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Scott-Brown’’s Essential Otorhinolaryngology, Head & Neck Surgery

為了解決Otolaryngologist的問題，作者 這樣論述：

R James A England MD FRCS (ORL-HNS) is Consultant Otolaryngologist Head and Neck and Thyroid Surgeon and Honorary Senior Lecturer at Hull University Teaching Hospitals NHS Trust, UK.Eamon Shamil MBBS, MRes, MRCS, DOHNS is Specialist Registrar in ENT - Head and Neck Surgery at St. George’s University

Hospital, South London Deanery, UK

Otolaryngologist進入發燒排行的影片

利用機器學習技術與唾液蛋白標記建置口腔鱗狀細胞癌風險預測模型

為了解決Otolaryngologist的問題，作者王奕晟 這樣論述：

Table of Contents指導教授推薦書口試委員會審定書中文摘要 iiiAbstract ivList of Figures viiList of Tables viiiChapter 1 Introduction 11.1. Background 11.1.1. Stage and treatment in OSCC 21.1.2. Risk factors in habitual behaviors 31.1.3. Premalignant lesions before OSCC 31.1.4. Autoantibodies

as protein biomarkers 41.1.5. Machine learning techniques 51.2. Motivation 51.3. Objective 6Chapter 2 Literature review 72.1. Malignant transformation in OPMD 72.2. Autoantibodies application in cancers 82.3. Machine learning application in clinical practice 9Chapter 3 Materi

als and methods 123.1. Study participants and design 123.2. Saliva processing and autoantibodies detection 133.3. Development of prediction models 143.4. Evaluation of predictive models 193.5. Statistical analysis 20Chapter 4 Results 224.1. Demographic characteristics and the le

vels of salivary IgA autoantibodies 224.2. Comparing the performance of predictive models 224.3. Variable importance in predictive models 29Chapter 5 Discussion 315.1. Salivary autoantibodies in OSCC risk prediction 315.2. Habitual behaviors are still important factors for predicting

OSCC 325.3. Machine learning has good performance for predicting high-risk cases of OSCC 325.4. Binary data format fits the ensemble model 345.5. Limitations 34Chapter 6 Conclusion 36References 37List of FiguresFigure 1. The flow chart of screening for OSCC. 13Figure 2. The supp

ort vectors, marked with grey squares, define the margin of the largest division between the two classes (Cortes and Vapnik 1995). 16Figure 3. A simple DNN architecture (Topol 2019). 18Figure 4. The flow chart of continuous variables processing. 19Figure 5. The flow chart of the predictive

models’ development and evaluation. 21Figure 6. Performance of six different data processing strategies for distinguishing the high-risk from the low-risk population by AUC. 24Figure 7. Variable importance plot in XGBoost 30List of TablesTable 1. All tuning parameters of the six models.

15Table 2. Baseline demographic characteristics and the levels of salivary autoantibodies in all participants. 23Table 3. The AUCs of the original MFI value and different age formats. 25Table 4. The AUCs of the binary MFI format and different age formats. 26Table 5. Holm-Bonferroni method p

ost hoc test (p-value) in converting age (years) and autoantibodies (MFI value) to binary format. 27Table 6. The AUCs of the logarithmic MFI format and different age formats. 28Table 7. The AUCs of the standardized MFI format and different age formats. 29

Better Breastfeeding: A Doctor’’s Guide to Nursing Without Pain and Frustration

為了解決Otolaryngologist的問題，作者Dahl, Linda 這樣論述：

Linda Dahl, MD, is an ENT-otolaryngologist who also started her own practice, where she focuses on infants with breastfeeding issues, and developed a methodology to help bring mothers and infants together without pain, frustration, or multiple consults. She has treated over 23,000 baby/mom breastfe

eding pairs over the years with a nearly 90% success rate. She has also written and co-written articles published in such scientific journals as the International Journal of Pediatric Otorhinolaryngology and the Journal of Voice and the book, Clinicians Guide to Breastfeeding: Evidence-based Evaluat

ion and Management.

運用深度學習辨識病理嗓音作為聲帶疾患診斷工具

為了解決Otolaryngologist的問題，作者胡皓淳 這樣論述：

前言:聲音沙啞會顯著的影響生活品質，且須經由喉部內視鏡檢查才能做出正確的診斷。然而此項檢查為侵入性且儀器設備昂貴導致並非所有基層診所都有此項設備。而且喉部內視鏡的判讀也需要有經驗的喉科醫師才能做出正確的診斷。本研究希望能夠利用人工智慧將聲音沙啞患者的嗓音音訊檔案進行深度學習，以期以沙啞嗓音辨識即可做出相關聲帶疾患的診斷，增加醫療之可近性。方法:我們共收集了正常嗓音、聲帶萎縮、單側聲帶麻痹、器質性聲帶病變、內收肌痙攣性發聲障礙五種不同類型的聲音，聲音內容為母音“阿”加上標準國語短文的朗讀。我們將各組音檔的對應的時頻圖藉由卷積式神經網路進行深度學習，並將訓練成果與人類專家做比較。結果:本研究共進

行兩次試驗。第一次試驗收集了29筆正常嗓音的音檔以及527筆病理嗓音的音檔，病理嗓音音檔包含了聲帶萎縮210筆、單側聲帶麻痹43筆、器質性聲帶病變244筆以及內收肌痙攣性發聲障礙30筆。深度學習模型在五分類（正常嗓音、聲帶萎縮、單側聲帶麻痹、器質性聲帶病變、內收肌痙攣性發聲障礙）的狀況下可以達到敏感度0.70、特異度0.90、準確度65.5%，與人類專家相比較，兩位喉科醫師的準確度分別為58.6%及49.1%，兩位一般耳鼻喉科醫師的準確度分別為38.8%及34.5%。第二次試驗收集了189筆正常嗓音的音檔以及552筆病理嗓音的音檔，病理嗓音音檔包含了聲帶萎縮224筆、單側聲帶麻痹50筆、器質性

聲帶病變248筆以及內收肌痙攣性發聲障礙30筆。深度學習模型在五分類的狀況下可以達到敏感度0.66、特異度0.91、準確度66.9%，與人類專家相比較，兩位喉科醫師的準確度分別為60.1%及56.1%，兩位一般耳鼻喉科醫師的準確度分別為51.4%及43.2%。結論:本研究顯示藉由深度學習辨識病理嗓音可以達到初步辨識聲帶疾患的目的。由於嗓音辨識非侵入性的檢查特色，可運用在廣泛性的初步篩檢或是即時性的初步判讀，像是在需要遠端醫療或是沒有喉部內視鏡的醫療不便區域，可以幫助第一線的基層醫師初步篩選出需要後送進行侵入性檢查的病人。且本次研究也成功建立了華語病理嗓音資料庫，可以做為未來聲音沙啞相關研究的資

料來源。