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- W3211291973 abstract "Pressure ulcers (PUs) are a common problem associated with great morbidity and cost. Because of the lack of professional personnel in the institutions or home care system, the diagnosis and treatment of PUs were sometimes delayed. We aim to develop an automatic tissue classification and severity evaluation system of PUs using AI technology. All PU images were collected from patients in the Far Eastern Memorial Hospital (FEMH) in recent 3 years and labeled according to different tissue types. The labeled images were used to train the tissue segmentation model with U-net convolutional neural network (CNN). The percentage of the different tissue can be calculated automatically and would be compared with the manually labeled one. The output of tissue classification was transformed to a clinical decision support system (CDSS) with a rule-base formula. Our study showed that proposed system can classify each tissue automatically and reach an accuracy about 93.6%. However, the CDSS's accuracy rate was only 64% for the referral suggestion and 62% for the care recommendation. Hence, the system should be modified with more data and information." @default.
- W3211291973 created "2021-11-08" @default.
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- W3211291973 date "2021-05-14" @default.
- W3211291973 modified "2023-10-16" @default.
- W3211291973 title "A Clinical Decision Support System of Pressure Ulcers Tissue Classification" @default.
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- W3211291973 doi "https://doi.org/10.1145/3472813.3473215" @default.
- W3211291973 hasPublicationYear "2021" @default.
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