瞳孔大小會隨著進入眼睛的光量而變化，這個過程稱為瞳孔光反射。醫護人員通過觀測瞳孔光反射來評估患者的生理狀態，病情和找尋病因。在臨床上，醫護人員通過觀察因筆燈的光刺激而促使的瞳孔收縮的幅度與速度，判斷出瞳孔光反射的等級。由於瞳孔光反射的分級缺乏定量的定義與標準的臨床協議，再加上醫護人員經驗水平的不同或其他因素的影響，醫護人員依靠視覺觀測的主觀評估存在著評估者間與評估者內的評估一致性不足的問題。這也導致醫護人員無法正確追蹤並瞭解患者的狀況。

本研究提出了一種基於卷積神經網路與長短期記憶或雙向長短期記憶建構的深度學習演算法，實現客觀的自動化瞳孔光反射量測。本研究與臺灣林口長庚紀念醫院的兩位眼科醫師合作，通過臨床試驗獲取資料集。本研究使用團隊開發的便攜式瞳孔計獲得記錄了受試者的瞳孔光反射影片，並將影片作為資料集。瞳孔光反射的分級標籤由合作的兩位眼科醫師通過觀察影片後提供，作為資料集的基本事實。

本研究使用人工智慧技術的深度學習模型從資料集中學習兩位眼科醫師潛在的分級標準與規則，從而實現更準確與可重複的瞳孔光反射量測，輔助醫師進行診斷，減輕醫護人員的負擔並提高醫療品質。

本研究使用通過臨床試驗得到的124筆資料，訓練出三種分級方式（精細分級，粗略分級和二元分級）的模型。最後，依據模型的測試結果得知，粗略分級與二元分級的模型更能勝任這份工作。在未來，可將模型移植到便攜式瞳孔計上，實現瞳孔光反射量測功能，方便醫護人員迅速地爲受試者進行瞳孔光反射的檢查。

Pupil size changes with the amount of light entering the eye, this process is called Pupillary Light Reflex. Medical staff evaluate the patient's physiological state, and condition and find the cause by observing the pupillary light reflex. Clinically, medical staff judge the grade of pupillary light reflex by observing the amplitude and speed of pupil constriction induced by the light stimulation of the penlight. Due to the lack of quantitative definitions and standard clinical protocols for the pupillary light reflex measurement, coupled with differences in the experience of medical staff or other factors, the medical staff's subjective assessment relying on visual observations has the problems with the interrater and intrarater reliability. This also results in the inability of the medical staff to properly track and understand the patient's condition.

This study proposes a deep learning model based on Convolutional Neural Network and Long Short-Term Memory or Bidirectional Long-Short-Term Memory to achieve an objective and automated pupillary light reflex measurement. This study cooperates with two ophthalmologists at Chang Gung Memorial Hospital in Linkou, Taiwan to obtain the clinical trial dataset. For this study, a portable pupillometer developed by our team is used to obtain the videos recording the pupillary light reflexes. The videos are served as the dataset. The graded labels of pupillary light reflexes are provided by two collaborating ophthalmologists after watching the videos. The graded labels are served as the ground truth for the dataset.

This study uses the deep learning model of artificial intelligence technology to learn the potential grading standards and rules of two ophthalmologists from the dataset. It can achieve a more accurate and repeatable pupillary light reflex measurement to assist physicians in the diagnosis, reduce the burden on the medical staff, and improve the quality of medical care.

This study uses 124 data that is obtained through the clinical trials to train the models with three grading methods (fine grading, coarse grading, and binary grading). Finally, according to the test results of the models, it is known that the coarse grading and binary grading models are more suitable for the job. In the future, the model can be transplanted to the portable pupillometer to realize the function of pupillary light reflex measurement, which is convenient for the medical staff to perform a pupillary light reflex examination for subjects quickly.

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