High scale 3D modelling and orthophoto of curved masonries for a multipurpose representation, analysis and assessment

It is important nowadays to underline some relevant topics concerning the effective contribution of 3D high detailed products derived from innovation and integration of Geomatics technologies, allowing a remarkable development in descriptive metric capabilities, supporting and improving the material recording, representation, analysis and characterization about alteration of the constructive systems. Considering the relevance of the complex interdisciplinary research of these issues that move around the Cultural Heritage safeguard and due to its extreme vulnerability, these models must give a response to different problems. Primarily they has to provide complete models on which to pursue accurate morpho-dimensional documentation, and to base structural assessment, decay investigations, and consequently to underpin restoration practices and support operational workflow in CH assets monitoring. Some peculiarities of new methods for semi-automatic processing algorithms are thus evidenced, advantaging their proficiency to behave as tools for a more sustainable approach in the general process of preservation and protection. Specifically about the ancient masonries documentation, the chance of using digital products derived from very high scale models, as the detailed orthoimages projection and surfaces development offers many opportunities. Here, a late-medieval stratified dovecote tower in Verolengo (TO) with a particular trunk-conical shape had been analysed in order to reconstruct an identity and a historical and architectural framework, de facto not recognized yet. A 3D reconstruction by dense matching techniques will be presented, in the complex context that are the vertical high buildings, presenting one of the highest level of vulnerability. The importance of the 3D model availability, closely connected to dense radiometric information, has been particularly expressed in two main direction for the diagnosis both of volumetric structure assessment and the material characterization of the mixed masonries walls

Sharing Data and Image Processing Pipelines: The Information Analysis & Management initiative

International audienc

CONDITION ASSESSMENT OF RC BRIDGES. INTEGRATING MACHINE LEARNING, PHOTOGRAMMETRY AND BIM

Abstract. The survey of building pathologies is focused on reading the state of conservation of the building, composed by the survey of constructive and decorative details, the masonry layering, the crack pattern, the degradation and the color recognition. The drawing of these representations is a time-consuming task, accomplished by manual work by skilled operators who often rely on in-situ analysis and on pictures. In this project three-dimensional an automated method for the condition survey of reinforced concrete spalling has been developed. To realize the automated image-based survey it has been exploited the Mask R-CNN neural network. The training phase has been executed over the original model, providing new examples of images with concrete cover detachments. At the same time, a photogrammetry process involved the images, in order to obtain a point cloud which acts as a reference to a Scan to BIM process. The BIM environment serves as a collector of information, as it owns the ontology to recreate entities and relationships. The information as extracted by neural network and photogrammetry serve to create the pictures which depict the concrete spalling in the BIM environment. A process of projecting information from the images to the BIM recreates the shapes of the pathology on the objects of the model, which becomes a decision support system for the built environment. A case study of a concrete beam bridge in northern Italy demonstrates the validity of the process.</p

Lidar data analyses for assessing the conservation status of the so-called baths-church in hierapolis of phrygia (TR)

The LiDAR technology has aroused considerable interest in the field of structural study of historical buildings, aimed at the structural assessment in the presence of different states of stresses and at the evaluation of the health status. The interest is due mostly by the ability of generating models of the built structures being able to predetermine different levels of schematization, two-dimensional and three-dimensional, in order to be able to perform evaluation processes assigning simplified geometric contents that correspond to the physical reality of the artefacts. This paper intends to report some results of these experiences applied in archaeological domain, to the so-called Baths-Church at Hierapolis in Phrygia (Pamukkale, TR). In particular, the generation of accurate models from dense clouds and their reduction to models with simplified geometries too, is explored, with the further aim of testing automated strategies for features detection and editing process that leads to appropriate models for visual and analytical structural assessment. The accuracy and density parameters of the LiDAR clouds will be analysed to derive orthophotos and continuous mesh models, both to obtain the best results from the application of research algorithms such as region growing to detect blocks, and to allow visual analysis on digital models and not on site. The ability to determine with high accuracy both the size and the anomalies of the wall systems (out of plumb and other rotation or local mechanisms of collapse), together with the possibility of identifying the lay of the individual drywall blocks and also the signs of cracks and collapses, allow deriving suitable models both for FE (Finite Elements) analysis and DE (Discrete Elements) analysis, as well as analytical ones

AI assisted reader evaluation in acute CT head interpretation (AI-REACT): protocol for a multireader multicase study

Introduction A non-contrast CT head scan (NCCTH) is the most common cross-sectional imaging investigation requested in the emergency department. Advances in computer vision have led to development of several artificial intelligence (AI) tools to detect abnormalities on NCCTH. These tools are intended to provide clinical decision support for clinicians, rather than stand-alone diagnostic devices. However, validation studies mostly compare AI performance against radiologists, and there is relative paucity of evidence on the impact of AI assistance on other healthcare staff who review NCCTH in their daily clinical practice. Methods and analysis A retrospective data set of 150 NCCTH will be compiled, to include 60 control cases and 90 cases with intracranial haemorrhage, hypodensities suggestive of infarct, midline shift, mass effect or skull fracture. The intracranial haemorrhage cases will be subclassified into extradural, subdural, subarachnoid, intraparenchymal and intraventricular. 30 readers will be recruited across four National Health Service (NHS) trusts including 10 general radiologists, 15 emergency medicine clinicians and 5 CT radiographers of varying experience. Readers will interpret each scan first without, then with, the assistance of the qER EU 2.0 AI tool, with an intervening 2-week washout period. Using a panel of neuroradiologists as ground truth, the stand-alone performance of qER will be assessed, and its impact on the readers’ performance will be analysed as change in accuracy (area under the curve), median review time per scan and self-reported diagnostic confidence. Subgroup analyses will be performed by reader professional group, reader seniority, pathological finding, and neuroradiologist-rated difficulty. Ethics and dissemination The study has been approved by the UK Healthcare Research Authority (IRAS 310995, approved 13 December 2022). The use of anonymised retrospective NCCTH has been authorised by Oxford University Hospitals. The results will be presented at relevant conferences and published in a peer-reviewed journal

LIDAR DATA ANALYSES FOR ASSESSING THE CONSERVATION STATUS OF THE SO-CALLED BATHS-CHURCH IN HIERAPOLIS OF PHRYGIA (TR)

The LiDAR technology has aroused considerable interest in the field of structural study of historical buildings, aimed at the structural assessment in the presence of different states of stresses and at the evaluation of the health status.The interest is due mostly by the ability of generating models of the built structures being able to predetermine different levels of schematization, two-dimensional and three-dimensional, in order to be able to perform evaluation processes assigning simplified geometric contents that correspond to the physical reality of the artefacts.This paper intends to report some results of these experiences applied in archaeological domain, to the so-called Baths-Church at Hierapolis in Phrygia (Pamukkale, TR). In particular, the generation of accurate models from dense clouds and their reduction to models with simplified geometries too, is explored, with the further aim of testing automated strategies for features detection and editing process that leads to appropriate models for visual and analytical structural assessment. The accuracy and density parameters of the LiDAR clouds will be analysed to derive orthophotos and continuous mesh models, both to obtain the best results from the application of research algorithms such as region growing to detect blocks, and to allow visual analysis on digital models and not on site.The ability to determine with high accuracy both the size and the anomalies of the wall systems (out of plumb and other rotation or local mechanisms of collapse), together with the possibility of identifying the lay of the individual drywall blocks and also the signs of cracks and collapses, allow deriving suitable models both for FE (Finite Elements) analysis and DE (Discrete Elements) analysis, as well as analytical ones.</p

AI assisted reader evaluation in acute CT head interpretation (AI-REACT): protocol for a multireader multicase study.

Introduction: A non-contrast CT head scan (NCCTH) is the most common cross-sectional imaging investigation requested in the emergency department. Advances in computer vision have led to development of several artificial intelligence (AI) tools to detect abnormalities on NCCTH. These tools are intended to provide clinical decision support for clinicians, rather than stand-alone diagnostic devices. However, validation studies mostly compare AI performance against radiologists, and there is relative paucity of evidence on the impact of AI assistance on other healthcare staff who review NCCTH in their daily clinical practice. Methods and analysis: A retrospective data set of 150 NCCTH will be compiled, to include 60 control cases and 90 cases with intracranial haemorrhage, hypodensities suggestive of infarct, midline shift, mass effect or skull fracture. The intracranial haemorrhage cases will be subclassified into extradural, subdural, subarachnoid, intraparenchymal and intraventricular. 30 readers will be recruited across four National Health Service (NHS) trusts including 10 general radiologists, 15 emergency medicine clinicians and 5 CT radiographers of varying experience. Readers will interpret each scan first without, then with, the assistance of the qER EU 2.0 AI tool, with an intervening 2-week washout period. Using a panel of neuroradiologists as ground truth, the stand-alone performance of qER will be assessed, and its impact on the readers’ performance will be analysed as change in accuracy (area under the curve), median review time per scan and self-reported diagnostic confidence. Subgroup analyses will be performed by reader professional group, reader seniority, pathological finding, and neuroradiologist-rated difficulty. Ethics and dissemination: The study has been approved by the UK Healthcare Research Authority (IRAS 310995, approved 13 December 2022). The use of anonymised retrospective NCCTH has been authorised by Oxford University Hospitals. The results will be presented at relevant conferences and published in a peer-reviewed journal. Trial registration number NCT06018545

La investigación sobre fármacos antipsicóticos atípicos en España: una evaluación bibliométrica

Objectives: We carried out a bibliometric study on the scientific publications in relation to atypical antipsychotic drugs (AADs) in Spain. Methods: We used the EMBASE and MEDLINE databases and we applied some bibliometric indicators of paper production and dispersion (Price’s law and Bradford’s law, respectively). We also calculated the participation index of the different countries and correlated the bibliometric data with some social and health data (total per capita expenditure on health and gross domestic expenditure on research and development). Results: We collected 656 original papers published between 1988 and 2011. Our study results fulfilled Price’s law with scientific production on AADs showing exponential growth (correlation coefficient r = 0.9693, vs. r = 0.9177 after linear adjustment). The most widely studied drugs were risperidone (181 papers), olanzapine (143), clozapine (94), and quetiapine (74). Division into Bradford zones yielded a nucleus occupied by the European Psychiatry and European Neuropsychopharmacology (70 articles). Totally 194 different journals were published, with 5 of the first 10 used journals having an impact factor being greater than 4. Conclusion: The publications on AADs in Spain have undergone exponential growth over the studied period, without evidence of reaching a saturation point

Microenvironmental Factors that Shape Bacterial Metabolites in Inflammatory Bowel Disease

Inflammatory bowel disease (IBD) is a significant global health problem that involves chronic intestinal inflammation and can involve severe comorbidities, including intestinal fibrosis and inflammation-associated colorectal cancer (CRC). Disease-associated alterations to the intestinal microbiota often include fecal enrichment of Enterobacteriaceae, which are strongly implicated in IBD development. This dysbiosis of intestinal flora accompanies changes in microbial metabolites, shaping host:microbe interactions and disease risk. While there have been numerous studies linking specific bacterial taxa with IBD development, our understanding of microbial function in the context of IBD is limited. Several classes of microbial metabolites have been directly implicated in IBD disease progression, including bacterial siderophores and genotoxins. Yet, our microbiota still harbors thousands of uncharacterized microbial products. In-depth discovery and characterization of disease-associated microbial metabolites is necessary to target these products in IBD treatment strategies. Towards improving our understanding of microbiota metabolites in IBD, it is important to recognize how host relevant factors influence microbiota function. For example, changes in host inflammation status, metal availability, interbacterial community structure, and xenobiotics all play an important role in shaping gut microbial ecology. In this minireview, we outline how each of these factors influences gut microbial function, with a specific focus on IBD-associated Enterobacteriaceae metabolites. Importantly, we discuss how altering the intestinal microenvironment could improve the treatment of intestinal inflammation and associated disorders, like intestinal fibrosis and CRC