• Journal of The Korea Institute of Healthcare Architecture
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• v.3 no.4
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• pp.59-68
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• 1997

This study is intended to review the Nucleus System of standard hospitals in U.K. The research is focused on the theoretical background, contents of Nucleus System, and the operational problems through the survey of sample hospitals. The contents of research include the development history of standardization, outlines, aimes, advantages, the data of Nucleus system, and the study-visits of sample hospitals. The conclusion could be summarized as follows ; 1) The form of standard hospitals is compact and low-rise the major movements are horizontal. The standard plans of the functional dpartments are unified as cruciform with $15m{\times}15m$ module. 2) The Nulceus System has been developed. The hospitals have 3 stories maximum and courtyards for natural light & ventilation. 3) The advantages of Nucleus System includes reduction of design & construction period, the buildability due to the repetitive construction, and the running cost. And the disadvantages are mentioned as the lack of storage, staff accomodation, pantry, and sanitary facilities. 4) Sample hospitals provide human scale, possibilities of growth & change, and curing environment from art decoration & artificial lake. 5) In case of Korean situation, even the minimum standardization such as hospital design guidelines should be developed in near future.

• The Korean journal of internal medicine
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• v.39 no.4
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• pp.555-562
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• 2024

This study reviews the recent progress of artificial intelligence for colonoscopy from detection to diagnosis. The source of data was 27 original studies in PubMed. The search terms were "colonoscopy" (title) and "deep learning" (abstract). The eligibility criteria were: (1) the dependent variable of gastrointestinal disease; (2) the interventions of deep learning for classification, detection and/or segmentation for colonoscopy; (3) the outcomes of accuracy, sensitivity, specificity, area under the curve (AUC), precision, F1, intersection of union (IOU), Dice and/or inference frames per second (FPS); (3) the publication year of 2021 or later; (4) the publication language of English. Based on the results of this study, different deep learning methods would be appropriate for different tasks for colonoscopy, e.g., Efficientnet with neural architecture search (AUC 99.8%) in the case of classification, You Only Look Once with the instance tracking head (F1 96.3%) in the case of detection, and Unet with dense-dilation-residual blocks (Dice 97.3%) in the case of segmentation. Their performance measures reported varied within 74.0-95.0% for accuracy, 60.0-93.0% for sensitivity, 60.0-100.0% for specificity, 71.0-99.8% for the AUC, 70.1-93.3% for precision, 81.0-96.3% for F1, 57.2-89.5% for the IOU, 75.1-97.3% for Dice and 66-182 for FPS. In conclusion, artificial intelligence provides an effective, non-invasive decision support system for colonoscopy from detection to diagnosis.