在胸腔手術中，使用雙腔氣管內管(DLT)可以便利而有效地達成單肺呼吸的處置。醫師能夠透過此方法控制手術側的單肺塌陷，用以輔助胸腔與縱膈區域的手術操作。對麻醉科醫師而言，如何選擇最適合病人的DLT大小與插管深度是十分重要的，因為要做出正確的插管處置很大程度上仰賴於此，而不正確的DLT置放有可能導致呼吸道損傷、單肺分隔失敗、或是因堵塞次級支氣管而造成的低血氧症等問題。然而在如何選擇合適的DLT大小與插管深度這兩個問題上，一般均仰賴於醫師本身的經驗，沒有基於客觀證據的標準。在本次的回顧性研究中，我們收集了曾於2013-2021年間在台南市立安南醫院接受過胸腔鏡/胸腔手術與DLT插管的病人數據，再加上從他們的影像學資料中測得的呼吸道數據，並以此訓練機器學習模型來預測最適合病人的DLT大小與插管深度。透過資料分析，與預測DLT大小和插管深度最相關的幾個參數為性別、身高、體重、氣管直徑與胸圍。機器學習的結果顯示回歸決策樹模型在預測DLT大小上表現較佳(R2 = 0.51, RMSE = 1.45, MAE = 1.01)，而線性回歸模型加上隨機抽樣一致算法(RANSAC)在預測插管深度上則表現較佳(R2 = 0.81, RMSE = 0.60, MAE = 0.51)。然而因為樣本數過少的限制，我們認為如果進一步擴大樣本數，這些模型的預測力都還有再進步的空間。相信若是能夠在臨床上使用這些預測模型，或許能為臨床人員在選擇DLT大小與決定插管深度時提供一種額外的客觀參考標準。

In thoracic surgery, double lumen endotracheal tubes (DLTs) provide surgeons with an effective method to achieve one lung ventilation. This technique allows controlled collapse of the operative lung, and facilitates surgical access into the thoracic and mediastinal area. However, incorrect placement of the DLT during surgery can cause problems such as airway trauma, failure of lung isolation, or hypoxemia resulting from accidental blockage of a secondary bronchus. Since correct tube placement depends a lot on using the right sized DLT and placing it to an adequate depth, how to decide the best fit DLT size and intubation depth for a patient is crucial for anesthesiologists; yet a lack of evidence-based guidelines for choosing DLT size and intubation depth has always been a problem. In this retrospective study, we tried to predict the best fit DLT size and intubation depth via machine learning models based on the demographic data and tracheobronchial tree measurements of thoracotomy/thoracoscopy patients who received DLT intubation at An-Nan Hospital during 2013-2021. We found the most relevant parameters on predicting tube size and intubation depth were the patient’s sex, weight, height, transverse diameter of trachea, and chest circumference. Results showed that the decision tree regression model better predicted a patient’s best fit tube size (R2 = 0.51, RMSE = 1.45, MAE = 1.01), while multiple linear regression with RANSAC better predicted the intubation depth (R2 = 0.81, RMSE = 0.60, MAE = 0.51); yet we presume there is still room for improvement on the models’ predictivity, which can be achieved by further increasing the size of the sample data set. We believe that these predictive models may provide clinicians with a more objective alternative reference when it comes to deciding DLT size and intubation depth in a clinical setting.

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