Search Results (650)

Circulating tumor DNA (ctDNA) has gained increasing attention as a non-invasive biomarker with potential utility across multiple stages of melanoma. ctDNA reflects tumor-derived genetic alterations in real time and has shown value in detecting minimal residual disease, identifying early recurrence, estimating tumor burden, and monitoring response to systemic therapies. In early-stage melanoma, postoperative ctDNA positivity is strongly associated with higher recurrence risk and often precedes radiologic detection. In advanced disease, ctDNA correlates with tumor volume and can distinguish responders from non-responders during targeted therapy and immunotherapy, while also identifying emerging resistance mechanisms. Despite these advantages, clinical implementation remains limited by low shedding in early-stage disease, variation among detection platforms, and the absence of standardized clinical thresholds. Recent advances, including fragmentomics, methylation assays, and multi-target sequencing strategies, aim to improve sensitivity, particularly in low-tumor-burden settings. Integration of ctDNA with radiomics, artificial intelligence, and digital pathology represents an additional opportunity to enhance precision in risk stratification and treatment adaptation. This review summarizes current evidence on ctDNA biology, detection methods, and clinical applications in melanoma and outlines ongoing challenges and future directions required for translation into routine practice. Full article

The convergence of Artificial Intelligence and the Internet of Things has given rise to the Artificial Intelligence of Things (AIoT), which enables connected systems to operate with greater autonomy, adaptability, and contextual awareness. In agriculture, this evolution supports precision farming, improves resource allocation, and strengthens climate resilience by enhancing the capacity of farming systems to anticipate, absorb, and recover from environmental shocks. This review provides a structured synthesis of the transition from IoT-based monitoring to AIoT-driven intelligent agriculture and examines key applications such as smart irrigation, pest and disease detection, soil and crop health assessment, yield prediction, and livestock management. To ensure methodological rigor and transparency, this study follows the PRISMA 2020 guidelines for systematic literature reviews. A comprehensive search and multi-stage screening procedure was conducted across major scholarly repositories, resulting in a curated selection of studies published between 2018 and 2025. These sources were analyzed thematically to identify technological enablers, implementation barriers, and contextual factors affecting adoption particularly within low-income countries where infrastructural constraints, limited digital capacity, and economic disparities shape AIoT deployment. Building on these insights, the article proposes an AIoT architecture tailored to resource-constrained agricultural environments. The architecture integrates sensing technologies, connectivity layers, edge intelligence, data processing pipelines, and decision-support mechanisms, and is supported by governance, data stewardship, and capacity-building frameworks. By combining systematic evidence with conceptual analysis, this review offers a comprehensive perspective on the transformative potential of AIoT in advancing sustainable, inclusive, and intelligent food production systems. Full article

Background/Objectives: Melanoma remains one of the most malignant types of skin cancer with rising incidence numbers, despite the progress made in the prevention and management of the disease. Recent technological advancements, such as developments in the field of molecular biology, imaging, and artificial intelligence (AI), have led to a paradigm shift in the diagnosis, assessment, and management of melanoma. The current review aims to integrate current research on melanoma, moving beyond the boundaries of conventional histological analysis. Methods: This is a critical appraisal narrative review that focuses on recent studies in the areas of translation research and digital health with regard to melanoma. This research particularly targeted recent studies within the last five years, with landmark studies implicated when appropriate. Evidence was synthesized within the major categories that include epidemiology, early diagnosis, histopathology, predictive biomarkers, genetic/epigenetic changes, AI-assisted diagnostic platforms, and novel therapeutic platforms & targets. Results: Early detection techniques, innovative imaging, and biomarker-guided risk adjustment can improve diagnostic accuracy and prognostic stratification. The potential of AI in dermoscopy, digital pathology, and decision analytical systems is evident, although validation, bias, and integration issues need to be addressed. Advances in immunotherapy, targeted therapies, and novel molecular/immunological targets are expanding and facilitating integrated and personalized management. Conclusions: There is a trend in melanoma research to shift towards an integrated diagnostic platform that involves the use of AI, molecular characterization, and clinical inputs to enable more accurate and personalized diagnoses. To realize this potential, there is a need to validate, collaborate, and address ethics and implementation. Full article

Osteoarthritis (OA) is a prevalent joint disorder characterized by symptoms such as pain and stiffness, often leading to loss of function and disability. Knee osteoarthritis (KOA) represents the most prevalent type of osteoarthritis. KOA is usually detected using X-ray radiographs of the knee; however, the classification of disease severity remains subjective and varies among clinicians, motivating the need for automated assessment methods. In recent years, deep learning–based approaches have shown promising performance for KOA classification tasks, particularly when applied to structured imaging datasets. This review analyzes convolution neural network (CNN)-based approaches reported in the literature and compares their performance across multiple criteria. Studies were identified through systematic searches of IEEE Xplore, SpringerLink, Elsevier (ScienceDirect), Wiley Online Library, ACM Digital Library, and other sources such as PubMed and arXiv, with the last search conducted in March 2025. The review examines datasets used (primarily X-ray and MRI), preprocessing strategies, segmentation techniques, and deep learning architectures. Reported classification accuracies range from 61% to 98%, depending on the dataset, imaging modality, and task formulation. Finally, this paper highlights key methodological limitations in existing studies and outlines future research directions to improve the robustness and clinical applicability of deep learning–based KOA classification systems. Full article

The incorporation of artificial intelligence (AI) and digital technology in healthcare has revolutionized service delivery, improving diagnostic precision, patient outcomes, and operational efficacy. Nonetheless, despite considerable progress, numerous problems persist that impede the realization of full potential. Current reviews predominantly emphasize the advantages of AI in disease detection and health guidance, neglecting significant concerns such as social opposition, regulatory frameworks, and geographical discrepancies. This SLR, executed in accordance with PRISMA principles, examined 21 publications from 2020 to 2025 to assess the present condition of AI and digital technologies inside Saudi Arabia’s healthcare industry. Initially, 863 publications were obtained, from which 21 were chosen for comprehensive examination. Significant discoveries encompass the extensive utilization of telemedicine, data analytics, mobile health applications, Internet of Things, electronic health records, blockchain technology, online platforms, cloud computing, and encryption methods. These technologies augment diagnostic precision, boost patient outcomes, optimize administrative procedures, and foster preventative medicine, contributing to cost-effectiveness, environmental sustainability, and enduring service provision. Nonetheless, issues include data privacy concerns, elevated implementation expenses, opposition to change, interoperability challenge, and regulatory issues persist as substantial barriers. Subsequent investigations must concentrate on the development of culturally relevant AI algorithms, the enhancement of Arabic natural language processing, and the establishment of AI-driven mental health systems. By confronting these challenges and utilizing emerging technologies, Saudi Arabia has the potential to establish its status as a leading nation in medical services innovation, guaranteeing patient-centered, efficient, and accessible healthcare delivery. Recommendations must include augmenting data privacy and security, minimizing implementation expenses, surmounting resistance to change, enhancing interoperability, fortifying regulatory frameworks, addressing regional inequities, and investing in nascent technologies. Full article

Artificial intelligence (AI) is rapidly reshaping adult cardiac surgery, enabling more accurate diagnostics, personalized risk assessment, advanced surgical planning, and proactive postoperative care. Preoperatively, deep-learning interpretation of ECGs, automated CT/MRI segmentation, and video-based echocardiography improve early disease detection and refine risk stratification beyond conventional tools such as EuroSCORE II and the STS calculator. AI-driven 3D reconstruction, virtual simulation, and augmented-reality platforms enhance planning for structural heart and aortic procedures by optimizing device selection and anticipating complications. Intraoperatively, AI augments robotic precision, stabilizes instrument motion, identifies anatomy through computer vision, and predicts hemodynamic instability via real-time waveform analytics. Integration of the Hypotension Prediction Index into perioperative pathways has already demonstrated reductions in ventilation duration and improved hemodynamic control. Postoperatively, machine-learning early-warning systems and physiologic waveform models predict acute kidney injury, low-cardiac-output syndrome, respiratory failure, and sepsis hours before clinical deterioration, while emerging closed-loop control and remote monitoring tools extend individualized management into the recovery phase. Despite these advances, current evidence is limited by retrospective study designs, heterogeneous datasets, variable transparency, and regulatory and workflow barriers. Nonetheless, rapid progress in multimodal foundation models, digital twins, hybrid OR ecosystems, and semi-autonomous robotics signals a transition toward increasingly precise, predictive, and personalized cardiac surgical care. With rigorous validation and thoughtful implementation, AI has the potential to substantially improve safety, decision-making, and outcomes across the entire cardiac surgical continuum. Full article

Digital technologies that continuously quantify animal behavior, physiology, and production offer significant potential for the early identification of health and welfare disorders of dairy cows. In this study, a multimodal artificial intelligence (AI) framework is proposed for real-time health monitoring of dairy cows through the integration of physiological, behavioral, production, and thermal imaging data, targeting veterinarian-confirmed udder, leg, and hoof infections. Predictions are generated at the cow-day level by aggregating multimodal measurements collected during daily milking events. The dataset comprised 88 lactating cows, including veterinarian-confirmed udder, leg, and hoof infections grouped under a single ‘sick’ label. To prevent information leakage, model evaluation was performed using a cow-level data split, ensuring that data from the same animal did not appear in both training and testing sets. The system is designed to detect early deviations from normal health trajectories prior to the appearance of overt clinical symptoms. All measurements, with the exception of the intra-ruminal bolus sensor, were obtained non-invasively within a commercial dairy farm equipped with automated milking and monitoring infrastructure. A key novelty of this work is the simultaneous integration of data from three independent sources: an automated milking system, a thermal imaging camera, and an intra-ruminal bolus sensor. A hybrid deep learning architecture is introduced that combines the core components of established models, including U-Net, O-Net, and ResNet, to exploit their complementary strengths for the analysis of dairy cow health states. The proposed multimodal approach achieved an overall accuracy of 91.62% and an AUC of 0.94 and improved classification performance by up to 3% compared with single-modality models, demonstrating enhanced robustness and sensitivity to early-stage disease. Full article

Digital twins, understood as computational replicas of poultry production systems updated in real time by sensor data, are increasingly invoked as transformative tools for precision livestock farming and sustainable agriculture. They are credited with enhancing feed efficiency, reducing greenhouse gas emissions, enabling disease detection earlier and improving animal welfare. Yet close examination of the published evidence reveals that these promises rest on a surprisingly narrow empirical foundation. Across the available literature, no peer reviewed study has quantified the full lifecycle carbon footprint of digital twin infrastructure in poultry production. Only one field validated investigation reports a measurable improvement in feed conversion ratio attributable to digital optimization, and that study’s design constrains its general applicability. A standardized performance assessment framework specific to poultry has not been established. Quantitative evaluations of reliability are scarce, limited to a small number of studies reporting data loss, sensor degradation and cloud system downtime, and no work has documented abandonment timelines or reasons for discontinuation. The result is a pronounced gap between technological aspiration and verified performance. Progress in this domain will depend on small-scale, deeply instrumented deployments capable of generating the longitudinal, multidimensional evidence required to substantiate the environmental and operational benefits attributed to digital twins. Full article

Background: Medication adherence and persistence in treating chronic diseases present as a continuous challenge for healthcare providers in long-term management. The most frequent reasons that several diseases are poorly controlled in the population include suboptimal drug adherence and discontinuation of therapies. One main issue why physicians cannot detect patients with poor adherence is that they have relatively limited time and tools to do so. Aim: To review the critical role of medication adherence in the management of chronic diseases by addressing the following: what medication adherence is; its critical role; factors and strategies influencing it; challenges and consequences of poor adherence; patients at risk; present and future strategies in place to detect and improve adherence; implications for public health and health value creation for patients; key analytical frameworks for understanding it; determinants; how adherence improves health; the role of healthcare professionals and technological innovations; implications of medication adherence; adherence as a key area for exploring the psychological mechanisms underlying patient behavior; and patient adherence as a major social and public health challenge. Finally, this review considers strengths, limitations, recommendations, and future value. Methodology: The following databases were used to carry out the review: PubMed, Scopus, Google Scholar, and ScienceDirect. The following themes were combined in the search: what adherence is, why it is critical, why adherence occurs, and how to improve adherence. The following search terms were used: what adherence is and critical, why and adherence and occurs, and how and to improve adherence. Results: Under the theme of why adherence is critical, five sub-themes were reviewed; four sub-themes were reviewed under the theme of why adherence occurs; and five sub-themes were reviewed under the theme of how to improve adherence. Conclusions: Strategies to enhance medication adherence involve a comprehensive approach that includes patient education, streamlined treatment plans, digital tools, and effective communication from healthcare professionals. Full article

This study examines the economic feasibility of adopting digital technologies for the early detection of Verticillium wilt in olive cultivation in Northern Evia, Greece. A Net Present Value (NPV) framework with different scenarios was employed to derive three adoption thresholds: the minimum effectiveness required to break even, the maximum tolerable cost at a target effectiveness, and the break-even olive-oil price. The results reveal substantial variability across scenarios, reflecting uncertainty in both disease dynamics and market conditions. Key determinants of feasibility include detection effectiveness, adoption costs, olive oil prices, and disease incidence. Larger holdings consistently face more favourable thresholds due to economies of scale, while smaller farms remain constrained unless collective actions or policy support reduces costs. The preliminary evidence indicates that early detection technologies can strengthen the resilience of olive farms, especially in high-incidence areas, though feasibility remains highly sensitive to costs, prices, and pathogen pressure. Finally, the findings underscore the need for targeted policy interventions to facilitate broader adoption. Full article

Non-communicable diseases (NCDs) impose an overwhelming burden on global health systems. Prevailing healthcare for NCDs remains largely hospital-centered, episodic, and reactive, rendering them poorly suited to address the long-term, heterogeneous, and multifactorial nature of NCDs. Rapid advances in digital technologies, artificial intelligence (AI), and precision medicine have catalyzed the development of an integrative framework for digital-intelligent precision health management, characterized by the functional integration of data, models, and decision support. It is best understood as an integrated health management framework operating across three interdependent dimensions. First, it is grounded in multidimensional health-related phenotyping, enabled by continuous digital sensing, wearable and ambient devices, and multi-omics profiling, which together allow for comprehensive, longitudinal characterization of individual health states in real-world settings. Second, it leverages intelligent risk warning and early diagnosis, whereby multimodal data are fused using advanced machine learning algorithms to generate dynamic risk prediction, detect early pathological deviations, and refine disease stratification beyond conventional static models. Third, it culminates in health management under intelligent decision-making, integrating digital twins and AI health agents to support personalized intervention planning, virtual simulation, adaptive optimization, and closed-loop management across the disease continuum. Framed in this way, digital-intelligent precision health management enables a fundamental shift from passive care towards proactive, anticipatory, and individual-centered health management. This Perspectives article synthesizes recent literature from the past three years, critically examines translational and ethical challenges, and outlines future directions for embedding this framework within population health and healthcare systems. Full article

The temperate Himalayan states of Jammu and Kashmir, Himachal Pradesh, Uttarakhand, Ladakh, Sikkim, and Arunachal Pradesh in India face unique agro-ecological challenges across agriculture and allied sectors, including pest and disease pressures, inefficient resource use, post-harvest losses, and fragmented supply chains. This review systematically examines 21 critical problem areas, with three key challenges identified per sector across agriculture, agricultural engineering, fisheries, forestry, horticulture, sericulture, and animal husbandry. Artificial Intelligence (AI) and Machine Learning (ML) interventions, including computer vision, predictive modeling, Internet of Things (IoT)-based monitoring, robotics, and blockchain-enabled traceability, are evaluated for their regional applicability, pilot-level outcomes, and operational limitations under temperate Himalayan conditions. The analysis highlights that AI-enabled solutions demonstrate strong potential for early pest and disease detection, improved resource-use efficiency, ecosystem monitoring, and market integration. However, large-scale adoption remains constrained by limited digital infrastructure, data scarcity, high capital costs, low digital literacy, and fragmented institutional frameworks. The novelty of this review lies in its cross-sectoral synthesis of AI/ML applications tailored to the Himalayan context, combined with a sector-wise revenue-loss assessment to quantify economic impacts and guide prioritization. Based on the identified gaps, the review proposes feasible, context-aware strategies, including lightweight edge-AI models, localized data platforms, capacity-building initiatives, and policy-aligned implementation pathways. Collectively, these recommendations aim to enhance sustainability, resilience, and livelihood security across agriculture and allied sectors in the temperate Himalayan region. Full article

Abdominal radiography is commonly used as an initial diagnostic tool in dogs and cats with gastrointestinal (GI) signs. Historically, abdominal radiographs were considered unreliable for detecting GI masses, with detection rates below 50%. The purpose of this retrospective, case–control study was to determine the accuracy of abdominal radiographs in identifying the presence and location of GI masses and to assess the influence of the reviewer experience. Radiographs from 114 dogs and 111 cats were reviewed by two board-certified radiologists, one first year radiology resident, and one rotating intern. Patients were categorized into three groups: animals with a GI mass greater than 2 cm (dogs n = 44; cats n = 41), animals with a normal abdomen (both n = 50), and animals with abdominal disease but no GI mass (both n = 20). Reviewers demonstrated high specificity but low sensitivity for both detection and localization of GI masses. Sensitivity for detecting a mass ranged from 34 to 64% in dogs and 36 to 71% in cats; specificity exceeded 87% in dogs and 92% in cats. Sensitivity for location identification ranged from 9 to 58% in dogs and 21 to 68% in cats; specificity exceeded 76% in dogs and 81% in cats. No statistically significant differences in detection rates were found among reviewers. The accuracy of plain digital radiography for the detection of gastrointestinal masses in dogs (75%) and cats (81%) is better than previously reported film radiography but remains inferior to other imaging modalities. However, its high specificity supports its clinical utility in ruling out gastrointestinal masses. Full article

Hereditary ataxias are a heterogeneous group of disorders with overlapping clinical presentations but diverse genetic and molecular etiologies. Biomarkers are increasingly essential to improve diagnosis, refine prognosis, and accelerate the development of targeted therapies. Following PRISMA-ScR guidelines, we conducted a scoping review of PubMed and complementary sources (2010–2025) to map and describe the current landscape of genetic, imaging, fluid, electrophysiological, and digital biomarkers across the most prevalent hereditary ataxias, including SCA1, SCA2, SCA3, SCA6, SCA7, SCA17, SCA27B, dentatorubral–pallidoluysian atrophy (DRPLA), Friedreich’s ataxia (FRDA), RFC1-related ataxia (CANVAS), SPG7, and fragile X-associated tremor/ataxia syndrome (FXTAS). Eligible evidence encompassed observational cohorts, clinical trials, case series, and case reports providing primary biomarker data, with the objective of characterizing evidence breadth and identifying knowledge gaps rather than assessing comparative effectiveness. Across modalities, converging evidence highlights subtype-specific biomarker signatures. MRI volumetry, DTI, and FDG-PET map characteristic neurodegeneration patterns. Fluid biomarkers such as neurofilament light chain are informative across several SCAs and FRDA, while frataxin levels constitute robust endpoints in FRDA trials. Pathology-specific biomarkers such as ataxin-3 are advancing as tools for target engagement and may generalize to future gene-lowering strategies. Electrophysiological and oculographic measures show sensitivity for early disease detection, and wearable technologies are emerging as scalable tools for longitudinal monitoring. This scoping review synthesizes the heterogeneous evidence on hereditary ataxia biomarkers, highlighting multimodal frameworks that link molecular mechanisms with clinical endpoints. Mapping current approaches also reveals substantial variability and gaps across diseases and modalities, underscoring the need for harmonized validation in international multicenter cohorts and systematic integration into future clinical trials to advance precision medicine in hereditary ataxias. Full article

Background/Objectives: Cardiovascular diseases (CVDs) remain the leading cause of morbidity and mortality worldwide and increasingly are recognized as a continuum of interconnected conditions rather than isolated entities. Methods: A structured narrative literature search was performed in PubMed, Scopus, and Google Scholar for publications from 2015 to 2025 using combinations of different keywords: “cardiovascular disease spectrum”, “multi-omics”, “precision cardiology”, “machine learning”, and “artificial intelligence in cardiology”. Results: Evidence was synthesized across seven major clusters of cardiovascular conditions, and across these domains, common biological pathways were mapped onto heterogeneous clinical phenotypes, and we summarize how multi-omics integration, AI-enabled imaging and digital tools contribute to improved risk prediction and more informed clinical decision-making within this spectrum. Conclusions: Interpreting cardiovascular conditions as components of a shared disease spectrum clarifies cross-disease interactions and supports a shift from organ- and syndrome-based classifications toward mechanism- and data-driven precision cardiology. The convergence of multi-omics, and AI offers substantial opportunities for earlier detection, individualized prevention, and tailored therapy, but requires careful attention to data quality, equity, interpretability, and practical implementation in routine care. Full article