learning model integrated with XAI is proposed to develop an interpretable framework for brain Tumour prediction. The model is trained on an extensive dataset comprising imaging and clinical data and demonstrates high AUC while leveraging XAI for model explainability and feature selection. The study findings indicate that this approach improves predictive performance, achieving an accuracy of 92.98% and a miss rate of 7.02%. Additionally, interpretability tools such as LIME and Grad-CAM provide clinicians with a clearer understanding of the decision-making process, supporting diagnosis and treatment. This model represents a significant advancement in brain Tumour prediction, with the potential to enhance patient outcomes and contribute to the field of neuro-oncology.

Natural language processing (NLP) is crucial to extract information accurately from unstructured text to provide insights for clinical decision-making, quality improvement, and medical research. This study compared the performance of a rule-based NLP system and a medical-domain transformer-based model to detect negated concepts in radiology reports. Using a corpus of 984 de-identified radiology reports from a large U.S.-based academic health system (1000 consecutive reports, excluding 16 duplicates), the investigators compared the rule-based medspaCy system and the Clinical Assertion and Negation Classification Bidirectional Encoder Representations from Transformers (CAN-BERT) system to detect negated expressions of terms from RadLex, the Unified Medical Language System Metathesaurus, and the Radiology Gamuts Ontology. Power analysis determined a sample size of 382 terms to achieve α = 0.05 and β = 0.8 for McNemar's test; based on an estimate of 15% negated terms, 2800 randomly selected terms were annotated manually as negated or not negated. Precision, recall, and F1 of the two models were compared using McNemar's test. Of the 2800 terms, 387 (13.8%) were negated. For negation detection, medspaCy attained a recall of 0.795, precision of 0.356, and F1 of 0.492. CAN-BERT achieved a recall of 0.785, precision of 0.768, and F1 of 0.777. Although recall was not significantly different, CAN-BERT had significantly better precision (χ2 = 304.64; p < 0.001). The transformer-based CAN-BERT model detected negated terms in radiology reports with high precision and recall; its precision significantly exceeded that of the rule-based medspaCy system. Use of this system will improve data extraction from textual reports to support information retrieval, AI model training, and discovery of causal relationships.

Recent advancements in neuroimaging and machine learning have significantly improved our ability to diagnose and categorize isocitrate dehydrogenase (IDH)-wildtype glioblastoma, a disease characterized by notable tumoral heterogeneity, which is crucial for effective treatment. Neuroimaging techniques, such as diffusion tensor imaging and magnetic resonance radiomics, provide noninvasive insights into tumor infiltration patterns and metabolic profiles, aiding in accurate diagnosis and prognostication. Machine learning algorithms further enhance glioblastoma characterization by identifying distinct imaging patterns and features, facilitating precise diagnoses and treatment planning. Integration of these technologies allows for the development of image-based biomarkers, potentially reducing the need for invasive biopsy procedures and enabling personalized therapy targeting specific pro-tumoral signaling pathways and resistance mechanisms. Although significant progress has been made, ongoing innovation is essential to address remaining challenges and further improve these methodologies. Future directions should focus on refining machine learning models, integrating emerging imaging techniques, and elucidating the complex interplay between imaging features and underlying molecular processes. This review highlights the pivotal role of neuroimaging and machine learning in glioblastoma research, offering invaluable noninvasive tools for diagnosis, prognosis prediction, and treatment planning, ultimately improving patient outcomes. These advances in the field promise to usher in a new era in the understanding and classification of IDH-wildtype glioblastoma.

Osteoporosis, a prevalent skeletal disorder, necessitates accurate and accessible diagnostic tools for effective disease management and fracture prevention. While dual-energy X-ray absorptiometry (DXA) remains the clinical standard for bone mineral density (BMD) assessment, its limitations, including ionizing radiation exposure and susceptibility to artifacts, underscore the need for alternative technologies. Ultrasound-based methods have emerged as promising radiation-free alternatives, with radiofrequency echographic multi-spectrometry (REMS) representing a significant advancement in axial skeleton assessment, specifically at the lumbar spine and proximal femur. REMS analyzes unfiltered radiofrequency ultrasound signals, providing not only BMD estimates but also a novel fragility score (FS), which reflects bone quality and microarchitectural integrity. This review critically evaluates the underlying principles, diagnostic performance, and clinical applications of REMS. It compares REMS with DXA, quantitative computed tomography (QCT), and trabecular bone score (TBS), highlighting REMS's potential advantages in artifact-prone scenarios and specific populations, including children and patients with secondary osteoporosis. The clinical utility of REMS in fracture risk prediction and therapy monitoring is explored alongside its operational precision, cost-effectiveness, and portability. In addition, the integration of artificial intelligence (AI) within REMS software has enhanced its capacity for artifact exclusion and automated spectral interpretation, improving usability and reproducibility. Current limitations, such as the need for broader validation and guideline inclusion, are identified, and future research directions are proposed. These include multicenter validation studies, development of pediatric and secondary osteoporosis reference models, and deeper evaluation of AI-driven enhancements. REMS offers a compelling, non-ionizing alternative for axial bone health assessment and may significantly advance the diagnostic landscape for osteoporosis care.

Classical biomedical data science models are trained on a single modality and aimed at one specific task. However, the exponential increase in the size and capabilities of the foundation models inside and outside medicine shows a shift toward task-agnostic models using large-scale, often internet-based, data. Recent research into smaller foundation models trained on specific literature, such as programming textbooks, demonstrated that they can display capabilities similar to or superior to large generalist models, suggesting a potential middle ground between small task-specific and large foundation models. This study attempts to introduce a domain-specific multimodal model, Congress of Neurological Surgeons (CNS)-Contrastive Language-Image Pretraining (CLIP), developed for neurosurgical applications, leveraging data exclusively from Neurosurgery Publications. We constructed a multimodal data set of articles from Neurosurgery Publications through PDF data collection and figure-caption extraction using an artificial intelligence pipeline for quality control. Our final data set included 24 021 figure-caption pairs. We then developed a fine-tuning protocol for the OpenAI CLIP model. The model was evaluated on tasks including neurosurgical information retrieval, computed tomography imaging classification, and zero-shot ImageNet classification. CNS-CLIP demonstrated superior performance in neurosurgical information retrieval with a Top-1 accuracy of 24.56%, compared with 8.61% for the baseline. The average area under receiver operating characteristic across 6 neuroradiology tasks achieved by CNS-CLIP was 0.95, slightly superior to OpenAI's Contrastive Language-Image Pretraining at 0.94 and significantly outperforming a vanilla vision transformer at 0.62. In generalist classification, CNS-CLIP reached a Top-1 accuracy of 47.55%, a decrease from the baseline of 52.37%, demonstrating a catastrophic forgetting phenomenon. This study presents a pioneering effort in building a domain-specific multimodal model using data from a medical society publication. The results indicate that domain-specific models, while less globally versatile, can offer advantages in specialized contexts. This emphasizes the importance of using tailored data and domain-focused development in training foundation models in neurosurgery and general medicine.

Diagnostic imaging is an integral part of identifying spondyloarthropathies (SpA), yet the interpretation of these images can be challenging. This review evaluated the use of deep learning models to enhance the diagnostic accuracy of SpA imaging. Following PRISMA guidelines, we systematically searched major databases up to February 2024, focusing on studies that applied deep learning to SpA imaging. Performance metrics, model types, and diagnostic tasks were extracted and analyzed. Study quality was assessed using QUADAS-2. We analyzed 21 studies employing deep learning in SpA imaging diagnosis across MRI, CT, and X-ray modalities. These models, particularly advanced CNNs and U-Nets, demonstrated high accuracy in diagnosing SpA, differentiating arthritis forms, and assessing disease progression. Performance metrics frequently surpassed traditional methods, with some models achieving AUCs up to 0.98 and matching expert radiologist performance. This systematic review underscores the effectiveness of deep learning in SpA imaging diagnostics across MRI, CT, and X-ray modalities. The studies reviewed demonstrated high diagnostic accuracy. However, the presence of small sample sizes in some studies highlights the need for more extensive datasets and further prospective and external validation to enhance the generalizability of these AI models. Question How can deep learning models improve diagnostic accuracy in imaging for spondyloarthropathies (SpA), addressing challenges in early detection and differentiation from other forms of arthritis? Findings Deep learning models, especially CNNs and U-Nets, showed high accuracy in SpA imaging across MRI, CT, and X-ray, often matching or surpassing expert radiologists. Clinical relevance Deep learning models can enhance diagnostic precision in SpA imaging, potentially reducing diagnostic delays and improving treatment decisions, but further validation on larger datasets is required for clinical integration.

Diagnosis of lesions of the parapharyngeal space (PPS) often poses a diagnostic and therapeutic challenge due to its deep location. As a result of the topographical relationship to nearby neck spaces, a very precise differential diagnosis is possible based on imaging criteria. When in doubt, imaging-guided - usually CT-guided - biopsy and even drainage remain options.Through a precise analysis of the literature including the most recent publications, this review precisely describes the basic and most recent imaging applications for various PPS pathologies and the differential diagnostic scheme for assigning the respective lesions in addition to the possibilities of using interventional radiology.The different pathologies of PPS from congenital malformations and inflammation to tumors are discussed according to frequency. Characteristic criteria and, more recently, the use of advanced imaging procedures and the introduction of artificial intelligence (AI) allow a very precise differential diagnosis and support further diagnosis and therapy. After precise access planning, almost all pathologies of the PPS can be biopsied or, if necessary, drained using CT-assisted procedures.Radiological procedures play an important role in the diagnosis and treatment planning of PPS pathologies. · Lesions of the PPS account for about 1-2% of all pathologies of the head and neck region. The majority are benign lesions and inflammatory processes.. · If differential diagnostic questions remain unanswered, material can - if necessary - be obtained via a CT-guided biopsy. Exclusion criteria are hypervascularized processes, especially paragangliomas and angiomas.. · The use of artificial intelligence (AI) in head and neck imaging of various pathologies, such as tumor segmentation, pathological TMN classification, detection of lymph node metastases, and extranodal extension, has significantly increased in recent years.. · Vogl TJ, Burck I, Stöver T et al. Parapharyngeal Space: Diagnostic Imaging and Intervention. Rofo 2025; 197: 638-646.

Computed tomography coronary angiography provides a non-invasive evaluation of coronary artery disease that includes phenotyping of atherosclerotic plaques and the surrounding perivascular adipose tissue (PVAT). Image analysis techniques have been developed to quantify atherosclerotic plaque burden and morphology as well as the associated PVAT attenuation, and emerging radiomic approaches can add further contextual information. PVAT attenuation might provide a novel measure of vascular health that could be indicative of the pathogenetic processes implicated in atherosclerosis such as inflammation, fibrosis or increased vascularity. Bidirectional signalling between the coronary artery and adjacent PVAT has been hypothesized to contribute to coronary artery disease progression and provide a potential novel measure of the risk of future cardiovascular events. However, despite the development of more advanced radiomic and artificial intelligence-based algorithms, studies involving large datasets suggest that the measurement of PVAT attenuation contributes only modest additional predictive discrimination to standard cardiovascular risk scores. In this Review, we explore the pathobiology of coronary atherosclerotic plaques and PVAT, describe their phenotyping with computed tomography coronary angiography, and discuss potential future applications in clinical risk prediction and patient management.

Breast cancer continues to be a major health concern, and early detection is vital for enhancing survival rates. Magnetic resonance imaging (MRI) is a key tool due to its substantial sensitivity for invasive breast cancers. Computer-aided detection (CADe) systems enhance the effectiveness of MRI by identifying potential lesions, aiding radiologists in focusing on areas of interest, extracting quantitative features, and integrating with computer-aided diagnosis (CADx) pipelines. This review aims to provide a comprehensive overview of the current state of CADe systems in breast MRI, focusing on the technical details of pipelines and segmentation models including classical intensity-based methods, supervised and unsupervised machine learning (ML) approaches, and the latest deep learning (DL) architectures. It highlights recent advancements from traditional algorithms to sophisticated DL models such as U-Nets, emphasizing CADe implementation of multi-parametric MRI acquisitions. Despite these advancements, CADe systems face challenges like variable false-positive and negative rates, complexity in interpreting extensive imaging data, variability in system performance, and lack of large-scale studies and multicentric models, limiting the generalizability and suitability for clinical implementation. Technical issues, including image artefacts and the need for reproducible and explainable detection algorithms, remain significant hurdles. Future directions emphasize developing more robust and generalizable algorithms, integrating explainable AI to improve transparency and trust among clinicians, developing multi-purpose AI systems, and incorporating large language models to enhance diagnostic reporting and patient management. Additionally, efforts to standardize and streamline MRI protocols aim to increase accessibility and reduce costs, optimizing the use of CADe systems in clinical practice. LEVEL OF EVIDENCE: NA TECHNICAL EFFICACY: Stage 2.

The potential of Large Language Models (LLMs) in enhancing a variety of natural language tasks in clinical fields includes medical imaging reporting. This pilot study examines the efficacy of a retrieval-augmented generation (RAG) LLM system considering zero-shot learning capability of LLMs, integrated with a comprehensive database of PET reading reports, in improving reference to prior reports and decision making. We developed a custom LLM framework with retrieval capabilities, leveraging a database of over 10 years of PET imaging reports from a single center. The system uses vector space embedding to facilitate similarity-based retrieval. Queries prompt the system to generate context-based answers and identify similar cases or differential diagnoses. From routine clinical PET readings, experienced nuclear medicine physicians evaluated the performance of system in terms of the relevance of queried similar cases and the appropriateness score of suggested potential diagnoses. The system efficiently organized embedded vectors from PET reports, showing that imaging reports were accurately clustered within the embedded vector space according to the diagnosis or PET study type. Based on this system, a proof-of-concept chatbot was developed and showed the framework's potential in referencing reports of previous similar cases and identifying exemplary cases for various purposes. From routine clinical PET readings, 84.1% of the cases retrieved relevant similar cases, as agreed upon by all three readers. Using the RAG system, the appropriateness score of the suggested potential diagnoses was significantly better than that of the LLM without RAG. Additionally, it demonstrated the capability to offer differential diagnoses, leveraging the vast database to enhance the completeness and precision of generated reports. The integration of RAG LLM with a large database of PET imaging reports suggests the potential to support clinical practice of nuclear medicine imaging reading by various tasks of AI including finding similar cases and deriving potential diagnoses from them. This study underscores the potential of advanced AI tools in transforming medical imaging reporting practices.