背景: 台灣缺乏關於第一波新冠肺炎疫情的研究。本篇研究新冠肺炎重症的死亡危險因子與建立胸部X光的人工智慧的判讀模型。 方法: 本篇回溯性分析在西元二零二一年五月十五日至七月十五日之間在台北慈濟醫院的病歷資料。所有個案皆為插管使用呼吸器的病患。每一位病患都收錄四張胸部X光片，分別為第一張，插管前，插管後以及最嚴重。我們以移動網路第三版的方法來訓練人工智慧判讀模型，並且以交叉驗證方法來評估模型的表現。 結果: 本篇總共收錄了六十四位病患。整體死亡率為百分之四十五。從症狀發生到插管平均為八日。使用升壓藥，嚴重的X光指標（BRIXIA評分系統）是死亡的危險因子。人工智慧模型有準確的死亡預測能力，其四類X光的預測準確度值分別為0.88，0.92，0.92，0.94。 結論：呼吸衰竭而插管的新冠肺炎病患有高死亡率。使用升壓藥，嚴重的X光指標是死亡的危險因子。人工智慧模型有準確的死亡預測能力。 ;Background: The data of the first episode of the COVID-19 pandemic in Taiwan is scarce. We researched the risk factors of death among mechanically-ventilated patients with COVID-19 in Taiwan during the first episode of COVID-19. In addition, we are inspired to create a new artificial-intelligence-based death prognostication model by utilization of chest X-ray. Method: We retrospectively extracted the medical data of patients with COVID-19 at Taipei Tzu Chi Hospital from May 15th to July 15th in 2021. We recruited patients who received invasive mechanical ventilation. The chest X-ray images of each recruited patient were assigned into four groups (first, before-intubation, post-intubation, and worst). The BRIXIA and percent opacification scores were reviewed by two pulmonologists. To set up a prognostication model, we used the MobilenetV3-Small model with “ImageNet” pretrained weights, followed by high Dropout regularization layers. We practiced the model with Five-Fold Cross-Validation to assess model efficacy. Result: We finally recruited sixty-four patients. The overall death rate was forty-five percent. The median days since symptom commencement to endotracheal intubation was eight. Age, inferior academic degree, occurrence of COVID-19 complications, and a more severe achievement of the worst chest X-ray were linked to a higher death risk. The accuracy of the first, pre-intubation, post-intubation, and worst chest X-ray by the artificial-intelligence model were 0.88, 0.92, 0.92, and 0.94 respectively.
