Automated interpretation of congenital heart disease from multi-view echocardiograms

Congenital heart disease (CHD) is the most common birth defect and the leading cause of neonate death in China. Clinical diagnosis can be based on the selected 2D key-frames from five views. Limited by the availability of multi-view data, most methods have to rely on the insufficient single view analysis. This study proposes to automatically analyze the multi-view echocardiograms with a practical end-to-end framework. We collect the five-view echocardiograms video records of 1308 subjects (including normal controls, ventricular septal defect (VSD) patients and atrial septal defect (ASD) patients) with both disease labels and standard-view key-frame labels. Depthwise separable convolution-based multi-channel networks are adopted to largely reduce the network parameters. We also approach the imbalanced class problem by augmenting the positive training samples. Our 2D key-frame model can diagnose CHD or negative samples with an accuracy of 95.4\%, and in negative, VSD or ASD classification with an accuracy of 92.3\%. To further alleviate the work of key-frame selection in real-world implementation, we propose an adaptive soft attention scheme to directly explore the raw video data. Four kinds of neural aggregation methods are systematically investigated to fuse the information of an arbitrary number of frames in a video. Moreover, with a view detection module, the system can work without the view records. Our video-based model can diagnose with an accuracy of 93.9\% (binary classification), and 92.1\% (3-class classification) in a collected 2D video testing set, which does not need key-frame selection and view annotation in testing. The detailed ablation study and the interpretability analysis are provided.Comment: Published in Medical Image Analysi

Evaluation of Acoustic Emission Monitoring of Existing Concrete Structures

An increasing number of existing structures are approaching the end of their technical service life. Therefore, monitoring these structures to get the information of their health condition, which is called structural health monitoring (SHM), is becoming more significant. In SHM, acoustic emission (AE) technique shows promising features in detection, localization and characterization of damage. This technique has been applied in many fields, such as steel structures, composite structures and concrete structures. In dealing with existing concrete structures, cracks can influence the features of AE signals when they are on the way from the defect to the sensor. Therefore, it may challenge the commonly-used notion of AE monitoring. The influence of crack on AE monitoring has not been sufficiently investigated. More quantitative assessment on the crack influence on AE signals is believed to be valuable for the evaluation of accuracy and reliability of AE monitoring of existing concrete structures. In this research, the influence of crack on AE signals has been studied quantitatively. Experiments on cracks with different opening have been performed to support the formulation of the signals. The results are then used as inputs for an analytical study on source localization influenced by a crack. A three-dimensional (3D) triangulation technique has been used for source localization on a modeled cracked concrete beam. Localization error can therefore be estimated for AE sources in the presence of existing cracks. Furthermore, the influence of a crack on the amplitude of AE signals has been discussed. With the demonstrated crack-induced errors and attenuations ranges, the results can provide insights to the accuracy and reliability of AE monitoring in practical situations

On-site inspection of a reinforced concrete structure deteriorated due to corrosion by means of acoustic emission and other techniques

status: publishe

Report on state-of-the-art data assessment and visualisation methods : Deliverable D3.1

Effective analysis and visualisation of data is critical for the efficient application of the data provided by carriageway and bridge condition monitoring technologies. It supports better decisions in relation to asset reliability, availability, safety, economy and environment. This report discusses the link between the data provided by monitoring technologies on the properties of assets and how the collected data can be analysed and visualised to provide value in decision support. The next step in the report is to use this understanding to develop an appraisal system which could enable technologies in the INFRACOMS technology database to be appraised (scored) in relation to their abilities for data analysis, visualisation, integration and use in decision support. The presented system is referred to as the D3.1 scoring system. It consists of four components covering data visualisation, data analysis, integration within current data architectures and potential for practical decision-making. The present D3.1 report primarily examines the components pertaining to data visualisation and data analysis, while the exploration of the other two components, data architecture and decision support, will be carried out in the D3.2 report. It is proposed that the D3.1 scoring system could be used to appraise the capability of monitoring technologies to support asset management decisions, and would become an integral component of the INFRACOMS Appraisal Toolkit. It will also be used to further filter the current INFRACOMS Technology Database 2.0 technologies as part of the Appraisal Toolkit as INFRACOMS completes the development of the toolkit/database within WP2

Appraisal methodology : Deliverable D2.1

This report represents INFRACOMS deliverable D2.1 Appraisal Methodology. It builds upon the deliverables of INFRACOMS Work Package 1 which identified the information needs, gaps and priorities of NRAs in terms of their approach to data collection and monitoring, and a list of current and emerging measurement technologies. This report includes a review of several commonly-used appraisal methodologies that can be used to evaluate the effectiveness, suitability and potential impact of new technologies for an organisation. These methodologies include Technology Readiness Levels (TRLs), Cost Benefit Analysis (CBA), Life Cycle Cost Analysis (LCCA), Risk Assessment, and Multi-Criteria Decision Analysis (MCDA). Elements of these commonly used methodologies are included in the INFRACOMS Appraisal Methodology. The report also includes key highlights from a workshop with NRAs conducted in January 2023 which also fed into the design of the appraisal methodology. The INFRACOMS Appraisal Methodology described here is designed around the technology use case, that is, a particular application of a technology by a NRA. It incorporates three core processes for Pre-Evaluation, Evaluation and Case Studies of technology use cases. It also includes processes for NRAs to define their strategic and technical priorities so that the appraisal process can be tailored to addressing their individual requirements, as identified from Work Package 1

Current Practice, Future need and Gap Analysis : Deliverable D1.1

This report is INFRACOMS first deliverable D1.1. It addresses the “Understanding of information needs and gaps” component of the project. The aim has been to identify the current priorities and future needs of NRAs for the management of carriageway and bridge assets, specifically in terms of their approach to data collection and monitoring. The approach has been to establish existing knowledge via a review of previous projects, current best practices and standards in data collection and inspection, and a review of current business processes, NRA strategies around data collection and digitalisation etc. The report identifies a set of key imperatives for carriageway and bridge assets covering Availability, Reliability, Environment, Economy and Safety. Each of these is supported by the collection of key condition data, which is used to report technical parameters and performance indicators that can be combined to assess the ability of the asset to meet its key imperatives. A wide range of technologies are identified, which are currently applied to collect the data that supports this assessment. The consultation shows that there are also gaps between the desired and the current capability for the assessment of these assets. These include gaps in the data, challenges in the ability to collect the data, gaps in the application of the data that is already collected etc. A review of emerging technologies shows that there are tools and technologies that could help to fill these gaps. These could overcome the limitations of current technologies, better integrate new data sources, provide greater flexibility in using current and new data, and provide better analysis. They include remote sensing, Internet of Things (IoT), crowdsourcing, and advanced data processing/visualisation

Integration of New and Emerging Technologies into Data Architectures : Deliverable D3.2

To appraise the ability to integrate the data provided by a specific technology into an existing data architecture this report commences with the development of an approach to describe the "ideal" data architecture, that can integrate various types of data from new and emerging technologies to facilitate decision making. The data architecture forms a pipeline from raw data creation/delivery to data ingestion, data organization, data analysis and visualisation, until information that is useful for decision making. We then review two existing data architectures as examples in the context of the proposed data architecture pipeline. From the understanding of the two sides – the data properties of technologies and the capabilities of data architectures – we develop an appraisal scoring process to evaluate the ability to integrate the new data into the existing data architecture. To generalize this approach, the report presents a list of questions that can be used by stakeholders to help understand the data architecture used by any NRA (not only limited to the selected examples) when conduct the appraisal. We also develop an appraisal scoring process to evaluate the potential of the technologies to support practical decision making.The outcomes in this report (D3.2) and the previous one (D3.1), complete the INFRACOMS appraisal (scoring) system for the aspects of: data analysis, visualisation, integration into data architecture and potential support for decision making (forming part of the overall appraisal process). An example application of the process is presented for the case of acoustic emission monitoring the wire break in steel cables. In addition, the process has been applied to further technologies in the INFRACOMS database 1.0, and provided in the appendix. It is anticipated that refinement, and further guidanc