Search Results (115)

Cardiovascular disease remains the leading global health challenge, claiming approximately 19.8 million lives annually. The convergence of wearable technology and artificial intelligence represents a transformative shift in cardiovascular healthcare, enabling continuous real-time monitoring beyond conventional clinical settings. This narrative review synthesises current evidence on integrating consumer-grade and medical-grade wearable devices with AI algorithms for continuous cardiovascular monitoring applications, with particular attention to real-world translational applicability and global health equity. This review examined the technological landscape of wearable cardiovascular monitoring devices, including smartwatches with photoplethysmography and electrocardiogram capabilities, continuous cardiac monitoring patches, and emerging biosensor technologies. Also, the review explored AI methodologies, particularly machine learning and deep learning architectures, employed in processing complex physiological data streams from these devices. Clinical applications demonstrate impressive capabilities: arrhythmia detection with sensitivity rates exceeding 98%, continuous blood pressure monitoring through cuffless technologies, heart failure decompensation prediction, and cardiovascular risk stratification. However, substantial challenges persist, including data quality assurance, algorithm interpretability, regulatory compliance, and seamless clinical workflow integration. Privacy concerns, health disparities in algorithm performance, and the need for robust validation across diverse populations remain critical considerations. AI-enhanced wearable systems hold considerable potential for shifting cardiovascular care from reactive treatment paradigms towards predictive, preventive, and precision medicine approaches. Future directions include edge computing architectures, federated learning approaches, personalised AI models, enhanced interoperability with electronic health records, and expansion to resource-limited settings, ultimately improving patient outcomes whilst reducing healthcare costs. Full article

Quantifying the spatial distribution of immune cells within intact skin tissue is essential for understanding diseases such as vitiligo, in which CD8⁺ T cells selectively destroy epidermal melanocytes within the discrete, parallelogram-shaped epidermal compartments of mouse tail skin, which we term scales. Existing workflows rely on manual region drawing, which is labor-intensive and operator-dependent. Here we present ScaleNet, a three-stage deep-learning pipeline for automated per-scale quantification of whole-mount immunofluorescent images, implemented as an Imaris XTension to enable seamless integration with existing 3D imaging workflows. ScaleNet (i) encodes a 3D confocal volume as a pseudo-RGB projection that preserves height information lost by standard maximum-intensity projection, (ii) applies two independently trained Detectron2 Mask R-CNN models—one for epidermal scales and one for hair follicles—with sliced inference (SAHI) to segment whole-mount images at full resolution, and (iii) maps the resulting 2D mask back into the Imaris 3D coordinate system to quantify user-defined Spot objects per scale. Applied to vitiligo mice imaging, ScaleNet produced per-scale counts of CD8⁺ T cells and DCT⁺ melanocytes, enabling unbiased spatial statistics in the tail epidermis, demonstrating that ScaleNet can provide the quantitative spatial resolution needed to dissect the micro-anatomical dynamics of autoimmune depigmentation. Full article

Background/Objectives: The first permanent molar (M1) is critical for occlusal development but is highly susceptible to caries and molar–incisor hypomineralization (MIH). When M1 prognosis is poor, extraction may be necessary, requiring accurate evaluation for post-extraction space management. This study aims to develop and validate an automated deep learning framework using YOLOv8n with oriented bounding boxes (OBB) to predict the likelihood of spontaneous space closure following M1 extractions, thereby reducing diagnostic subjectivity and inter-observer variability. Methods: A dataset of 200 pediatric panoramic radiographs was segmented into quadrants and annotated for second permanent molars (M2s) and third molars (M3s). The YOLOv8n-OBB architecture was trained on 640 × 640 pixel images over 100 epochs. The framework integrated M3 presence, M2 Demirjian developmental maturity (proxied by bounding box height), and M2 angulation (via rotation vectors) to map inputs onto an evidence-based clinical decision matrix for prognostic stratification. Results: The model achieved exceptional detection and localization performance with an overall mean average precision (mAP@0.5) of 0.983. Class-specific validation showed high accuracy for M2 (F1-score = 0.978) and M3 (F1-score = 0.904). Quantitative cross-referencing confirmed a seamless mapping of spatial coordinates onto clinical success classes without error propagation. Conclusions: These findings substantiate the YOLOv8n-OBB model as a robust and interpretable decision-support tool. By standardizing prognostic assessments and optimizing treatment planning workflows, the framework serves as an effective aid in pediatric dentistry for managing M1 extractions. Full article

Artificial intelligence (AI) and machine learning (ML) algorithms possess the capability to accelerate the design of novel materials; however, their advancement in materials science is severely hindered by a fundamental deficit of experimental data, commonly referred to as data starvation. Unlike solution-based chemistry, where high-throughput (HT) technologies are a well-established standard, the automated synthesis of solid materials—particularly polymers and multicomponent composites—poses an extreme engineering challenge. Furthermore, the traditional, manual research model is inherently flawed by human bias, notably the systematic non-publication of negative results, which deprives AI models of critical boundary information regarding the design space. This paper is the first in a three-part review series defining the architecture of a fully automated, unbiased “data factory” for closed-loop discovery. This section focuses on the physical foundations of the HT workflow: experimental planning, automated synthesis, and material management. Emphasis is placed on the paradigm shift from classical, discrete Design of Experiments (DoE) to the novel concept of Continuous Gradient DoE. It reviews how robotic platforms utilizing precise gravimetric and volumetric feeders, integrated with extruders and in-line capillary rheology, enable the seamless, high-throughput manufacturing of thermoplastics and composites. Moreover, an innovative approach to sample logistics is presented, redefining classical storage patterns through the implementation of Continuous Material Management. This encompasses direct physical tagging (e.g., inkjet marking on continuous filaments or films), spool-based transport systems, and precise, real-time metadata mapping. As demonstrated, the integration of these systems yields an order-of-magnitude increase in productivity (generating tens of thousands of novel material variants annually), a radical reduction in unit costs, and the production of terabytes of standardized, machine-readable data. Establishing this reliable hardware and analytical infrastructure represents the essential first step toward unlocking the full potential of artificial intelligence in advanced materials engineering. Full article

This research represents an advancement in smartphone-based image acquisition methodology, building upon a previous study to estimate the essential oil content of bergamot fruits in situ using a deep learning approach. To overcome an operational constraint due to a bulky portable dark box to standardise illumination, this study proposes a more versatile solution: a mobile application based on a colour card reference. By replacing physical shielding with digital compensation, the app functions as a local colourimetric sensor, enabling real-time correction of images acquired directly in the orchard, regardless of environmental variables such as direct sunlight or shadows. Workflow relies on an automated calibration procedure. Upon image acquisition, the application utilises ArUco Markers to autonomously detect and extract both the colour card and the fruit surface. The core of the innovation lies in the colour calibration algorithm based on RGB histogram matching logic, which calculates the precise chromatic transformation required to align the field data with the reference card data (acquired under controlled conditions). These calculated parameters are then dynamically mapped onto the fruit’s image. The final output is a normalised high-fidelity image, ready for the calculation of chromatic indices, such as the citrus colour index, or for seamless integration into predictive models. The results show that the application is a valid tool for colour calibration, thanks to the good agreement with the values obtained using the inspection chamber. The latter can therefore be replaced by the app, which allows reliable results to be obtained even when used on its own. Full article

Background: Precise intraoperative localisation of small colorectal tumours during laparoscopic surgery remains challenging due to absent tactile feedback and subserosal tumour location. Current standard methods, particularly India ink tattooing, demonstrate 15–30% failure rates for lesions less than 10 mm, leading to prolonged operative times, incomplete resections, and re-operations. Multiple emerging technologies promise improved localisation, yet comparative evidence remains fragmented. Objective: To map and characterise the current landscape of intraoperative marking and identification technologies for small colorectal tumour localisation during laparoscopic surgery, with emphasis on radiofrequency-based methods and alternative approaches, and to identify evidence gaps guiding future research. Methods: Following PRISMA-ScR guidelines, we systematically searched PubMed, Web of Science, and Scopus databases from January 2000 through December 2025 for studies evaluating tumour localisation technologies in colorectal cancer surgery, including primary tumour localisation during laparoscopic colectomy and localisation of colorectal liver metastases during hepatic surgery, or transferable anatomical applications with documented translational potential to colorectal surgery. Two independent reviewers screened all records, with discrepancies resolved through discussion and a third senior reviewer consulted for unresolved disagreements; data were extracted on technical performance, safety, feasibility, cost-effectiveness, usability, innovation potential, and evidence quality. Results: We included 89 studies comprising 18 colorectal-specific articles and 71 transferable/GI-adjacent studies. Detection success rates ranged from 71% to 100% across modalities. Near-infrared fluorescence with indocyanine green demonstrated the strongest clinical evidence with 75–100% detection across eight colorectal studies encompassing 2134 procedures and seamless workflow integration. Radiofrequency identification systems achieved 91.9–99% detection in feasibility studies with promising tissue penetration of 15–35 mm but limited colorectal validation. Electromagnetic navigation excelled in rigid organs with 85–98% success but showed degraded performance in mobile bowel at 71–75%. Critical evidence gaps included absent head-to-head comparative trials, non-standardised outcome metrics limiting cross-study comparability, and limited long-term safety data with only 14 studies providing follow-up exceeding six months. Conclusions: ICG fluorescence represents the most clinically mature technology identified, representing a priority candidate for colorectal-specific validation in challenging localisation scenarios. RFID systems demonstrate promising characteristics justifying prioritised research investment through adequately powered comparative trials. Future research must emphasise consortium-based comparative effectiveness studies, standardised outcome metrics, and integration with robotic and AI-assisted surgical platforms to accelerate clinical translation. Full article

This study investigates the determinants of driver acceptance of “M-Flow”, Thailand’s first Advanced Traffic Management solution utilizing Multi-Lane Free Flow (MLFF) technology. While designed to eliminate toll plaza bottlenecks through AI-driven automated billing, the system’s operational efficiency is hindered by a “trust gap” caused by a stringent ten-fold penalty for late payment compliance. By integrating the Technology Readiness Index (TRI 2.0) with the Technology Acceptance Model (TAM), this research explores how psychological readiness dictates the success of smart traffic infrastructures. Data from 485 drivers were analyzed using Structural Equation Modeling (SEM). The results reveal that while technological optimism and innovativeness act as motivators, Insecurity (β = −0.723) emerges as the dominant psychological barrier, directly suppressing the perceived ease of use and triggering behavioral resistance. The findings demonstrate that technical efficiency and diverse payment options alone are insufficient to ensure mass adoption if the regulatory climate fosters financial anxiety. To maximize system throughput, this study recommends that policymakers shift from punitive enforcement to “trust engineering.” By enhancing financial transparency, simplifying the registration-to-payment workflow, and mitigating the “penalty trap” perception, authorities can achieve the psychological seamlessness that is a strict prerequisite for a fully trusted smart transportation infrastructure in Thailand. Full article

As the Kingdom of Saudi Arabia (KSA) accelerates its transition toward smart mobility under Vision 2030, establishing a robust digital infrastructure is paramount for the safe deployment of autonomous vehicles (AVs). High-definition (HD) maps serve as a critical foundation for this infrastructure, yet their deployment is severely bottlenecked by extreme operational costs, massive data processing payloads, and rapid environmental variations across vast highway networks. To address these challenges, this paper proposes a comprehensive, localized national strategy structured around three key tasks. First, it establishes a unified national HD map standard to guarantee seamless interoperability and data sharing among competing AV manufacturers and government transport authorities. Second, it implements an AI-powered baseline workflow using Mobile Mapping Systems (MMS) for high-fidelity static map construction, anchored and validated within designated pilot zones, including the King Abdulaziz University campus and key sectors in the Kingdom. Third, it deploys a decentralized, vision-based crowdsourcing system that leverages active public and commercial vehicle fleets for real-time map maintenance. By integrating a sovereign edge-cloud AI infrastructure that respects local Personal Data Protection Law (PDPL), this framework bridges the gap between high-accuracy baseline mapping and long-term economic sustainability, offering an actionable technical roadmap for scaling a resilient digital transport layer across the Kingdom. Full article

Accurate quantification of riprap volume is critical for cost control, quality assurance, and navigation safety in inland waterway maintenance projects. Conventional methods, such as draft mark reading and RTK-based point surveying, are constrained by limited accuracy, low efficiency, and operational risk. To address these limitations, this study proposes a fully automated riprap volume quantification method based on portable LiDAR simultaneous localization and mapping. The proposed framework establishes a seamless, intervention-free workflow. This automated process sequentially integrates real-time scan monitoring, target vessel extraction, riprap segmentation, deck baseline reconstruction, and 3D volume estimation. Specifically, riprap-laden transport vessels are automatically identified using density-based clustering and trajectory information. Subsequently, deck-loaded riprap piles are extracted through point-cloud geometric analysis and quantified via deck fitting and mesh reconstruction. The method was validated through ten field experiments in the Jingjiang reach of the middle Yangtze River, China. Compared to benchmark volumes established via standard point-cloud processing software, the proposed method achieved an average relative error of 1.37% and a maximum error strictly below 5%. Furthermore, the system proved highly efficient, requiring an average processing time of only 392.1 s per dataset. The results demonstrate that the proposed method is accurate, efficient, and robust, and has strong potential for intelligent riprap quantification in inland waterway engineering. Full article

Addressing the prevalent issues of scattered data sources, reliance on multi-software collaboration, and low integration efficiency between Building Information Modeling (BIM) and Life Cycle Assessment (LCA) in current building life cycle carbon emission accounting, this study aims to construct a BIM-driven, data-traceable automated method for building life cycle carbon accounting. This paper proposes a life cycle carbon accounting framework based on Revit secondary development. By defining unified data mapping rules and constructing a scalable localized carbon emission factor database, this framework achieves a seamless workflow from BIM model information extraction and intelligent factor matching to phased accounting and report generation. Taking an office building in Nanning as an empirical case study, the results indicate that the operational stage and the building material production stage are the primary emission sources, accounting for 78.82% and 24.13% of the total emissions, respectively; the transportation stage accounts for 1.68%; the construction stage accounts for 0.40%; and the demolition and recycling stage exhibits negative emissions of −3.53% due to material recovery benefits. The accounting results of the developed plugin exhibit a relative error of 6.67% compared to traditional methods, and the robustness of the accounting framework is verified through uncertainty analysis. Sensitivity analysis further reveals that the grid emission factor, key material factors, and building design service life are the core variables affecting carbon emissions. The contribution of this study lies in proposing an operable and scalable BIM-LCA integrated solution. Its practical value resides in providing a real-time data feedback tool for low-carbon optimization during the building design stage, as well as offering a highly transparent methodological reference for carbon accounting in engineering practice, thereby supporting data-driven decision-making in the pursuit of sustainable urban development. Full article

While integrating blockchain technology into Business Process Management (BPM) has gained attention, existing compilation-based approaches suffer from high redeployment costs and isolated network structures. This study proposes an FSM-based workflow interpreter engine utilizing the Inter-Blockchain Communication (IBC) protocol within the Cosmos ecosystem to overcome these limitations. The proposed system adopts an interpreter architecture that treats business logic as lightweight JSON specifications instead of hard-coding it into smart contracts. This separation allows for process updates through data modification rather than contract redeployment, significantly increasing operational flexibility. Furthermore, custom IBC packet structures were designed to enable seamless cross-chain process synchronization between independent application-specific blockchains. Experimental results demonstrate that the interpreter approach reduces process update costs by over 90% compared to conventional compilation methods. Additionally, gas consumption exhibited a linear growth pattern relative to task count and gateway complexity, ensuring cost predictability for large-scale business scenarios. Interoperability validation using a standard Procurement Order (PO) process showed successful cross-chain state transitions with a latency of approximately 1.45 s. This research provides a practical solution for building trust-based decentralized collaboration ecosystems by simultaneously achieving operational efficiency and interoperability in blockchain BPM. Full article

Background/Objectives: Precise nuclei instance segmentation is a prerequisite for reliable digital pathology, yet the scarcity of pixel-level annotations remains a significant bottleneck for deep learning models. Methods: We propose a self-evolving framework for robust nuclei segmentation that uses only sparse point annotations, extending the Segment Anything Model (SAM). To overcome the limitations of static pseudo-labels, our method introduces a self-evolving labeling strategy via Exponential Moving Average (EMA), which adaptively refines learning targets. We also integrate instance-aware contrastive learning using point prompts as spatial anchors and implement a consensus-based filtering mechanism between prompt-guided and prompt-free decoders. Results: Extensive evaluations on CPM17, MoNuSeg, and the challenging CoNSeP datasets demonstrate that our framework achieves state-of-the-art performance across various backbones, including ViT-B and ViT-H. Conclusions: By enabling a seamless transition from general-purpose foundation models to specialized histopathology experts, this self-refining approach delivers a highly efficient, accurate solution for automated diagnostic workflows in clinical settings. Full article

Background: This article provides a comprehensive overview of recent advancements in artificial intelligence (AI) and deep-learning technologies for breast cancer (BC) diagnosis across various imaging modalities. Methods: A systematic review was conducted in strict adherence to the PRISMA guidelines, incorporating a comparative analysis of 65 peer-reviewed studies published between 2018 and 2024. The evaluation focused on diagnostic performance, architectural developments, and clinical integration strategies. Results: The review synthesizes primary findings on convolutional neural networks (CNNs), emerging architectures including graph neural networks, and hybrid models, with diagnostic accuracy, risk prediction, and personalized screening strategies identified as the leading research domains. Notable achievements include CNNs attaining up to 98.5% accuracy in mammography and Vision Transformers reaching 96% in histopathological analysis. Furthermore, the implementation of explainable AI methodologies, such as SHAP, LIME, and Grad-CAM, is emphasized for maintaining transparency, trust, and accountability in clinical decision-making. Conclusions: AI constitutes a pivotal factor in facilitating early BC diagnosis and optimizing treatment outcomes. Nevertheless, significant challenges persist, including dataset heterogeneity, model generalizability, standardization of imaging protocols, computational resource limitations, and the seamless integration of these technologies into established clinical workflows. Future research must prioritize robust multi-dataset validation and standardized implementation frameworks to overcome existing limitations and advance successful BC diagnostic practices. Full article

Assistive bathing for the elderly and disabled presents significant challenges regarding caregiver workload and safety. This paper presents the design and verification of a multi-module integrated intelligent bathing assistance system. The system automates the entire bathing sequence through four coordinated modules: a robotic scrubbing unit, a climate-controlled cabin, a passive multifunctional wheelchair, and a multi-degree-of-freedom transfer device. A key innovation is the wheelchair’s passive design with an automated docking mechanism, ensuring safety in wet environments. Unlike existing commercial solutions and the existing literature, which primarily focus on fragmented, singular functionalities (such as transfer-only devices or fixed-spray cabins), the core advantage of the developed system lies in its holistic integration of safe physical transfer, adaptive robotic scrubbing, and microenvironment control into a seamless, unified architecture. Employing a modular and ergonomic approach, the system executes a predefined 12-step automated workflow. Experimental validation demonstrates an average bathing time of 16.6 min and a quantifiable 69.8% reduction in caregiver workload, confirming the system’s high efficiency and practical utility in alleviating caregiver burden. Full article

Background: Artificial intelligence (AI) holds transformative potential for mental health services. However, existing reviews have predominantly focused on algorithmic accuracy, with limited attention to how these technologies are implemented and integrated into real-world service delivery. This scoping review addresses this gap by examining the contexts in which AI technologies—including large language models (LLMs) and machine learning—are implemented, as well as the factors influencing their sustainable adoption within real-world mental health service systems. Methods: Following the established methodological framework, a systematic search (2015–2026) was conducted in PubMed and Scopus. Two independent reviewers screened an initial pool of 829 records using Zotero and Rayyan to minimize selection bias. Following title, abstract, and full-text screening based on predefined eligibility criteria, 26 studies focusing on real-world AI applications (e.g., clinical settings, community services, and case management) were included in the final synthesis. Results: The findings indicate a rapid acceleration in research, with 50% of included studies (n = 13) published since 2024. AI-driven decision support systems were the most prevalent (50%, n = 13), followed by predictive machine learning models (27%) and generative AI applications (15%). Most tools were designed for clinician use (77%) and implemented in hospital-based settings (46%). Although 46% of studies reported real-world implementation, more than half remained at the pilot stage. Notably, research emphasis has shifted from technical efficacy toward feasibility, and implementation contexts (n = 17). Conclusion: AI in mental health is transitioning from laboratory validation to real-world integration. However, the current landscape remains heavily centered on clinician workflows and screening functions, with limited expansion into community-based recovery and long-term prevention. To move beyond the pilot stage, future initiatives should prioritize seamless workflow integration and the application of structured ethical and implementation frameworks that support clinician–patient relationships. This review provides an evidentiary basis for advancing sustainable, AI-enhanced mental health service delivery. Full article