血紅素濃度是一個重要的健康與診斷指標，醫護人員欲判斷病人是否貧血、是否需要輸血，皆須確認病人的血紅素濃度。目前各醫院常用的全血細胞計數(CBC)檢測血紅素濃度的方法，需要抽取病人的靜脈血，送入實驗室做檢測，CBC檢測是利用流式細胞術的技術觀測血液樣本中的紅血球、白血球以及血小板數量來檢測血紅素濃度；此法費時，不適用於某些需要即時或連續檢測的臨床需求，且對於長期須抽血檢測的病人族群，常會因抽血疼痛而對檢驗有所抗拒，抽血造成的傷口也可能增加感染的風險。因此我們開發了一套手持非侵入式光學系統，搭配機器學習所建立的快速準確漫反射光學演算法，將測得之人體漫反射光譜，演算獲得組織吸收光譜，再經由比爾定律擬合吸收光譜，求得血紅素濃度。本論文將詳述系統架構與演算原理，並透過假體實驗與臨床實驗，在臨床上與CBC檢測的血紅素濃度呈現高度相關(r = 0.870)、平均偏移量為-0.006 g/dL、Limit of agreement(LOA)為3.36 g/dL(-1.62 - 1.62 g/dL)，驗證本系統的效能與血紅素計算的準確性。

Concentration of hemoglobin is an important index for healthcare professional to diagnosis anemia and make decision of transfusion. Complete Blood Count(CBC), which uses flow cytometry to count the number of red blood cell, white blood cell and platelet in the blood sample drawn from patient’s vein, is the main way to test the concentration of hemoglobin in hospitals. However, there are some disadvantages like time consuming, pain, infection. In this study, we develop an non-invasive and hand-held optical system with algorithm which combined Monte-Carlo method, artificial neural network and beer-lambert law to test the concentration of hemoglobin. We studied sample of patients in the emergency department who required a CBC in National Cheng Kung University Hospital. We used linear regression analysis and Bland-Altman analysis to compare hospital lab hemoglobin with our device hemoglobin. 24 subjects were enrolled in this study, comparison of hospital lab hemoglobin and our device hemoglobin found a correlation coefficient r = 0.870. Bland-Altman analysis of hospital lab hemoglobin and our device hemoglobin had a bias of -0.006 g/dL with 95% of values within the limit of agreement of 1.62 g/dL to -1.62 g/dL.

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