Considerable challenges exist in managing lung cancer and mesothelioma, including diagnostic complexity, treatment stratification, early detection and imaging quantification. Variable incidence in mesothelioma also makes equitable provision of high-quality care difficult. In this context, artificial intelligence (AI) offers a range of assistive/automated functions that can potentially enhance clinical decision-making, while reducing inequality and pathway delay. In this state-of-the-art narrative review, we synthesise evidence on this topic, focusing particularly on tools that ingest routine pathology and radiology images. We summarise the strengths and weaknesses of AI applied to common multidisciplinary team (MDT) functions, including histological diagnosis, therapeutic response prediction, radiological detection and quantification, and survival estimation. We also review emerging methods capable of generating novel biological insights and current barriers to implementation, including access to high-quality training data and suitable regulatory and technical infrastructure. Neural networks trained on pathology images have proven utility in histological classification, prognostication, response prediction and survival. Self-supervised models can also generate new insights into biological features responsible for adverse outcomes. Radiology applications include lung nodule tools, which offer critical pathway support for imminent lung cancer screening and urgent referrals. Tumour segmentation AI offers particular advantages in mesothelioma, where response assessment and volumetric staging are difficult using human readers due to tumour size and morphological complexity. AI is also critical for radiogenomics, permitting effective integration of molecular and radiomic features for discovery of non-invasive markers for molecular subtyping and enhanced stratification. AI solutions offer considerable potential benefits across the MDT, particularly in repetitive or time-consuming tasks based on pathology and radiology images. Effective leveraging of this technology is critical for lung cancer screening and efficient delivery of increasingly complex diagnostic and predictive MDT functions. Future AI research should involve transparent and interpretable outputs that assist in explaining the basis of AI-supported decision making.

This study aims to explore research hotspots and development trends in molecular imaging of glioma from 2014 to 2024. A total of 2957 publications indexed in the web of science core collection (WoSCC) were analyzed using bibliometric techniques. To visualize the research landscape, co-citation clustering, keyword analysis, and technological trend mapping were performed using CiteSpace and Excel. Publication output peaked in 2021. Emerging research trends included the integration of radiomics and artificial intelligence and the application of novel imaging modalities such as positron emission tomography and magnetic resonance spectroscopy. Significant progress was observed in blood-brain barrier disruption techniques and the development of molecular probes, especially those targeting IDH and MGMT mutations. Molecular imaging has been pivotal in advancing glioma research, contributing to improved diagnostic accuracy and personalized treatment strategies. However, challenges such as clinical translation and standardization remain. Future studies should focus on integrating advanced technologies into routine clinical practice to enhance patient care.

This review explores the transformative role of artificial intelligence (AI) in the early detection and prognosis prediction of diabetic retinopathy (DR), a leading cause of vision loss in diabetic patients. AI, particularly deep learning and convolutional neural networks (CNNs), has demonstrated remarkable accuracy in analyzing retinal images, identifying early-stage DR with high sensitivity and specificity. These advancements address critical challenges such as intergrader variability in manual screening and the limited availability of specialists, especially in underserved regions. The integration of AI with telemedicine has further enhanced accessibility, enabling remote screening through portable devices and smartphone-based imaging. Economically, AI-based systems reduce healthcare costs by optimizing resource allocation and minimizing unnecessary referrals. Key findings highlight the dominance of Medicine (819 documents) and Computer Science (613 documents) in research output, reflecting the interdisciplinary nature of this field. Geographically, China, the United States, and India lead in contributions, underscoring global efforts to combat DR. Despite these successes, challenges such as algorithmic bias, data privacy, and the need for explainable AI (XAI) remain. Future research should focus on multi-center validation, diverse AI methodologies, and clinician-friendly tools to ensure equitable adoption. By addressing these gaps, AI can revolutionize DR management, reducing the global burden of diabetes-related blindness through early intervention and scalable solutions.

The anterior cruciate ligament (ACL) is a crucial stabilizer of the knee joint, and its injury risk and surgical outcomes are closely linked to femoral and tibial anatomy. This review focuses on current evidence on how skeletal parameters, such as femoral intercondylar notch morphology, tibial slope, and insertion site variations-influence ACL biomechanics. A narrowed or concave femoral notch raises the risk of impingement, while a higher posterior tibial slope makes anterior tibial translation worse, which increases ACL strain. Gender disparities exist, with females exhibiting smaller notch dimensions, and hormonal fluctuations may contribute to ligament laxity. Anatomical changes that come with getting older make clinical management even harder. Adolescent patients have problems with epiphyseal growth, and older patients have to deal with degenerative notch narrowing and lower bone density. Preoperative imaging (MRI, CT, and 3D reconstruction) enables precise assessment of anatomical variations, guiding individualized surgical strategies. Optimal femoral and tibial tunnel placement during reconstruction is vital to replicate native ACL biomechanics and avoid graft failure. Emerging technologies, including AI-driven segmentation and deep learning models, enhance risk prediction and intraoperative precision. Furthermore, synergistic factors, such as meniscal integrity and posterior oblique ligament anatomy, need to be integrated into comprehensive evaluations. Future directions emphasize personalized approaches, combining advanced imaging, neuromuscular training, and artificial intelligence to optimize prevention, diagnosis, and rehabilitation. Addressing age-specific challenges, such as growth plate preservation in pediatric cases and osteoarthritis management in the elderly, will improve long-term outcomes. Ultimately, a nuanced understanding of skeletal anatomy and technological integration holds promise for reducing ACL reinjury rates and enhancing patient recovery.

The skull has long been recognized and utilized in forensic investigations, evolving from basic to complex analyses with modern technologies. Advances in radiology and technology have enhanced the ability to analyze biological identifiers-sex, stature, and age at death-from the skull. The use of computed tomography imaging helps practitioners to improve the accuracy and reliability of forensic analyses. Recently, artificial intelligence has increasingly been applied in digital forensic investigations to estimate sex, stature, and age from computed tomography images. The integration of artificial intelligence represents a significant shift in multidisciplinary collaboration, offering the potential for more accurate and reliable identification, along with advancements in academia. However, it is not yet fully developed for routine forensic work, as it remains largely in the research and development phase. Additionally, the limitations of artificial intelligence systems, such as the lack of transparency in algorithms, accountability for errors, and the potential for discrimination, must still be carefully considered. Based on scientific publications from the past decade, this article aims to provide an overview of the application of computed tomography imaging in estimating sex, stature, and age from the skull and to address issues related to future directions to further improvement.

Lung cancer remains the leading cause of cancer-related mortality worldwide, due to lacking effective early-stage screening approaches. Imaging, such as low-dose CT, poses radiation risk, and biopsies can induce some complications. Additionally, traditional serum tumor markers lack diagnostic specificity. This highlights the urgent need for precise and non-invasive early detection techniques. This systematic review aims to evaluate the limitations of conventional screening methods (imaging/biopsy/tumor markers), seek breakthroughs in liquid biopsy for early lung cancer detection, and assess the potential value of Artificial Intelligence (AI), thereby providing evidence-based insights for establishing an optimal screening framework. We systematically searched the PubMed database for the literature published up to May 2025. Key words include "Artificial Intelligence", "Early Lung cancer screening", "Imaging examination", "Innovative technologies", "Liquid biopsy", and "Puncture biopsy". Our inclusion criteria focused on studies about traditional and innovative screening methods, with an emphasis on original research concerning diagnostic performance or high-quality reviews. This approach helps identify critical studies in early lung cancer screening. Novel liquid biopsy techniques are non-invasive and have superior diagnostic efficacy. AI-assisted diagnostics further enhance accuracy. We propose three development directions: establishing risk-based liquid biopsy screening protocols, developing a stepwise "imaging-AI-liquid biopsy" diagnostic workflow, and creating standardized biomarker panel testing solutions. Integrating traditional methodologies, novel liquid biopsies, and AI to establish a comprehensive early lung cancer screening model is important. These innovative strategies aim to significantly increase early detection rates, substantially enhancing lung cancer control. This review provides both theoretical guidance for clinical practice and future research.

Peripheral Nerves (PNs) are traditionally evaluated using US or MRI, allowing radiologists to identify and classify them as normal or pathological based on imaging findings, symptoms, and electrophysiological tests. However, the anatomical complexity of PNs, coupled with their proximity to surrounding structures like vessels and muscles, presents significant challenges. Advanced imaging techniques, including MR-neurography and Diffusion-Weighted Imaging (DWI) neurography, have shown promise but are hindered by steep learning curves, operator dependency, and limited accessibility. Discrepancies between imaging findings and patient symptoms further complicate the evaluation of PNs, particularly in cases where imaging appears normal despite clinical indications of pathology. Additionally, demographic and clinical factors such as age, sex, comorbidities, and physical activity influence PN health but remain unquantifiable with current imaging methods. Artificial Intelligence (AI) solutions have emerged as a transformative tool in PN evaluation. AI-based algorithms offer the potential to transition from qualitative to quantitative assessments, enabling precise segmentation, characterization, and threshold determination to distinguish healthy from pathological nerves. These advances could improve diagnostic accuracy and treatment monitoring. This review highlights the latest advances in AI applications for PN imaging, discussing their potential to overcome the current limitations and opportunities to improve their integration into routine radiological practice.

Cone beam CT is an imaging modality that provides high-resolution, cross-sectional imaging in the fluoroscopy suite. In neuroradiology, cone beam CT has been used for various applications including temporal bone imaging and during spinal and cerebral angiography. Furthermore, cone beam CT has been shown to improve imaging of spinal CSF leaks during myelography. One drawback of cone beam CT is that images have a relatively high noise level. In this technical report, we describe the first application of a high-resolution convolutional neural network to denoise cone beam CT myelographic images. We show examples of the resulting improvement in image quality for a variety of types of spinal CSF leaks. Further application of this technique is warranted to demonstrate its clinical utility and potential use for other cone beam CT applications.ABBREVIATIONS: CBCT = cone beam CT; CB-CTM = cone beam CT myelography; CTA = CT angiography; CVF = CSF-venous fistula; DSM = digital subtraction myelography; EID = energy integrating detector; FBP = filtered back-projection; SNR = signal-to-noise ratio.

The second-trimester ultrasound is a crucial tool in prenatal care, typically conducted between 18 and 24 weeks of gestation to evaluate fetal anatomy, growth, and mid-trimester screening. This article provides a comprehensive overview of the best practices and guidelines for performing this examination, with a focus on detecting fetal anomalies. The ultrasound assesses key structures and evaluates fetal growth by measuring biometric parameters, which are essential for estimating fetal weight. Additionally, the article discusses the importance of placental evaluation, amniotic fluid levels measurement, and the risk of preterm birth through cervical length measurements. Factors that can affect the accuracy of the scan, such as the skill of the operator, the quality of the equipment, and maternal conditions such as obesity, are discussed. The article also addresses the limitations of the procedure, including variability in detection. Despite these challenges, the second-trimester ultrasound remains a valuable screening and diagnostic tool, providing essential information for managing pregnancies, especially in high-risk cases. Future directions include improving imaging technology, integrating artificial intelligence for anomaly detection, and standardizing ultrasound protocols to enhance diagnostic accuracy and ensure consistent prenatal care.

This comprehensive review examines the current state and evolution of artificial intelligence applications in colorectal cancer detection through medical imaging from 2019 to 2025. The study presents a quantitative analysis of 110 high-quality publications and 9 publicly accessible medical image datasets used for training and validation. Various convolutional neural network architectures-including ResNet (40 implementations), VGG (18 implementations), and emerging transformer-based models (12 implementations)-for classification, object detection, and segmentation tasks are systematically categorized and evaluated. The investigation encompasses hyperparameter optimization techniques utilized to enhance model performance, with particular focus on genetic algorithms and particle swarm optimization approaches. The role of explainable AI methods in medical diagnosis interpretation is analyzed through visualization techniques such as Grad-CAM and SHAP. Technical limitations, including dataset scarcity, computational constraints, and standardization challenges, are identified through trend analysis. Research gaps in current methodologies are highlighted through comparative assessment of performance metrics across different architectural implementations. Potential future research directions, including multimodal learning and federated learning approaches, are proposed based on publication trend analysis. This review serves as a comprehensive reference for researchers in medical image analysis and clinical practitioners implementing AI-based colorectal cancer detection systems.