臺灣博碩士論文加值系統

在急診室中治療出血性中風是與時間賽跑的過程。提高決策的準確性和執行醫囑的速度將有助於更及時地治療病患，從而提高治療品質和恢復情況。

近年來，臨床決策支持系統（CDSS）結合人工智慧（AI）於醫療領域的應用越來越多，本研究證明，CDSS和醫生之間的關係不是競爭或相互排斥。

本研究有兩個主要目標。 第一，驗證在獲得疑似腦內出血（ICH）患者的CT掃描圖像后，立即利用AI自動輔助解讀，是否能降低臨床誤診率。 第二，在急診科（ED）的真實環境中測試和導入這種人工智慧工具，並驗證其在優化臨床工作流程方面的真實表現。

這項臨床試驗研究的假設是，帶有深度學習（DL）的無顯影電腦斷層掃描（NCCT）可協助檢測ICH，從而加快急診科的臨床效率，減少病患於急診科的停留時間（LOS)。

本研究共納入了2,999名患者（最終診斷為ICH和無ICH的患者分別為188名和2,811名)。我們的研究旨在驗證使用人工智慧軟體工具DeepCT是否能縮短ICH患者在急診科的停留時間。

臨床結果顯示，在急診室導入DeepCT系統後，最終診斷為ICH的患者的LOS明顯縮短（使用DeepCT的 560.67±124.12分鐘與不使用DeepCT的780.83±147.35分鐘; P = 0.0251)。

Treating hemorrhagic stroke is a race against time in the emergency room. Improving the accuracy of decision-making and the speed of executing doctor's orders will help treat patients more promptly, and consequently improving the quality of treatment and recovery situation.

In recent years, there have been increasing applications of clinical decision support system (CDSS) combined with artificial intelligence (AI) for emergency diagnosis in the medical field, and this study proved that the relationship between CDSS and physicians is not competitive or mutually exclusive.

The present study had two main objectives. First, to verify whether the clinical misdiagnosis rate can be reduced when AI is used to automatically assist in interpreting the CT scan images of patients with suspected intracerebral hemorrhage (ICH) in the emergency process immediately after obtaining them. Second, to test and implement this AI tool in a real environment in the emergency department (ED), and subsequently verify its real-world performance in optimizing clinical workflow.

The hypothesis of this clinical pilot study is that non-contrast computerized tomography (CT) (NCCT) with deep learning (DL) will assist in the detection of ICH, thereby accelerating the clinical efficiency of the emergency department and reducing the length of stay (LOS) in the emergency department.

A total of 2,999 patients were included in the study (188 and 2,811 patients with a final diagnosis of ICH and without ICH, respectively). Our study aimed to verify whether the LOS of ICH patients in the ED was shortened by using AI software tool DeepCT.

Clinical outcomes showed that after the DeepCT system was implemented in the ED, the LOS of patients with a final diagnosis of ICH was significantly shortened (560.67 ± 124.12 min with DeepCT vs. 780.83 ± 147.35 min without DeepCT; P = 0.0251).

Acknowledgments………………………………………………………………………………………………………………………………………………………………i
Disclosure……………………………………………………………………………………………………………………………………………………………………………ii
Table of Contents…………………………………………………………………………………………………………………………………………………………iii
List of tables…………………………………………………………………………………………………………………………………………………………………iv
List of figures………………………………………………………………………………………………………………………………………………………………v
Abstract…………………………………………………………………………………………………………………………………………………………………………………vi
Chinese Abstract……………………………………………………………………………………………………………………………………………………………vii

Chapter I
Introduction………………………………………………………………………………………………………………………………………………………………………1
Chapter II
Research Focus…………………………………………………………………………………………………………………………………………………………………3
Chapter III
Introduction of DeepCT……………………………………………………………………………………………………………………………………………5
Chapter IV
Study Design………………………………………………………………………………………………………………………………………………………………………11
Chapter V
Results……………………………………………………………………………………………………………………………………………………………………………………18
Chapter VI
Discussions…………………………………………………………………………………………………………………………………………………………………………24
Bibliographies
References……………………………………………………………………………………………………………………………………………………………………………35

List of Tables
Table 1 The real clinical performance of the DeepCT system……………………………………………21
Table 2 Data with final diagnosis confirmed as ICH…………………………………………………………………22
Table 3 Data with final diagnosis determined as without ICH…………………………………………23

List of Figures
Figure 1 DeeCT- An AI-Powered Brain CT CDSS……………………………………………………………………………………5
Figure 2 The result of the ROC Curve of the DeepCT system………………………………………………9
Figure 3 A schematic of DeepCT deep learning system………………………………………………………………10
Figure 4 The study design of ED workflow with and without AI Assistant……………16
Figure 5 Accelerating Health Care Transformation with Lean and Innovation……26
Figure 6 Lean Healthcare Wastes……………………………………………………………………………………………………………………27
Figure 7 The clinical feedback from the physicians…………………………………………………………………29

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