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巡迴體檢中心導入資訊化作業流程人力探討

為了解決Workflow diagram的問題，作者羅佳玲 這樣論述：

中文摘要 iAbstract ii表目錄 v圖目錄 vi第一章 緒論 1第一節 研究背景與動機 1第二節 研究目的 6第二章 文獻探討 7第一節 勞工健康檢查 7第二節 醫療資訊化系統 12第三節 以資訊化系統改善作業流程之應用 15第三章 研究方法 19第一節 研究設計 19第二節 研究流程 23第三節 研究對象 23第四章 研究結果 25第一節 個案醫院介紹 25第二節 個案研究步驟 30第三節 資訊化作業規劃 40第四節 效益評估 44第五章 結論與建議 46第一節

結論 46第二節 管理意涵 46第三節 建議 47第四節 研究限制 48參考文獻 50附件 58表目錄表一 受訪者名單 24表二 巡迴體檢中心客戶結構 28表三 巡迴體檢現場各站別作業面問題彙整 34表四 巡迴體檢業務內容分析 35表五 巡迴體檢中心企業合作產業 36表六 巡迴體檢中心護理師及庶務員人力時間計算 39表七 資訊化作業流程彙整 40表八 廠商規劃新增資訊化功能 42表九 巡檢資訊化改善後人力節省 44圖目錄圖一 巡迴體檢中心年度收入與人力編制 26圖二 巡迴體檢中心收入及服務量 26圖三 2020 年及 2021 年巡迴體檢中心收入影響 27

圖四 巡迴體檢作業流程 29圖五 巡迴體檢現場作業流程模擬圖 29圖六 特性要因圖 38圖七 巡檢資訊化系統架構圖 43

運用深度學習於膝關節損傷核磁共振影像之人工智慧偵測與診斷模型

為了解決Workflow diagram的問題，作者TRUONG NGUYEN KHANH HUNG 這樣論述：

Introduction: Efficient and accurate detection is vital for the diagnosis and treatment of knee injuries. In recent years, there is an increase in interest in deep learning (DL) approaches to detecting knee injuries in magnetic resonance imaging (MRI). Studies have shown that DL models are capable

of reaching the same level as human radiologists when it comes to sensitivity and specificity, while at the same time requiring significantly less training time. Current Artificial Intelligent (AI) - based systems are, however, still limited by many different factors, such as unbalanced classes in t

raining data, or the nature of these systems which makes false positives and false negatives almost an inevitability. There are multiple routes for improving upon the existing DL knee injury detection models. As they continue to become more and more advanced, it is expected that the use of these sys

tems will become more popular in the future.Method: In this study, we create multi models based on machine learning (ML) and DL algorithms to perform classification, recognition, and segmentation tasks on knee MRI. In which the two most important components in the knee joint in this study are the an

terior cruciate ligament (ACL) and meniscus.The first model, based on the DenseNet 121 neural network structure, was used to classify images with or without ACL injury. The dataset includes 799 knee MRI reports from Cho Ray Hospital (Vietnam). These MRI data were obtained from previous work in the h

ospital, containing knee MRI reports from 5 years (January 1st, 2015 – December 31st, 2019)Using the Faster-region convolutional neural network (Faster - RCNN) and several convolutional neural networks (CNN) backbone tests, such as VGG-16, Res-Net50, DenseNet-121, EfficientNet-B0, and EfficientNetV2

- B0 algorithms, the second group of models can recognize the ACL on knee MRI as a function of the typical imaging characteristics. This research collected 256 knee MRI examinations performed at Cho Ray Hospital, Ho Chi Minh City, Vietnam, between January 1, 2018, and December 31, 2020 (including t

raining and testing datasets).The third model focuses on automatic identification and classification of meniscus based on the Yolo-v4 object detection model. At the same time, the lesion location is also shown on images by the GRAD-CAM technique. The total number of subjects used in this study was 7

04 patients, including meniscus lesions and the control group. All MRIs in this study were collected before the surgery, and all had no prior surgical history. The MRI scanner at Cho Ray Hospital is MAGNETOM Skyra 3T (Siemen), and at Hoan My Hospital is 3.0T MRI Scanners SIGNA (GE Healthcare). In ad

dition, we also used a public dataset - MRNet dataset (validation dataset) from Stanford University Medical Center with 120 examinations for external testing.Results: The area under the ROC curve (AUC) for the ACL injury classification system was 80.63% with the axial plane and around 78% with both

the sagittal and coronal planes, respectively. All sensitivity and specificity point estimates of the proposed ACL injury detection system were all over 96%, indicating no significant differences in diagnostic performance between different planes.Our DL model detected meniscus tears with 91.4% accur

acy on the internal testing dataset, 89.2% accuracy on the external validation dataset, and 79.9% accuracy on the MRNet dataset, respectively. The meniscus tears were visualized by auto-enlarging the detection area and Grad-CAM images.Conclusion: This report describes the various approaches in knee

MRI experiments to provide different AI models for the prediction of knee injuries. The CNN model applied to classify injured ACL images had high sensitivity and specificity, showing that using a simple structured 2D-CNN is more effective for small datasets and can assist non-experts in assessing th

e assessment of ACL injuries. The proposed model applied to detect meniscus lesions had high accuracy and specificity, showing that our model can assist non-experts in assessing the assessment of meniscus injuries.