Purpose: This paper presents a framework for simulation on IoT based CBM (condition based monitoring) for rolling stocks. This enables to allocate maintenance resources effectively while satisfying preventive maintenance requirements. Methodology/Approach:We exploits Reliability centered maintenance (RCM) based on KTX (Korea Tran eXpress, Korea's high-speed rail system) motor reduction unit failure data for three years by utilising the internet of things (IoT) and RAMS (Reliability, Availability, Maintainability, Safety) methods. Findings:We come up with the predictive maintenance indicator; reliability functions as to the desired service level; and the failure and defect prediction indicator takes the form of cumulative failure function in the form of probability distribution function, which aim to realise the real-time condition monitoring and maintaining technical support services. Internet of Things (IoT) has been an important apparatus to improve the maintenance efficiency.Research Limitation/implication: This paper has limitations that the data are collected from references, not actual data; the detailed descriptions of IoT application to the railway rolling stocks are omitted, and it is not dealt in depth how maintenance efforts and performance are improved through the suggested reliability centered maintenance. Originality/Value of paper:This study has the academic importance in a sense that it integrates RAMS based maintenance methods and IoT. RAMS centered maintenance provides powerful rules for deciding a failure management policy; when it is technically appropriate; and for providing precise criteria for deciding how often routine tasks should be carried out. It will lead to the improved cost efficiency, sustainability and maintainability of railway maintenance system since the staff do not have to visit installation sites frequently. Lately, there is general agreement that prevention was better than inspection and that an increase in preventive cost was the means of reducing total quality costs. In connection with this issue, we will address the way of reducing failure costs and prevention costs with IoT: new appraisal method.
Digital pathology incorporates the acquisition, management, sharing, and interpretation of pathological information, including slides and data, in a digital environment. Digital slides are created using a scanning device to capture a high-resolution image on glass slides for analysis on a computer or a mobile device. Though digital pathology has drastically grown over the last 10 years and has created opportunities to support specialists, few have attempted to address its full-scale implementation in routine clinical practice. To incorporate new technologies in diagnostic processes, it is necessary to study their application, the value they provide to specialists, and their effects on improvements across the entire workflow, rather than studying a particular element. In this study, we aimed to identify what have the current digital pathology systems contributed to the pathological and diagnostic process. We retrieved articles published between 2010 and 2020 from the databases PubMed and Google Scholar. We explored how digital pathology systems can better utilize existing medical data and new technologies within the current diagnostic workflow. While the evidence concerning the efficacy and effectiveness of digital pathology is mounting, high-quality evidence regarding its impact on resource allocation and value for diagnosis is still needed to support clinical diagnosis and policy decision-making.
Background Artificial intelligence (AI) research is highly dependent on the nature of the data available. With the steady increase of AI applications in the medical field, the demand for quality medical data is increasing significantly. We here describe the development of a platform for providing and sharing digital pathology data to AI researchers, and highlight challenges to overcome in operating a sustainable platform in conjunction with pathologists. Methods Over 3000 pathological slides from five organs (liver, colon, prostate, pancreas and biliary tract, and kidney) in histologically confirmed tumor cases by pathology departments at three hospitals were selected for the dataset. After digitalizing the slides, tumor areas were annotated and overlaid onto the images by pathologists as the ground truth for AI training. To reduce the pathologists’ workload, AI-assisted annotation was established in collaboration with university AI teams. Results A web-based data sharing platform was developed to share massive pathological image data in 2019. This platform includes 3100 images, and 5 pre-processing algorithms for AI researchers to easily load images into their learning models. Discussion Due to different regulations among countries for privacy protection, when releasing internationally shared learning platforms, it is considered to be most prudent to obtain consent from patients during data acquisition. Conclusions Despite limitations encountered during platform development and model training, the present medical image sharing platform can steadily fulfill the high demand of AI developers for quality data. This study is expected to help other researchers intending to generate similar platforms that are more effective and accessible in the future.
BACKGROUND High-quality learning materials are needed for artificial intelligence (AI) development, but are not practically available; this situation is especially poor in the medical field. In particular, annotating medical images (e.g., annotation for tumor area by pathologists) is massive as well as expensive, and subject to privacy protection. These are major limitations for AI developers to approach and reproduce medical image data. OBJECTIVE This study aimed to reduce barriers for AI researchers to access medical image datasets by collating and sharing high-quality medical images with pathologists, and to find applicable ways to apply diagnostic AI assistance to reduce the pathologists’ workload. METHODS Pathology slides of tumors of five organs (liver, colon, prostate, pancreas and biliary tract, and kidney) from histologically confirmed cases were selected for this study. After scanning the slides to obtain whole slide digital images, the patient information was de-identified, and annotation for the tumor area was performed by the pathologist. Next, an AI-assisted annotation process was used in parallel to improve the annotation workload of pathologists and to draw complex lesion boundaries more accurately. This allowed all the data to include the annotations confirmed by experienced pathologists, and to be used as an AI learning dataset. RESULTS A web-based data-sharing platform for AI learning was built, and was unveiled in 2019. In total, 3,100 massive datasets of 5 organ carcinomas were shared through this platform, and were accessible to all researchers. The platform had the advantage that users could search data visually and intuitively; except for commercial purposes, all researchers made free use of the provided dataset for their research. Finally, the platform also provided five image data pre-processing algorithms that could help AI modeling learners. CONCLUSIONS We built and operated a web-based data-sharing platform for AI researchers providing a high-quality digital pathology dataset personally annotated by pathologists. We hope that our experience will help researchers who want to build such a platform in future, by sharing issues gained from collecting and sharing these valuable data.
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