簡易檢索 / 詳目顯示

研究生: 姜凱耀
Kai-Yao Chiang
論文名稱: 應用3D列印於喉閃頻內視鏡輔助診斷之創新客觀量測裝置及影像辨識系統開發與設計
Development and design of innovative objective measurement device and image recognition system using 3D printing for laryngostroboscopic endoscopy assisted diagnosis
指導教授: 郭中豐
Chung-Feng Jeffrey Kuo
口試委員: 郭中豐
Chung-Feng Kuo
黃昌群
Chang-Chiun Huang
劉紹正
Shao-Cheng Liu
邱錦勲
Chin-Hsun Chiu
學位類別: 碩士
Master
系所名稱: 工程學院 - 材料科學與工程系
Department of Materials Science and Engineering
論文出版年: 2021
畢業學年度: 109
語文別: 中文
論文頁數: 141
中文關鍵詞: 喉閃頻內視鏡喉部生理參數雷射投影裝置全身麻醉手術多元迴歸模型統計分析
外文關鍵詞: Laryngeal strobe endoscope, physiological parameters of larynx, laser projection device, tracheal intubation, multiple regression model, ROC
相關次數: 點閱:305下載:0
分享至:
查詢本校圖書館目錄 查詢臺灣博碩士論文知識加值系統 勘誤回報
  •   喉閃頻內視鏡為現今臨床上最常使用的咽喉檢查儀器,但由於每次影像拍攝位置不同,病患聲帶尺寸也不相同,而每位醫師依照自身經驗的觀點容易有主觀認定差異,無法直接估計喉部參數尺寸及客觀進行診療資訊的交流與判斷。本研究研發具有可靠客觀量測之雷射投影裝置附加於喉閃頻內視鏡上,藉由3D列印之快速開發及輕量化特性進行內部管路及光學設計,搭配可重複使用之輕金屬材料製作外殼,總重量為122公克。此裝置提供影像尺度轉換參考參數,其平均準確度達99.13%;並開發專屬之影像辨識輔助系統,可分割喉部重要區域並提取生理參數,自動取得最大聲門面積、左邊聲帶長度、左邊聲帶寬度、左邊聲帶面積、右邊聲帶長度、右邊聲帶寬度、右邊聲帶面積、張開聲帶總面積、聲門角度、總聲帶寬度、閉合聲帶面積、閉合聲帶長度以及閉合聲帶寬度等生理參數並量化。
      本研究共收集了84位正常受試者及36位全身麻醉氣管插管之手術病人,利用所研製之雷射投影裝置取得生理參數等實驗數據,對BMI、年齡等因子進行線性迴歸分析(Regression Analysis),證明年齡越大者有聲門面積逐漸變小的趨勢(p=0.027),而在氣管插管全身麻醉手術病人方面,術後聲門面積與聲門角度明顯小於術前25%,左邊聲帶面積、右邊聲帶面積與張開聲帶總面積分別高於術前31%、32%及32%。本研究使用數種資料分析方法將各項喉部生理參數進行分類,並依此提出全身麻醉氣管插管手術病人喉部是否發炎之診斷評估指標,再由接收者操作特徵曲線(ROC)及約登指數(Youden Index)求出最佳閾值,以評估是否需進一步治療之必要性。在喉部生理參數部分,透過多元迴歸模型得到最佳模型準確率為86.5%,ROC下面積(AUC)為0.8294,意味著該閾值具有極佳的判別能力(AUC > 0.8),而在色相紋理方面,可依勺狀間隙的色相及紋理變化有效分類出發炎與否,本研究所提出的發炎判斷指標其準確率為94.4%,ROC下面積(AUC)為0.912。
      本研究以喉部生理參數與勺狀間隙之色相及紋理提出判斷喉部是否發炎之依據,並證實了因氣管插管會造成聲帶紅腫、喉部發炎等現象,此結果可做為後續研究及臨床上手術前後復原狀況的參考依據,輔助醫師參考評估並給予適當治療,進而提升醫療品質。


      The laryngostroboscopic endoscopy is currently the most commonly used throat examination instrument in clinical practice. However, due to the different image shooting positions required each time and the various sizes of the patient's vocal cords, each physician's point of view is likely to be subjectively determined, as based on experience, and it is impossible to directly and reliably know the sizes and parameters of the larynx and make objective determination and communication regarding the diagnosis and treatment information. In this study, a laser projection device for reliable and objective measurement was attached to the laryngostroboscopic endoscope, which has the 3D printing features of quick development and lightweight for internal piping and optical design. The housing was made of reusable light metal material, with a total weight of only 122 g. The device developed by this study can provide reference parameters for image scale conversion with 99% accuracy. An exclusive image recognition aid system was also developed to segment the important areas of the larynx, and extract its physiological parameters. Moreover, it could automatically obtain quantitative parameters, such as glottal area, closed vocal cord area, closed vocal cord length, and closed vocal cord width.
      This study collected a total of 84 normal subjects and 36 patients undergoing tracheal intubation under general anesthesia. The developed laser projection device was used to obtain the experimental data of physiological parameters, and linear regression analysis was performed on various factors, such as BMI and age. The results prove that the older the age, the smaller the glottal area gradually becomes (p=0.027). and for patients undergoing general anesthesia for tracheal intubation, the postoperative glottal area and glottal angle are significantly less than 25% before surgery, and the left vocal cord area .The area of the right vocal cords and the total area of the open vocal cords were 31%, 32%, and 32% higher than those before surgery. In this study, several data analysis methods were used to classify various physiological parameters of the larynx, and based on this, the diagnostic evaluation index of whether the larynx of patients undergoing tracheal intubation surgery under general anesthesia was put forward, and then the receiver's operating characteristic curve (ROC) and approximate Youden Index finds the optimal threshold to assess the need for further treatment. In the part of physiological parameters of the larynx, the best model accuracy rate obtained through the multiple regression model is 86.5%, and the area under ROC (AUC) is 0.8294, which means that the threshold has excellent discrimination ability (AUC> 0.8). On the other hand, it can be effectively classified according to the hue and texture of the spoon-shaped gap. The accuracy of the inflammation judgment index proposed in this study is 94.4%, and the area under the ROC (AUC) is 0.912.
      This study used the physiological parameters of the larynx and the hue and texture of the spoon-shaped gap to provide a basis for judging whether the larynx is inflamed, and confirmed that the tracheal intubation can cause swelling of the vocal cords and inflammation of the larynx. This result can be used as a follow-up study and Clinically, the reference basis for the recovery status before and after surgery, assisting physicians to refer to and evaluate and give appropriate treatment, thereby improving the quality of medical treatment.

    摘要 I ABSTRACT III 誌謝 VI 目錄 VIII 圖目錄 XI 表目錄 XVI 第1章 緒論 1 1.1 研究動機 1 1.2 文獻回顧 2 1.2.1 聲帶檢測與聲門面積 3 1.2.2 應用於喉部測量裝置 5 1.2.3 醫學上色相及紋理分析 7 1.3 研究目的 8 1.4 論文架構 9 第2章 喉鏡檢查相關醫學介紹 12 2.1 人類喉嚨構造 12 2.2 人類喉嚨功能 14 2.3 喉鏡檢查介紹 15 2.4 麻醉方式 17 第3章 影像理論及研究方法 21 3.1 影像前處理 21 3.2.1 灰階轉換 21 3.2.2 影像遮罩 22 3.2 影像色彩空間轉換 22 3.3 形態學 25 3.3.1 標記化 25 3.3.2 侵蝕 26 3.3.3 膨脹 27 3.3.4 斷開與閉合 28 3.3.5 區域填充 29 3.4 影像分割 29 3.4.1 Otsu演算法 30 3.4.2 主動輪廓法 32 3.5 灰階共生矩陣 34 3.6 線性迴歸分析 37 3.7 3D列印技術 37 3.7.1 熔融沉積成型 (FDM) 38 3.7.2 光固化成型 (SLA) 39 3.7.3 選擇性雷射燒結 (SLS) 40 3.8 醫學指標分析方法 42 第4章 實驗與驗證 49 4.1 硬體架構 49 4.1.1 反射鏡 50 4.1.2 雷射 50 4.1.3 設計前置作業 52 4.1.4 雷射投影裝置 55 4.1.5 實體製作 58 4.2 雷射投影裝置驗證 62 4.3 影像辨識程式開發 64 4.3.1 影像擷取裝置與電腦硬體設備 65 4.3.2 樣本來源及選擇 65 4.3.3 影像處理流程 66 4.4 實驗結果 75 第5章 結果與討論 108 第6章 結論 113 參考文獻 114 附錄 124

    [1] Yelken, K., Gultekin, E., Guven, M., Eyibilen, A., & Aladag, I. (2010). Impairment of voice quality in paradoxical vocal fold motion dysfunction. Journal of Voice, 24(6), 724-727.
    [2] Rosen, C. A. (2005). Stroboscopy as a research instrument: development of a perceptual evaluation tool. The Laryngoscope, 115(3), 423-428.
    [3] Mehta, D. D., & Hillman, R. E. (2012). Current role of stroboscopy in laryngeal imaging. Current Opinion in Otolaryngology & Head and Neck Surgery, 20(6), 429.
    [4] Woo, P. (2014). Objective measures of laryngeal imaging: what have we learned since Dr. Paul Moore. Journal of Voice, 28(1), 69-81.
    [5] Shah, R. K., Feldman, H. A., & Nuss, R. C. (2007). A grading scale for pediatric vocal fold nodules. Otolaryngology—Head and Neck Surgery, 136(2), 193-197.
    [6] Sipp, J. A., Kerschner, J. E., Braune, N., & Hartnick, C. J. (2007). Vocal fold medialization in children: injection laryngoplasty, thyroplasty, or nerve reinnervation?. Archives of Otolaryngology–Head & Neck Surgery, 133(8), 767-771.
    [7] Djukic, V., Milovanovic, J., Jotic, A. D., & Vukasinovic, M. (2014). Stroboscopy in detection of laryngeal dysplasia effectiveness and limitations. Journal of Voice, 28(2), 262-e13.
    [8] Haney, M. M., Hamad, A., Leary, E., Bunyak, F., & Lever, T. E. (2019). Automated quantification of vocal fold motion in a recurrent laryngeal nerve injury mouse model. The Laryngoscope, 129(7), E247-E254.
    [9] Turkmen, H. I., Albayrak, A., Karsligil, M. E., & Kocak, I. (2017). Superpixel-based segmentation of glottal area from videolaryngoscopy images. Journal of Electronic Imaging, 26(6), 061608.
    [10] Menon, R., Petropoulakis, L., Soraghan, J. J., Lakany, H., MacKenzie, K., Hilmi, O., & Di Caterina, G. (2017). Automatic quantification of vocal cord paralysis-an application of fibre-optic endoscopy video processing. Proceedings of The 10th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2017), Porto, Portagal, 21-23, Feb, 108-113.
    [11] Lin, J., Walsted, E. S., Backer, V., Hull, J. H., & Elson, D. S. (2019). Quantification and analysis of laryngeal closure from endoscopic videos. IEEE Transactions on Biomedical Engineering, 66(4), 1127-1136.
    [12] Turkmen, H. I., & Karsligil, M. E. (2019). Advanced computing solutions for analysis of laryngeal disorders. Medical & Biological Engineering & Computing, 57,2535-2552.
    [13] Schade, G., Leuwer, R., Kraas, M., Rassow, B., & Hess, M. M. (2004). Laryngeal morphometry with a new laser ‘clip on’device. Lasers in Surgery and Medicine: The Official Journal of the American Society for Laser Medicine and Surgery, 34(5), 363-367.
    [14] Kuo, C. F. J., Wang, H. W., Hsiao, S. W., Peng, K. C., Chou, Y. L., Lai, C. Y., & Hsu, C. T. M. (2014). Development of laryngeal video stroboscope with laser marking module for dynamic glottis measurement. Computerized Medical Imaging and Graphics, 38(1), 34-41.
    [15] Ochoa, M. E., del Carmen Marín, M., Frutos-Vivar, F., Gordo, F., Latour-Pérez, J., Calvo, E., & Esteban, A. (2009). Cuff-leak test for the diagnosis of upper airway obstruction in adults: a systematic review and meta-analysis. Intensive Care Medicine, 35(7), 1171.
    [16] Tadié, J. M., Behm, E., Lecuyer, L., Benhmamed, R. & Guérot, E. (2010). Post-intubation laryngeal injuries and extubation failure: a fiberoptic endoscopic study. Intensive Care Medicine, 36(6), 991-998.
    [17] Khemani, R. G., Randolph, A., & Markovitz, B. (2009). Corticosteroids for the prevention and treatment of post‐extubation stridor in neonates, children and adults. Cochrane Database of Systematic Reviews, 8, CD001000.
    [18] Erginel, S., Ucgun, I., Yildirim, H., Metintas, M., & Parspour, S. (2005). High body mass index and long duration of intubation increase post-extubation stridor in patients with mechanical ventilation. The Tohoku journal of Experimental Medicine, 207(2), 125-132.
    [19] Jaber, S., Chanques, G., Matecki, S., Ramonatxo, M., Vergne, C., Souche, B., & Eledjam, J. J. (2003). Post-extubation stridor in intensive care unit patients. Intensive Care Medicine, 29(1), 69-74.
    [20] Yu, D.Y., Chen, S.Y., Wu, C.Y., Chang, J.H., & Bien, M.Y. (2014). The etiology, evaluation and management of post-extubation laryngeal edema in adult patients. Journal of Respiratopry Therapy, (13),37-50.
    [21] Wittekamp, B. H., van Mook, W. N., Tjan, D. H., Zwaveling, J. H., & Bergmans, D. C. (2009). Clinical review: post-extubation laryngeal edema and extubation failure in critically ill adult patients. Critical Care, 13(6), 233.
    [22] Wang, C., Tsai, Y., Huang, C., Wu, Y., Ye, M., Chou, H., & Lin, M. (2007). The role of the cuff leak test in predicting the effects of corticosteroid treatment on postextubation stridor. Chang Gung Medical Journal, 30(1), 53.
    [23] Fan, T., Wang, G., Mao, B., Xiong, Z., Zhang, Y., Liu, X., & Yang, S. (2008). Prophylactic administration of parenteral steroids for preventing airway complications after extubation in adults: meta-analysis of randomised placebo controlled trials. British Medical Journal Publishing Group, 337, a1841.
    [24] Torres, A., Gatell, J. M., Aznar, E., El-Ebiary, M., Puig de la Bellacasa, J., González, J., & Rodriguez-Roisin, R. (1995). Re-intubation increases the risk of nosocomial pneumonia in patients needing mechanical ventilation. American Journal of Respiratory and Critical Care Medicine, 152(1), 137-141.
    [25] Shi, J., Uyeda, J. W., Duran-Mendicuti, A., Potter, C. A., & Nunez, D. B. (2019). Multidetector CT of laryngeal injuries: principles of injury recognition. Radiographics, 39(3), 879-892.
    [26] Dailey, S. H., Kobler, J. B., Hillman, R. E., Tangrom, K., Thananart, E., Mauri, M., & Zeitels, S. M. (2005). Endoscopic measurement of vocal fold movement during adduction and abduction. The Laryngoscope, 115(1), 178-183.
    [27] Bohr, C., Kraeck, A., Eysholdt, U., Ziethe, A., & Döllinger, M. (2013). Quantitative analysis of organic vocal fold pathologies in females by high‐speed endoscopy. The Laryngoscope, 123(7), 1686-1693.
    [28] Tao, C., Zhang, Y., & Jiang, J. J. (2007). Extracting physiologically relevant parameters of vocal folds from high-speed video image series. IEEE Transactions on Biomedical Engineering, 54(5), 794-801.
    [29] Voigt, D., Döllinger, M., Braunschweig, T., Yang, A., Eysholdt, U., & Lohscheller, J. (2010). Classification of functional voice disorders based on phonovibrograms. Artificial Intelligence in Medicine, 49(1), 51-59.
    [30] Ozturan, O., Dogan, R., Yenigun, A., Veyseller, B., & Yildirim, Y. S. (2017). Photographic objective alterations for laryngopharyngeal reflux diagnosis. Journal of Voice, 31(1), 78-85.
    [31] Osada, T., Arakawa, A., Sakamoto, N., Ueyama, H., Shibuya, T., Ogihara, T., & Watanabe, S. (2011). Autofluorescence imaging endoscopy for identification and assessment of inflammatory ulcerative colitis. World journal of Ggastroenterology: WJG, 17(46), 5110.
    [32] Degirmenci, N., Dogan, R., Tugrul, S., Senturk, E., Toprak, A., & Ozturan, O. (2020). Red–green–blue analysis of nasal mucosa discolouration in allergic rhinitis. The Journal of Laryngology & Otology, 134(4), 332-337.
    [33] Torheim, T., Malinen, E., Kvaal, K., Lyng, H., Indahl, U. G., Andersen, E. K., & Futsaether, C. M. (2014). Classification of dynamic contrast enhanced MR images of cervical cancers using texture analysis and support vector machines. IEEE Transactions on Medical Imaging, 33(8), 1648-1656.
    [34] Kuo, C. F. J., Kao, C. H., Dlamini, S., & Liu, S. C. (2020). Laryngopharyngeal reflux image quantization and analysis of its severity. Scientific Reports, 10(1), 1-12.
    [35] Akkasaligar, P. T., & Biradar, S. (2014). Classification of medical ultrasound images of kidney. International Journal of Computer Application, 3, 24-28.
    [36] Murry, T. (1991). Clinical voice disorders an interdisciplinary approach in mayo clinic proceedings. Elsevier, 66(6), 656.
    [37] Kezirian, E. J., Hohenhorst, W., & de Vries, N. (2011). Drug-induced sleep endoscopy: the vote classification. European Archives of Otorhino Laryngology, 268(8), 1233-1236.
    [38] Krauskopf, J., Williams, D. R., & Heeley, D. W. (1982). Cardinal directions of color space. Vision Research, 22(9), 1123-1131.
    [39] Otsu, N. (1979). A threshold selection method from gray-level histograms. IEEE Transactions on Systems, Man, and Cybernetics, 9(1), 62-66.
    [40] Kass, M., Witkin, A., & Terzopoulos, D. (1988). Snakes: active contour models. International Journal of Computer Vision, 1(4), 321-331.
    [41] Haralick, R. M., Shanmugam, K., & Dinstein, I. H. (1973). Textural features for image classification. IEEE Transactions on Systems, Man, and Cybernetics, (6), 610-621.
    [42] Agarwal, P., Ramayya, A. G., Osiemo, B., Goodrich, S., Glauser, G., McClintock, S. D., & Malhotra, N. R. (2019). Association of overlapping neurosurgery with patient outcomes at a large academic medical center. Neurosurgery, 85(6), E1050-E1058.
    [43] Xu, Z., Qiu, J., Yang, B., Huang, P., Cai, L., Chen, L., & Wu, G. (2019). Evaluation of factors influencing the guide to read biomedical English literature course for Chinese new medical postgraduates—a multiple regression analysis. Journal of Educational Technology Development and Exchange, 6(1), 295.
    [44] Wasserstein, R. L., & Lazar, N. A. (2016). The ASA statement on p-values: context, rocess, and purpose. The American Statistician, 129-133.
    [45] Bowers, A. J., & Zhou, X. (2019). Receiver operating characteristic (ROC) area under the curve (AUC): A diagnostic measure for evaluating the accuracy of predictors of education outcomes. Journal of Education for Students Placed at Risk (JESPAR), 24(1), 20-46.
    [46] Hajian-Tilaki, K. (2013). Receiver operating characteristic (ROC) curve analysis for medical diagnostic test evaluation. Caspian Journal of Internal Medicine, 4(2), 627.
    [47] Van Erkel, A. R., & Peter, M. (1998). Receiver operating characteristic (ROC) analysis: basic principles and applications in radiology. European Journal of Radiology, 27(2), 88-94.
    [48] Youden, W. J. (1950). Index for rating diagnostic tests. Cancer, 3(1), 32-35.
    [49] Sullivan, G. M., & Feinn, R. (2012). Using effect size—or why the p value is not enough. Journal of Graduate Medical Education, 4(3), 279-282.
    [50] Scheinherr, A., Bailly, L., Boiron, O., Lagier, A., Legou, T., Pichelin, M., & Giovanni, A. (2015). Realistic glottal motion and airflow rate during human breathing. Medical Engineering & Physics, 37(9), 829-839.
    [51] Kuna, S. T., & Vanoye, C. R. (1994). Laryngeal response during forced vital capacity maneuvers in normal adult humans. American Journal of Respiratory & Critical Care Medicine, 150(3), 729-734.
    [52] Dutschmann, M., Jones, S. E., Subramanian, H. H., Stanic, D., & Bautista, T. G. (2014). The physiological significance of postinspiration in respiratory control. Progress in Brain Research, 212, 113-130.
    [53] Rubinstein, I., England, S. J., Zamel, N., & Hoffstein, V. (1989). Glottic dimensions in healthy men and women. Respiration Physiology, 77(3), 291-299.
    [54] Baier, H., Wanner, A., Zarzecki, S., & Sackner, A. (1977). Relationships among glottis opening, respiratory flow, and upper airway resistance in humans. Journal of Applied Physiology, 43(4), 603-611.
    [55] Brown, I. G., Zamel, N., & Hoffstein, V. (1986). Pharyngeal cross-sectional area in normal men and women. Journal of Applied Physiology, 61(3), 890-895.
    [56] Pavlica, T., Bozic-Krstic, V., & Rakic, R. (2010). Correlation of vital lung capacity with body weight, longitudinal and circumference dimensions. Biotechnology & Biotechnological Equipment, 24(1), 325-328.
    [57] Linville, S. E. (1996). The sound of senescence. Journal of Voice, 10(2), 190-200.
    [58] Hagen, P., Lyons, G. D., & Nuss, D. W. (1996). Dysphonia in the elderly: diagnosis and management of age-related voice changes. Southern Medical Journal, 89(2), 204-207.
    [59] Kandogan, T., & Seifert, E. (2009). Influence of aging and sex on voice parameters in patients with unilateral vocal cord paralysis. The Laryngoscope, 115(4), 655-660.
    [60] Pontes, P., Brasolotto, A., & Behlau, M. (2005). Glottic characteristics and voice complaint in the elderly. Journal of Voice, 19(1), 84-94.
    [61] Honjo, I., & Isshiki, N. (1980). Laryngoscopic and voice characteristics of aged persons. Archives of Otolaryngology, 106(3), 149-150.
    [62] Mueller, P. B. (1985). Acoustic and morphologic study of the senescent voice. Ear Nose Throat Journal, 63, 71-75.
    [63] Close, L. G., & Woodson, G. E. (1989). Common upper airway disorders in the elderly and their management. Geriatrics, 44(1), 67-8.
    [64] da Silva, P. T., Master, S., Andreoni, S., Pontes, P., & Ramos, L. R. (2011). Acoustic and long-term average spectrum measures to detect vocal aging in women. Journal of Voice, 25(4), 411-419.
    [65] Rodeno, M. T., Sánchez-Fernández, J. M., & Rivera-Pomar, J. M. (1993). Histochemical and morphometrical ageing changes in human vocal cord muscles. Acta Oto-Laryngologica, 113(3), 445-449.
    [66] Sato, K., Hirano, M., & Nakashima, T. (2002). Age-related changes of collagenous fibers in the human vocal fold mucosa. Annals of Otology, Rhinology & Laryngology, 111(1), 15-20.
    [67] Kuruvilla-Dugdale, M., Dietrich, M., McKinley, J. D., & Deroche, C. (2020). An exploratory model of speech intelligibility for healthy aging based on phonatory and articulatory measures. Journal of Communication Disorders, 87, 105995.
    [68] Lundy, D. S., Silva, C., Casiano, R. R., Lu, F. L., & Xue, J. W. (1998). Cause of hoarseness in elderly patients. Otolaryngology—Head and Neck Surgery, 118(4), 481-485.
    [69] Dehqan, A., Scherer, R. C., Dashti, G., Ansari-Moghaddam, A., & Fanaie, S. (2012). The effects of aging on acoustic parameters of voice. Folia Phoniatrica et Logopaedica, 64(6), 265-270.
    [70] Ward, P. H., Colton, R., McConnell, F., Malmgren, L., Kashima, H., & Woodson, G. (1989). Aging of the voice and swallowing. Otolaryngology—Head and Neck Surgery, 100(4), 283-286.
    [71] Shindo, M. L., & Hanson, D. G. (1990). Geriatric voice and laryngeal dysfunction. Otolaryngologic Clinics of North America, 23(6), 1035-1044.
    [72] Çiyiltepe, M., & Şenkal, Ö. A. (2017). The ageing voice and voice therapy in geriatrics. Aging Clinical and Experimental Research, 29(3), 403-410.
    [73] Sinard, R. J., & Hall, D. (1998). The aging voice: how to differentiate disease from normal changes. Geriatrics, 53(7), 76-79.
    [74] Harries, M., Hawkins, S., Hacking, J., & Hughes, I. (1998). Changes in the male voice at puberty: vocal fold length and its relationship to the fundamental frequency of the voice. The Journal of Laryngology & Otology, 112(5), 451-454.
    [75] Hu, Q., Zhu, S. Y., Luo, F., Gao, Y., & Yang, X. Y. (2010). High‐frequency sonographic measurements of true and false vocal cords. Journal of Ultrasound in Medicine, 29(7), 1023-1030.
    [76] Markova, D., Richer, L., Pangelinan, M., Schwartz, D. H., Leonard, G., Perron, M., & Paus, T. (2016). Age-and sex-related variations in vocal-tract morphology and voice acoustics during adolescence. Hormones and Behavior, 81, 84-96.
    [77] Brodsky, M. B., Levy, M. J., Jedlanek, E., Pandian, V., Blackford, B., Price, C., & Akst, L. M. (2018). Laryngeal injury and upper airway symptoms after oral endotracheal intubation with mechanical ventilation during critical care: a systematic review. Critical Care Medicine, 46(12), 2010-2017.
    [78] Hassan, H. E., & Desouky HIEl, S. M. (2019). Laryngeal Findings and Aspiration Risk after Prolonged Endotracheal Intubation in Adult Patients. Otolaryngol, 9(386), 2.

    無法下載圖示 全文公開日期 2026/02/05 (校內網路)
    全文公開日期 2026/02/05 (校外網路)
    全文公開日期 本全文未授權公開 (國家圖書館:臺灣博碩士論文系統)
    QR CODE