Search Results (89)

Background/Objectives: Rectovaginal fistula (RVF) is a challenging condition that can substantially impair quality of life and often requires individualized surgical management. Accurate preoperative imaging is essential for identifying fistulous tracts, assessing pelvic floor involvement, and guiding surgical planning. However, conventional imaging modalities, including magnetic resonance imaging (MRI), may still have limitations in detecting small, occult, intermittently patent, or anatomically complex fistulas, particularly under static imaging conditions. This study aimed to evaluate the diagnostic performance of transperineal pelvic floor ultrasound (TPUS) combined with ultrasound contrast agent enema (UCAE) and its potential value in preoperative assessment of RVF. Methods: In this prospective single-center study, 62 women with surgically confirmed RVF were enrolled between January 2022 and April 2025. All patients underwent TPUS combined with UCAE before surgery, while subsets also underwent contrast-enhanced CT, contrast-enhanced MRI, or barium enema according to clinical indications. Imaging findings were compared with intraoperative findings as the reference standard. Fistula detection, morphological classification, size, anatomical location, pelvic floor injury assessment, and concordance between ultrasound-based surgical proposals and actual surgical procedures were systematically analyzed. Comparative analyses with conventional imaging modalities were performed based on available non-paired subgroups. Results: UCAE detected RVF in 60 of 62 patients, yielding a detection rate of 96.8%. In the available comparative cohort, UCAE demonstrated higher detection rates than conventional imaging modalities, particularly in small and complex fistulas. UCAE showed high agreement with intraoperative findings in terms of fistula morphology, size, and anatomical location. TPUS provided additional and complementary information regarding levator ani and anal sphincter injuries and yielded higher sensitivity and accuracy in pelvic floor injury assessment within the studied cohort. Ultrasound-based surgical proposals showed directional concordance with final intraoperative decisions in 88.7% of cases. No adverse events were observed during UCAE. Conclusions: TPUS combined with UCAE appears to be a safe and feasible preoperative imaging approach for RVF. By integrating fistula detection, anatomical classification, pelvic floor assessment, and surgical planning, this combined ultrasound approach may serve as a complementary imaging strategy to conventional imaging modalities, particularly for small or intermittently patent fistulas. Further multicenter studies and standardized imaging protocols are warranted to validate its clinical utility and generalizability. Full article

Background: Cardiovascular disease (CVD) remains the leading cause of mortality across the European Union (EU), with a fourfold to fivefold east–west gradient in standardized circulatory-disease mortality. Romania ranks second highest in the EU (787 per 100,000 inhabitants in 2023). Primary-prevention practice is organized around two quantitative frameworks—the 2021 European Society of Cardiology (ESC) SCORE2/SCORE2-OP system and the 2018/2019 American College of Cardiology/American Heart Association (ACC/AHA) Pooled Cohort Equations (PCE)—which converge on therapy but diverge on patient selection. Methods: We conducted a structured narrative review (SANRA-compliant) of contemporary primary-prevention guidelines, validation studies, key trials of risk-modifier interventions, and Romanian epidemiological data through April 2026. On this basis, we developed ROMA-CV (Risk Of Multifactorial Atherosclerosis—CardioVascular) as a conceptual, country-specific risk-stratification framework anchored to existing Class I/IIa recommendations or Level A/B evidence, rather than as a fully developed, ready-to-use clinical tool Results: Four structural limitations of SCORE2 are clinically consequential in Romania: (i) an age floor of 40 years that excludes the population in which premature myocardial infarction is most preventable; (ii) a country-level calibration coefficient applied to individuals; (iii) permissive treatment of lipoprotein(a) [Lp(a)]; and (iv) an under-emphasis of subclinical-atherosclerosis imaging. We propose ROMA-CV (Risk Of Multifactorial Atherosclerosis—Cardiovascular), a four-step algorithm retaining SCORE2 as the quantitative spine while embedding once-in-a-lifetime Lp(a) measurement, a mandatory amplifier checklist, and selective coronary artery calcium (CAC) or carotid/femoral ultrasound imaging. Conclusions: ROMA-CV is a hypothesis-generating proposal that operationalizes existing evidence-based recommendations into a deterministic Romanian pathway aligned with the 2025–2030 Romanian National Plan for Non-Communicable Diseases and the EU Cardiovascular Health Plan. The algorithm is not a validated clinical decision tool and requires prospective external validation in Romanian cohorts—alongside feasibility, cost-effectiveness, and implementation studies—before any consideration of routine clinical adoption. Full article

Facade thermal defect diagnosis is a critical prerequisite for energy-efficiency retrofitting of old residential buildings. However, conventional infrared thermography is easily affected by environmental conditions and occupant behavior, making it difficult to distinguish persistent thermal defects from transient anomalies. To address this challenge, this study proposes an integrated diagnostic framework for old residential buildings in Wuhan, China, combining unmanned aerial vehicle (UAV) infrared thermography, multi-temporal data acquisition, 3D flight-path planning, thermal anomaly recognition, facade spatial mapping, and temporal screening. Field experiments were conducted to determine key acquisition parameters, including sensor preheating time, imaging distance, and acquisition timing. Thermal anomalies were identified through image-processing techniques and mapped onto facade representations derived from 3D models. Repeated observations across different times and days were then used to evaluate anomaly recurrence and spatial stability. The results show that preheating the sensor for at least 10 min, maintaining a UAV-to-facade distance of 8–10 m, and acquiring data around 17:00 provide more reliable thermal images. Multi-temporal screening effectively reduces false positives caused by temporary disturbances, while persistent anomalies associated with window–wall joints, floor slabs, wall surfaces, and moisture-related areas can be identified more robustly. The proposed framework provides a practical workflow for facade thermal defect diagnosis and retrofit-oriented decision support. Full article

The digitization of existing building stock often depends on legacy 2D raster floor plans (scanned drawings, PDF exports, or photographs) because structured building information models are frequently unavailable for older properties. Manual measurement and visual inspection of such documents are time consuming and error prone. This paper presents an integrated deep learning pipeline that extracts semantic information from unstructured two-dimensional floor plan images of existing structures and supports preliminary compliance screening via locally deployed large language models. The pipeline employs YOLOv8 for the localization and classification of 18 architectural symbols and furniture items, and a U-Net with a ResNet34 encoder for the semantic segmentation of walls and interior room spaces. To translate pixel-level predictions into physical metrics, we implement an area calculation module based on user-defined reference scale calibration. An LLM evaluation module, deployed locally via Ollama with a retrieval-augmented generation pipeline, interprets extracted room metrics and flags potential non-compliance against referenced residential design guidelines; it is intended for the assessment of existing layouts rather than generative co-design. We expand a core dataset of 101 manually annotated source floor plans to 303 augmented instances using label-aligned geometric transformations, while reporting generalization in terms of the 101 unique source plans. On the held-out validation split (10 source plans), YOLOv8 achieves 92.3% mAP50 versus 87.2% for a Faster R-CNN reference model on the same data split (detection baselines differ in training epochs and pretraining; see Experiments); U-Net achieves 95.71% mIoU, surpassing DeepLabv3+ (93.2%) under matched segmentation training settings. The system is deployed as an interactive web application for legacy building survey and preliminary regulatory review when only two-dimensional documentation is available. Full article

This study presents the diagnostic and conservation work carried out on the Roman mosaic of the South cubiculum in the Latomia dei Niccolini (Marsala, western Sicily). The mosaic, decorated with polychrome tesserae featuring a kantharos motif, presented severe structural damage, including fractures, subsurface voids, and progressive material loss. To assess the causes of deterioration and design an effective conservation strategy, an integrated approach combining non-invasive geophysical and 3D survey methods was applied. Ground-penetrating radar (GPR) was selected as the main diagnostic tool because it allows high-resolution subsurface imaging while preserving the integrity of the fragile mosaic surface. By utilizing high-frequency 2 GHz antennas and complementary video inspection, a significant subsurface cavity beneath the mosaic preparation layer was successfully mapped, determining its critical relationship with the main diagonal surface fracture. Simultaneously, laser scanning and close-range photogrammetry enabled the creation of accurate 3D models supporting both documentation and restoration planning. The conservation concluded with surface cleaning, mortar consolidation, and the successful structural detachment and relocation of the compromised section onto a lightweight support for future museum display. The findings demonstrate that integrating 3D digital and geophysical data provides a quantitative, low-risk roadmap for preserving highly vulnerable archaeological floorings, moving beyond qualitative technical documentation to establish a replicable preservation framework. Full article

Deep neural networks have been widely applied in architectural analysis and design research, supporting tasks such as facade recognition, floor-plan analysis, and architectural visual classification. However, although existing models possess strong predictive capabilities, their decision-making processes remain characterized by a pronounced black-box nature, making it difficult to provide architects with understandable and traceable grounds for judgment. This limits their practical value in the architectural field, as designers require not only accurate outputs but also interpretable explanatory evidence regarding the basis of decision-making. This issue is particularly critical in architectural interpretation, where judgments are rarely made solely on the basis of isolated visual features, but are instead often formed through comparison and negotiation with precedents, spatial logic, and domain knowledge. To address this challenge, this paper proposes ArchiExplain, a multi-level interpretability framework for architectural image understanding, aiming to enable a deeper understanding of architectural images. The main contributions of this study are threefold: (1) We construct two architectural datasets for interpretability evaluation: a facade dataset composed of streetscape images from Harbin, China, and Greece, and a floor-plan dataset consisting of Real-plan drawings from real design cases and standardized generated R-plan drawings. Unlike existing datasets that primarily serve style recognition, semantic parsing, or image generation tasks, the datasets in this paper focus on evaluating the correspondence among model explanations, precedent associations, visual evidence, and predictive judgments. (2) Based on the above datasets, we propose the ArchiExplain framework. Unlike attribution methods such as Grad-CAM, Saliency Maps, and Integrated Gradients, which mainly reveal local discriminative regions, or influence-based methods that only trace the influence of training samples, this framework integrates training-sample influence tracing, Saliency Maps, and Integrated Gradients. It establishes a unified evidential chain among precedent samples, discriminative image regions, and final predictions, thereby transforming neural network decisions into an interpretable reasoning process with architectural significance. (3) Experimental results show that ArchiExplain performs stably on 100 randomly selected test samples, achieving an accuracy of 98.41% in the facade classification task and 98.34% in the floor-plan classification task. Further deletion/occlusion faithfulness analysis shows that the main attribution methods outperform the random baseline. Meanwhile, a questionnaire study involving 28 architects further verifies the consistency between model explanations and human architectural cognition. These findings indicate that ArchiExplain can enhance the transparency of architectural deep learning models and has practical application potential in architectural design analysis, model diagnosis, and precedent-based learning. Full article

This study introduces a domain-specific multimodal deep learning framework, centered on a Vision Transformer (ViT), to accelerate the prediction of operational carbon emissions in residential buildings. Our approach uniquely fuses two data modalities: the geometric information captured in floorplan images and the quantitative data from vector-based building parameters. By training and testing on a comprehensive dataset of 17,000 residential samples derived from a large-scale open-source Chinese database, the proposed model demonstrates exceptional predictive capability. On the test set, it achieved a mean bias error of 1.75%, a mean absolute percentage error of 2.14%, and a coefficient of determination (R²) of 0.95. Further validation through comparative analysis shows that our framework significantly outperforms established deep learning architectures, including ResNet-18, Inception-V4, and VGG-19, in both accuracy and robustness. The developed tool provides architects with a reliable and rapid method for assessing the carbon footprint of design options, thereby offering crucial scientific support for sustainable building design. Full article

Background: Rehabilitation of edentulous mandibles in a post-oncologic setting remains a major clinical challenge. In such situations, placement of conventional endosseous implants may be compromised by severe bone deficiency, a history of peri-implant infection, and constraints related to reconstructive soft tissues. Customized titanium subperiosteal implants, made possible by three-dimensional imaging, computer-aided design, and additive manufacturing, represent a potential alternative when conventional options are unfavorable. This case report describes a full-arch fixed rehabilitation of an edentulous mandible in a patient previously treated for squamous cell carcinoma of the floor of the mouth. Methods: A patient-specific titanium additively manufactured subperiosteal jaw implant (AMSJI) made of biocompatible titanium was designed using a digital planning workflow. Implant placement was performed in a single surgical session under general anesthesia, with fixation using osteosynthesis screws. A screw-retained full-arch provisional prosthesis was delivered intraoperatively, allowing immediate loading with adjustments aimed at avoiding compression of the healing soft tissues. Results: The patient achieved satisfactory functional and esthetic rehabilitation. Postoperative follow-up showed overall favorable mucosal tolerance; an early, limited peri-abutment mucosal dehiscence was observed and managed with suturing under local anesthesia, without compromising implant stability. Conclusions: This case highlights the clinical interest of patient-specific titanium subperiosteal implants as a fixed rehabilitation option in post-oncologic patients with major osseous and mucosal constraints and a history of reconstructive procedures. The combination of accurate digital planning and custom-made manufacturing may avoid the need for extensive bone grafting. However, these findings should be interpreted with caution due to the short-term follow-up and the inherent limitations of a single-case report, which limit the level of evidence and generalizability. Full article

The reconstruction of complex orbitomaxillary defects requires biomaterials that can simultaneously provide structural stability, biocompatibility, and accurate restoration of facial volume and contour. While rigid polymers such as polyetheretherketone (PEEK) offer reliable mechanical support, they do not adequately replicate the viscoelastic behavior of soft tissues. This report presents a translational revision case employing a personalized hybrid biomaterial approach that combines a 3D-printed PEEK implant for structural orbital floor support with a patient-specific polydimethylsiloxane (PDMS) implant for malar volumetric augmentation. Reconstruction was planned using CT segmentation and contralateral mirroring. Patient-specific implants were subsequently designed using CAD/CAM techniques, combining a rigid PEEK implant for structural orbital support with a flexible PDMS implant for malar volumetric augmentation with complementary mechanical properties. Revision surgery included the removal of inadequately positioned titanium hardware, the release of incarcerated extraocular muscles, and the restoration of orbital anatomy and facial symmetry. Postoperative imaging demonstrated stable implant positioning and sustained orbitomaxillary stability. Despite successful anatomical reconstruction, residual functional sequelae, including strabismus related to the severity of the initial orbital trauma, persisted and were addressed separately in a staged manner, resulting in satisfactory ocular alignment and resolution of diplopia in primary gaze. This case underscores the complementary functional roles of rigid and elastic polymers and highlights the translational potential of PDMS as a permanent, patient-specific implant material for volumetric and contour restoration in craniofacial reconstruction. Full article

Orbital floor fractures may cause long-term functional and esthetic impairments. Diplopia due to impaired function of the inferior rectus muscle is frequently an indication for surgical repair, but some cases, such as those where the diagnosis has been delayed or a previous attempt at repair has been made, may not always be amenable to surgical correction. It is advantageous for the surgeon to know whether the proper function of the inferior rectus muscle can be restored for the purposes of surgical planning and prognostication. The authors hypothesized that real-time MRI could be used to characterize the appearance of the inferior rectus muscle in a way that would facilitate future analysis of inferior rectus function in patients with diplopia due to orbital floor fractures. Real-time MRI was performed on 10 volunteer participants with normal ophthalmic function and orbital anatomy to assess inferior rectus appearance during vertical duction testing. ImageJ software was used to measure and record characteristics of the inferior rectus muscle, viewed in a quasi-sagittal plane. The ratios evaluated included inferior rectus muscle length in upgaze versus downgaze (UDR, mean 1.58) as well as inferior rectus muscle length versus distance from inferior rectus origin to inferior rectus inflection point in upgaze (LIR, mean 1.30) and downgaze (mean 1.20). These values were found to be conserved between orbits and individuals. This data offers quantitative insight regarding inferior rectus muscle appearance across the full arc of vertical gaze in healthy individuals. We plan to use this normative baseline dataset as a comparison for future phases of this project, using real-time MRI to evaluate traumatized orbits with diplopia and derangement of the inferior rectus muscle. Full article

Soft-ground-story configurations in high-rise buildings present a critical vulnerability during seismic events, often leading to disproportionate structural damage and collapse. This study focuses on the systematic identification of soft-ground-story high-rise structures in Bucharest, a city located in a high seismic hazard zone influenced by Vrancea intermediate-depth earthquakes. The research employs a multi-step methodology combining field surveys, structural documentation, and analysis of architectural layouts from various sources to detect soft-ground-story irregularities across the urban building stock in Bucharest. The findings reveal that such configurations remain prevalent in mixed-use structures along major boulevards, where open ground floors were historically favoured for commercial purposes. The results provide a database of soft-ground-story high-rise buildings in Bucharest, highlighting their prevalence in distinct urban districts and their potential impact on seismic risk. Quantitative screening indicators, vertical element area ratio and mean axial stress in ground-story columns, are proposed for rapid vulnerability assessment. Dynamic measurements confirm a 33–38% increase in fundamental eigenperiods after the 1977 earthquake, indicating moderate-to-extensive damage states. These findings underscore the urgent need for targeted retrofitting strategies and inform seismic risk mitigation policies. The study provides a foundation for future integration of advanced diagnostic tools, such as image-based deep learning and vibration monitoring, into citywide seismic resilience planning. Full article

The reinforcement and renovation of existing buildings constitute an important component of the future development of the civil engineering industry. Such projects typically require the original construction drawings of the building. However, for older structures, the original paper-based drawings may be damaged or lost. Moreover, traditional manual surveying and mapping methods are time-consuming, labor-intensive, and limited in accuracy. To address these issues, this paper proposes a floor plan generation method for existing buildings that integrates deep learning and stereo vision based on a fusion of synthetic and real data. First, collaborative modeling and automated rendering between a large language model and Blender are implemented based on the Model Context Protocol (MCP), enabling indoor scene modeling and image acquisition to construct a synthetic dataset containing structural components such as doors, windows, and walls. Meanwhile, manually annotated real indoor images are incorporated. Synthetic and real data are mixed in different proportions to form multiple dataset configurations for model training and validation. Subsequently, the SegFormer model is employed to perform semantic segmentation of indoor components. Combined with stereo camera calibration results, disparity computation is conducted to extract the three-dimensional spatial coordinates of component corner points. On this basis, the architectural floor plan is generated according to the spatial geometric relationships among structural components. Experimental results demonstrate that the proposed method effectively reduces the need for manual annotation and on-site measurement, providing an efficient technical solution for indoor floor plan generation of existing buildings. Full article

Integrating artificial intelligence (AI) into residential architectural design faces challenges due to fragmented workflows and the lack of localized datasets. This study proposes the CorbuAI framework, hypothesizing that a multimodal AI system integrating Pix2pix-GAN and Stable Diffusion (SD) can streamline the transition from floor plan generation to elevation and interior design within a specific regional context. We developed a custom dataset featuring 2335 manually refined Chinese residential floor plans and 1570 elevation images. The methodology employs a specialized U-Net V2.0 generator for functional layout synthesis and an SD-based model for stylistic transfer and elevation rendering. Evaluation was conducted through both subjective professional scoring and objective metrics, including the Perceptual Hash Algorithm (pHash). Results demonstrate that CorbuAI achieves high accuracy in spatial allocation (scoring 0.88/1.0) and high structural consistency in elevation generation (mean pHash similarity of 0.82). The framework significantly reduces design iteration time while maintaining professional aesthetic standards. This research provides a scalable AI-driven methodology for automated residential design, bridging the gap between schematic layouts and visual representation in the Chinese architectural context. Full article

Background: Early and accurate diagnosis remains crucial to improving outcomes in oral cancer. Optical coherence tomography (OCT) offers real-time, high-resolution imaging that may support diagnosis and treatment planning in oral squamous cell carcinoma (OSCC). Methods: In this prospective study, preoperative OCT scans were obtained from 68 histologically confirmed OSCC lesions, with 30 paired adjacent mucosa samples from the same patients as histologically negative comparators (diagnostic dataset: 98 lesions). OCT findings were compared with histopathology for diagnostic performance, OCT biomarker patterns by tumour grade, tumour depth measurement, margin assessment, and subsite-specific performance. Results: OCT demonstrated 98.5% sensitivity, 96.7% specificity, and an AUC of 0.98 for detection of invasive OSCC. OCT biomarkers—including abnormal epithelial architecture with variable epithelial thickness, stratification loss, basement membrane disruption, and increased subepithelial reflectivity—varied systematically with tumour differentiation grade. Tumour depth measurements showed acceptable agreement with histology, while margin definition was correct in 80% of cases. Performance was highest in the tongue and the floor of the mouth, with reduced performance in posterior/keratinised subsites. Image artefacts occurred in 5.1% of scans. Conclusions: OCT provides a reproducible, real-time adjunct for diagnosis, margin planning, and lesion stratification in OSCC, with recognised limitations related to light attenuation and operator-dependent factors. Multicentre validation and integration with digital interpretation platforms are warranted. Full article

Seismic monitoring is a crucial step in ensuring the safety and resilience of building structures. The implementation of effective monitoring systems, particularly across large-scale, complex building clusters, is currently hindered by the limitations of traditional sensor placement methods, which suffer from low efficiency, high subjectivity, and difficulties in replication. This paper proposes an innovative AI-based Automated Layout Method for seismic monitoring devices, leveraging building geometric recognition to provide a scalable, quantifiable, and reproducible engineering solution. The core methodology achieves full automation and quantification by innovatively employing a dual-channel approach (images and vectors) to parse architectural floor plans. It first converts complex geometric features—including corner coordinates, effective angles, and concavity/convexity attributes—into quantifiable deployment scoring and density functions. The method implements a multi-objective balanced control system by introducing advanced engineering metrics such as key floor assurance, central area weighting, spatial dispersion, vertical continuity, and torsional restraint. This approach ensures the final sensor configuration is scientifically rigorous and highly representative of the structure’s critical dynamic responses. Validation on both simple and complex Reinforced Concrete (RC) frame structures consistently demonstrates that the system successfully achieves a rational sensor allocation under budget constraints. The placement strategy is physically informed, concentrating sensors at critical floors (base, top, and mid-level) and strategically utilizing external corner points to maximize the capture of torsional and shear responses. Compared with traditional methods, the proposed approach has distinct advantages in automation, quantification, and adaptability to complex geometries. It generates a reproducible installation manifest (including coordinates, sensor types, and angle classification) that directly meets engineering implementation needs. This work provides a new, efficient technical pathway for establishing a systematic and sustainable seismic risk monitoring platform. Full article