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研究生: 黃怡靜
Huang, Yi-Jing
論文名稱: 整合口內鏡與AI空間定位技術建置創新口腔期吞嚥功能評估系統
Integrated Intraoral Endoscopy and AI-Driven Spatial Localization for Oral Phase Dysphagia Assessment
指導教授: 杜翌群
Du, Yi-Chun
學位類別: 碩士
Master
系所名稱: 工學院 - 生物醫學工程學系
Department of BioMedical Engineering
論文出版年: 2025
畢業學年度: 113
語文別: 英文
論文頁數: 75
中文關鍵詞: 吞嚥困難口腔期評估單目深度估計模型數位孿生
外文關鍵詞: Dysphagia, Oral Phase Assessment, Monocular Depth Estimation (MDE), Digital Twins
相關次數: 點閱:24下載:0
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  • 摘要 i Abstract ii 致謝 iii Table of Contents iv List of Figures vi List of Tables viii Chapter 1. Introduction 1 1.1 Preface 1 1.2 Background 2 1.2.1 Dysphagia 2 1.2.2 Current Evaluation Method 3 1.2.3 Clinical Pain Point and Unmet Need 4 1.3 Research motivation and purpose 5 Chapter 2. Literature Review 7 2.1 Swallowing Physiology and Pathophysiology of Dysphagia 7 2.2 Relationship Between Swallowing and Speech Articulation 8 2.3 Instrument-Based Evaluation of Swallowing Function 11 2.3.1 Tongue Pressure Measurement Devices 12 2.3.2 Swallowing Sound and Accelerometric Signal Analysis 13 2.3.3 Surface Electromyography 15 2.3.4 Micro-Electro-Mechanical Systems and Multimodal Sensors 16 2.3.5 Multimodal Sensing Technologies for Dysphagia Assessment 17 2.3.6 Comparison of Advantages and Limitations Across Technologies 19 2.4 Image-Based Analysis of Tongue Motion 21 2.5 Artificial Intelligence and Tongue Image Analysis Techniques 23 2.6 Monocular Depth Estimation 24 Chapter 3. Materials and Methods 28 3.1 Intraoral Scope 28 3.2 Intraoral Scope Fixation Device 29 3.3 Camera Calibration 30 3.4 Tongue Image Segmentation 31 3.5 Monocular Depth Estimation 32 Chapter 4. Experimental Design and Result 34 4.1 Experimental Design 34 4.2 Experiment 1: Comparison and Selection of MDE Models 35 4.2.1 Geometric Model Construction and Retraining of Depth Models 35 4.2.2 Real-World Image Collection and Ground Truth Establishment 36 4.2.3 Vertex Annotation and Ground Truth Distance Calculation 37 4.2.4 Results of Experiment 1 38 4.3 Experiment 2: Tongue Phantom Validation 39 4.3.1 Construction of Digital Twins and Tongue Phantoms 39 4.3.2 Image Acquisition and Feature Point Distance Estimation for Tongue Phantoms 41 4.3.3 Results of Experiment 2 41 4.4 Experiment 3: Clinical Feasibility of the TS-MDE system 43 4.4.1 YOLOv8 Training Result 43 4.4.2 TS-MDE system 44 4.4.3 Clinical Data 45 4.4.4 Device Data 47 4.4.5 Results of Experiment 3 48 4.5 Experiment 4: Variability Comparison with VSA 49 4.5.1 Calculation and Variability Analysis of VSA 50 4.5.2 TS-MDE Area Calculation and Variability Analysis 52 4.5.3 Results of Experiment 4 53 Chapter 5. Discussion 56 Chapter 6. Conclusion and Future Work 57 6.1 Conclusion 57 6.2 Future Work 58 References 59

    [1] J. Leira et al., "Dysphagia and its association with other health-related risk factors in institutionalized older people: A systematic review," Archives of Gerontology and Geriatrics, vol. 110, p. 104991, 2023.
    [2] M. R. Zavala-Solares, C. A. Reyes-Torres, and V. Funez-Madrid, "Oropharyngeal dysphagia treatment," NeuroGastroLatam Rev, vol. 4, no. 1, pp. 7-19, 2020.
    [3] K. Chen et al., "Research progress in the risk factors and screening assessment of dysphagia in the elderly," Frontiers in Medicine, vol. 9, p. 1021763, 2022.
    [4] W. Engelke, K. Jung, and M. Knösel, "Intra-oral compartment pressures: a biofunctional model and experimental measurements under different conditions of posture," Clinical Oral Investigations, vol. 15, pp. 165-176, 2011.
    [5] J. Kieser et al., "Measuring intraoral pressure: adaptation of a dental appliance allows measurement during function," Dysphagia, vol. 23, pp. 237-243, 2008.
    [6] J. M. Wilkinson, D. C. Codipilly, and R. P. Wilfahrt, "Dysphagia: evaluation and collaborative management," American family physician, vol. 103, no. 2, pp. 97-106, 2021.
    [7] F. Rajati, N. Ahmadi, Z. A.-S. Naghibzadeh, and M. Kazeminia, "The global prevalence of oropharyngeal dysphagia in different populations: a systematic review and meta-analysis," Journal of translational medicine, vol. 20, no. 1, p. 175, 2022.
    [8] A. K. Vose, S. Kesneck, K. Sunday, E. Plowman, and I. Humbert, "A survey of clinician decision making when identifying swallowing impairments and determining treatment," Journal of Speech, Language, and Hearing Research, vol. 61, no. 11, pp. 2735-2756, 2018.
    [9] E. J. Park et al., "Correlations between swallowing function and acoustic vowel space in stroke patients with dysarthria," NeuroRehabilitation, vol. 45, no. 4, pp. 463-469, 2019.
    [10] R. D. Kent, "Nonspeech oral movements and oral motor disorders: A narrative review," American journal of speech-language pathology, vol. 24, no. 4, pp. 763-789, 2015.
    [11] C. Ertekin and I. Aydogdu, "Neurophysiology of swallowing," Clinical Neurophysiology, vol. 114, no. 12, pp. 2226-2244, 2003.
    [12] I. M. Lang, "Brain stem control of the phases of swallowing," Dysphagia, vol. 24, no. 3, pp. 333-348, 2009.
    [13] C. M. Steele and A. J. Miller, "Sensory input pathways and mechanisms in swallowing: a review," Dysphagia, vol. 25, pp. 323-333, 2010.
    [14] J. Walton and P. Silva, "Physiology of swallowing," Surgery (Oxford), vol. 36, no. 10, pp. 529-534, 2018.
    [15] J. M. Patterson and S. McHanwell, "Physiology of Swallowing," in Scott-Brown's Otorhinolaryngology and Head and Neck Surgery: CRC Press, 2018, pp. 757-767.
    [16] H.-Y. Feng, P.-P. Zhang, and X.-W. Wang, "Presbyphagia: Dysphagia in the elderly," World journal of clinical cases, vol. 11, no. 11, p. 2363, 2023.
    [17] K. Panara and D. Padalia, "Physiology, swallowing," Treasure Island, FL: StatPearls Publishing, 2019.
    [18] P. Clavé and R. Shaker, "Dysphagia: current reality and scope of the problem," Nature Reviews Gastroenterology & Hepatology, vol. 12, no. 5, pp. 259-270, 2015.
    [19] C. Christmas and N. Rogus‐Pulia, "Swallowing disorders in the older population," Journal of the American Geriatrics Society, vol. 67, no. 12, pp. 2643-2649, 2019.
    [20] S. Thiyagalingam, A. E. Kulinski, B. Thorsteinsdottir, K. L. Shindelar, and P. Y. Takahashi, "Dysphagia in older adults," in Mayo clinic proceedings, 2021, vol. 96, no. 2: Elsevier, pp. 488-497.
    [21] M. Panebianco, R. Marchese-Ragona, S. Masiero, and D. Restivo, "Dysphagia in neurological diseases: a literature review," Neurological Sciences, vol. 41, pp. 3067-3073, 2020.
    [22] C. L. Ludlow, "Central nervous system control of voice and swallowing," Journal of Clinical Neurophysiology, vol. 32, no. 4, pp. 294-303, 2015.
    [23] N. Y. Li-Jessen and C. Ridgway, "Neuroanatomy of voice and swallowing," Neurologic and neurodegenerative diseases of the larynx, pp. 21-40, 2020.
    [24] K. M. Hiiemae and J. B. Palmer, "Tongue movements in feeding and speech," Critical Reviews in Oral Biology & Medicine, vol. 14, no. 6, pp. 413-429, 2003.
    [25] R. D. Kent and H. K. Vorperian, "Static measurements of vowel formant frequencies and bandwidths: A review," Journal of communication disorders, vol. 74, pp. 74-97, 2018.
    [26] S. Sandoval, V. Berisha, R. L. Utianski, J. M. Liss, and A. Spanias, "Automatic assessment of vowel space area," The Journal of the Acoustical Society of America, vol. 134, no. 5, pp. EL477-EL483, 2013.
    [27] Z.-c. Guo and R. Smiljanic, "Speaking clearly improves speech segmentation by statistical learning under optimal listening conditions," Laboratory Phonology, vol. 12, no. 1, 2021.
    [28] M. W. Caverle and A. P. Vogel, "Stability, reliability, and sensitivity of acoustic measures of vowel space: A comparison of vowel space area, formant centralization ratio, and vowel articulation index," The Journal of the Acoustical Society of America, vol. 148, no. 3, pp. 1436-1444, 2020.
    [29] E. J. Park, J. H. Kim, Y. H. Choi, J. E. Son, S. A. Lee, and S. D. Yoo, "Association between phonation and the vowel quadrilateral in patients with stroke: A retrospective observational study," Medicine, vol. 99, no. 39, p. e22236, 2020.
    [30] T. Cao, L. Moro-Velázquez, P. Żelasko, J. Villalba, and N. Dehak, "Vsameter: Evaluation of a new open-source tool to measure vowel space area and related metrics," in 2022 IEEE Spoken Language Technology Workshop (SLT), 2023: IEEE, pp. 517-524.
    [31] J. Sun, N. Yan, and L. Wang, "Constructing a three-dimension physiological vowel space of the Mandarin language using electromagnetic articulography," in 2013 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, 2013: IEEE, pp. 1-4.
    [32] T. Rebernik, J. Jacobi, R. Jonkers, A. Noiray, and M. Wieling, "A review of data collection practices using electromagnetic articulography," Laboratory Phonology, vol. 12, no. 1, p. 6, 2021.
    [33] S. Mat Zin, S. Md Rasib, F. M. Suhaimi, and M. Mariatti, "The technology of tongue and hard palate contact detection: a review," Biomedical engineering online, vol. 20, pp. 1-19, 2021.
    [34] A. Lee, M. Liker, Y. Fujiwara, I. Yamamoto, Y. Takei, and F. Gibbon, "EPG research and therapy: further developments," Clinical linguistics & phonetics, vol. 37, no. 8, pp. 701-721, 2023.
    [35] Z. Bílková et al., "ASSISLT: Computer-aided speech therapy tool," in 2022 30th European Signal Processing Conference (EUSIPCO), 2022: IEEE, pp. 598-602.
    [36] A. Sage, Z. Miodońska, M. Kręcichwost, J. Trzaskalik, E. Kwaśniok, and P. Badura, "Deep learning approach to automated segmentation of tongue in camera images for computer-aided speech diagnosis," Information Technology in Biomedicine, pp. 41-51, 2021.
    [37] A. Sage and P. Badura, "Detection and Segmentation of Mouth Region in Stereo Stream Using YOLOv6 and DeepLab v3+ Models for Computer-Aided Speech Diagnosis in Children," Applied Sciences, vol. 14, no. 16, p. 7146, 2024.
    [38] H. M. Clark and N. P. Solomon, "Age and sex differences in orofacial strength," Dysphagia, vol. 27, pp. 2-9, 2012.
    [39] J. H. Lee et al., "The relationship between tongue pressure and oral dysphagia in stroke patients," Annals of rehabilitation medicine, vol. 40, no. 4, pp. 620-628, 2016.
    [40] R. Franciotti, E. Di Maria, M. D’Attilio, G. Aprile, F. G. Cosentino, and V. Perrotti, "Quantitative measurement of swallowing performance using Iowa Oral Performance Instrument: A systematic review and meta-analysis," Biomedicines, vol. 10, no. 9, p. 2319, 2022.
    [41] K. Lenius, J. Stierwalt, L. L. LaPointe, M. Bourgeois, G. Carnaby, and M. Crary, "Effects of lingual effort on swallow pressures following radiation treatment," Journal of Speech, Language, and Hearing Research, vol. 58, no. 3, pp. 687-697, 2015.
    [42] T. Ono, K. Hori, Y. Masuda, and T. Hayashi, "Recent advances in sensing oropharyngeal swallowing function in Japan," Sensors, vol. 10, no. 1, pp. 176-202, 2009.
    [43] H.-Y. Liu et al., "A novel tongue pressure measurement instrument with wireless mobile application control function and disposable positioning mouthpiece," Diagnostics, vol. 11, no. 3, p. 489, 2021.
    [44] F. Movahedi, A. Kurosu, J. L. Coyle, S. Perera, and E. Sejdić, "A comparison between swallowing sounds and vibrations in patients with dysphagia," Computer methods and programs in biomedicine, vol. 144, pp. 179-187, 2017.
    [45] N. Tanaka et al., "Development of a swallowing frequency meter using a laryngeal microphone," Journal of Oral Rehabilitation, vol. 39, no. 6, pp. 411-420, 2012.
    [46] J.-M. Kim, M.-S. Kim, S.-Y. Choi, and J. S. Ryu, "Prediction of dysphagia aspiration through machine learning-based analysis of patients’ postprandial voices," Journal of neuroengineering and rehabilitation, vol. 21, no. 1, p. 43, 2024.
    [47] Y. Gong, J. Qiao, Y. Huang, Q. Zhang, and Z. Dou, "Mealcoach: Contact Microphone-Based Meal Supervision For Post-Stroke Dysphagia Patients," in 2023 45th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), 2023: IEEE, pp. 1-5.
    [48] E. Zahnd, F. Movahedi, J. L. Coyle, E. Sejdić, and P. G. Menon, "Correlating tri-accelerometer swallowing vibrations and hyoid bone movement in patients with dysphagia," in ASME International Mechanical Engineering Congress and Exposition, 2016, vol. 50534: American Society of Mechanical Engineers, p. V003T04A083.
    [49] J. M. Dudik, A. Kurosu, J. L. Coyle, and E. Sejdić, "Dysphagia and its effects on swallowing sounds and vibrations in adults," Biomedical engineering online, vol. 17, pp. 1-18, 2018.
    [50] L. Gravellier, M. Le Coz, J. Farinas, and J. Pinquier, "Detection of Pharyngolaryngeal Activities in Real-World Settings Using Wearable Sensors," in 2024 46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2024: IEEE, pp. 1-5.
    [51] B. P.-H. So et al., "Swallow detection with acoustics and accelerometric-based wearable technology: A scoping review," International journal of environmental research and public health, vol. 20, no. 1, p. 170, 2022.
    [52] N. Zhao et al., "A robust HD-sEMG sensor suitable for convenient acquisition of muscle activity in clinical post-stroke dysphagia," Journal of Neural Engineering, vol. 20, no. 1, p. 016018, 2023.
    [53] M. Vaiman and E. Eviatar, "Surface electromyography as a screening method for evaluation of dysphagia and odynophagia," Head & face medicine, vol. 5, pp. 1-11, 2009.
    [54] W. Hong et al., "Accurate and Noninvasive Dysphagia Assessment via a Soft High‐Density sEMG Electrode Array Conformal to the Submental and Infrahyoid Muscles," Advanced Science, p. 2500472, 2025.
    [55] J. McCormack, V. Casey, R. Conway, J. Saunders, and A. Perry, "OroPress a new wireless tool for measuring oro-lingual pressures: a pilot study in healthy adults," Journal of NeuroEngineering and Rehabilitation, vol. 12, pp. 1-9, 2015.
    [56] F. Zhao, Y.-W. Zhang, C.-Q. Xie, C. Yang, Z.-L. Dou, and X.-M. Wei, "Characteristics of Tongue pressure measured by Novel Multisite Flexible sensors in nasopharyngeal carcinoma patients with Dysphagia," Archives of physical medicine and rehabilitation, vol. 105, no. 3, pp. 531-538, 2024.
    [57] K. Hori et al., "Newly developed sensor sheet for measuring tongue pressure during swallowing," Journal of prosthodontic research, vol. 53, no. 1, pp. 28-32, 2009.
    [58] Y. Koyama, N. Ohmori, H. Momose, S.-i. Yamada, and H. Kurita, "Detection of swallowing disorders with a multiple-channel surface electromyography sensor sheet," Journal of Dental Sciences, vol. 17, no. 3, pp. 1185-1192, 2022.
    [59] B. Shin et al., "Automatic clinical assessment of swallowing behavior and diagnosis of silent aspiration using wireless multimodal wearable electronics," Advanced Science, vol. 11, no. 34, p. 2404211, 2024.
    [60] M. K. O’Brien et al., "Advanced machine learning tools to monitor biomarkers of dysphagia: a wearable sensor proof-of-concept study," Digital Biomarkers, vol. 5, no. 2, pp. 167-175, 2021.
    [61] Y. Wu, K. Guo, Y. Chu, Z. Wang, H. Yang, and J. Zhang, "Advancements and Challenges in Non-Invasive Sensor Technologies for Swallowing Assessment: A Review," Bioengineering, vol. 11, no. 5, p. 430, 2024.
    [62] M.-Y. Hsiao, C.-H. Wu, and T.-G. Wang, "Emerging role of ultrasound in dysphagia assessment and intervention: a narrative review," Frontiers in Rehabilitation Sciences, vol. 2, p. 708102, 2021.
    [63] M. A. Tily, H. Al-Nashash, and H. Mir, "An intraoral camera for supporting assistive devices," IEEE Sensors Journal, vol. 21, no. 6, pp. 8553-8563, 2020.
    [64] T. Hashimoto et al., "Tongueinput: Input method by tongue gestures using optical sensors embedded in mouthpiece," in 2018 57th Annual Conference of the Society of Instrument and Control Engineers of Japan (SICE), 2018: IEEE, pp. 1219-1224.
    [65] J. Vergara, A. Miles, J. Lopes de Moraes, and C. T. Chone, "Contribution of Wireless Wi-Fi Intraoral Cameras to the Assessment of Swallowing Safety and Efficiency," American Speech-Language-Hearing Association, 1558-9102, 2024.
    [66] X. Wu and G. Zheng, "Depth Estimation for Oral Cavity by Shape from Shading with Endoscope," in 2023 IEEE International Conference on Robotics and Automation (ICRA), 2023: IEEE, pp. 2697-2701.
    [67] Z. Li, Z. Yu, W. Liu, Y. Xu, D. Zhang, and Y. Cheng, "Tongue image segmentation via color decomposition and thresholding," Concurrency and Computation: Practice and Experience, vol. 31, no. 23, p. e4662, 2019.
    [68] W. Liu, J. Hu, Z. Li, Z. Zhang, Z. Ma, and D. Zhang, "Tongue image segmentation via thresholding and gray projection," KSII Transactions on Internet and Information Systems (TIIS), vol. 13, no. 2, pp. 945-961, 2019.
    [69] H. Yishuan, Z. Qi, and H. Zhanpeng, "Tongue image segmentation based on the sub-block region growing algorithm," in 2018 5th International Conference on Information Science and Control Engineering (ICISCE), 2018: IEEE, pp. 578-581.
    [70] L. Wei, C. Jinming, L. Bo, H. Wei, W. Xingjin, and Z. Hui, "Tongue image segmentation and tongue color classification based on deep learning," Digital Chinese Medicine, vol. 5, no. 3, pp. 253-263, 2022.
    [71] Q. Xu et al., "Multi-task joint learning model for segmenting and classifying tongue images using a deep neural network," IEEE journal of biomedical and health informatics, vol. 24, no. 9, pp. 2481-2489, 2020.
    [72] J. Li et al., "Establishment of noninvasive diabetes risk prediction model based on tongue features and machine learning techniques," International Journal of Medical Informatics, vol. 149, p. 104429, 2021.
    [73] K. Noguchi, I. Saito, T. Namiki, Y. Yoshimura, and T. Nakaguchi, "Reliability of non-contact tongue diagnosis for Sjögren's syndrome using machine learning method," Scientific reports, vol. 13, no. 1, p. 1334, 2023.
    [74] X. Qiao et al., "Intelligent tongue diagnosis model for gastrointestinal diseases based on tongue images," Biomedical Signal Processing and Control, vol. 96, p. 106643, 2024.
    [75] J. H. Lee, M.-K. Han, D. W. Ko, and I. H. Suh, "From big to small: Multi-scale local planar guidance for monocular depth estimation," arXiv preprint arXiv:1907.10326, 2019.
    [76] R. Ranftl, K. Lasinger, D. Hafner, K. Schindler, and V. Koltun, "Towards robust monocular depth estimation: Mixing datasets for zero-shot cross-dataset transfer," IEEE transactions on pattern analysis and machine intelligence, vol. 44, no. 3, pp. 1623-1637, 2020.
    [77] A. Rokaya, S. M. T. Islam, and K. Mostafa, "Enhancing Surgical Precision: Deep Learning-Based Depth Estimation in Minimally Invasive Surgery with the MiDaS Model," in International Conference on Robot Intelligence Technology and Applications, 2023: Springer, pp. 46-57.
    [78] A. Lou, Y. Li, Y. Zhang, and J. Noble, "Surgical Depth Anything: Depth Estimation for Surgical Scenes using Foundation Models," arXiv preprint arXiv:2410.07434, 2024.
    [79] S. F. Bhat, R. Birkl, D. Wofk, P. Wonka, and M. Müller, "Zoedepth: Zero-shot transfer by combining relative and metric depth," arXiv preprint arXiv:2302.12288, 2023.
    [80] J. J. Han, A. Acar, C. Henry, and J. Y. Wu, "Depth anything in medical images: A comparative study," arXiv preprint arXiv:2401.16600, 2024.
    [81] W. Yuan, X. Gu, Z. Dai, S. Zhu, and P. Tan, "Neural window fully-connected crfs for monocular depth estimation," in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2022, pp. 3916-3925.
    [82] Y. Zhou, Z. Qiu, M. Yang, Y. Hu, and J. Liu, "Synthetic Monocular Depth Estimation Dataset for Cataract Surgery Assistance," in 2023 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 2023: IEEE, pp. 1812-1817.
    [83] T. Tsukiyama, E. Marcushamer, T. J. Griffin, E. Arguello, P. Magne, and G. O. Gallucci, "Comparison of the anatomic crown width/length ratios of unworn and worn maxillary teeth in Asian and white subjects," The Journal of prosthetic dentistry, vol. 107, no. 1, pp. 11-16, 2012.
    [84] F. Cinelli, F. Piva, F. Bertini, D. S. Russo, and L. Giachetti, "Maxillary anterior teeth dimensions and relative width proportions: A narrative literature review," Dentistry Journal, vol. 12, no. 1, p. 3, 2023.
    [85] G. J. A. C. J. Qiu. "Ultralytics YOLOv8." Ultralytics. https://github.com/ultralytics/ultralytics (accessed 05/24, 2024).
    [86] A. Tashtoush, Y. Wang, M. T. Khasawneh, A. Hader, M. S. Shazeeb, and C. G. Lindsay, "Real-time object segmentation for laparoscopic cholecystectomy using YOLOv8," Neural Computing and Applications, vol. 37, no. 4, pp. 2697-2710, 2025.
    [87] S. Frey et al., "Optimizing intraoperative AI: evaluation of YOLOv8 for real-time recognition of robotic and laparoscopic instruments," Journal of Robotic Surgery, vol. 19, no. 1, p. 131, 2025.
    [88] H. Tang and K. Jia, "A new benchmark: On the utility of synthetic data with blender for bare supervised learning and downstream domain adaptation," in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2023, pp. 15954-15964.
    [89] A. I. Károly, I. Nádas, and P. Galambos, "Synthetic Multimodal Video Benchmark (SMVB): Utilizing Blender for rich dataset generation," in 2024 IEEE 22nd World Symposium on Applied Machine Intelligence and Informatics (SAMI), 2024: IEEE, pp. 000065-000070.
    [90] A. I. Károly and P. Galambos, "Automated dataset generation with Blender for deep learning-based object segmentation," in 2022 IEEE 20th Jubilee World Symposium on Applied Machine Intelligence and Informatics (SAMI), 2022: IEEE, pp. 000329-000334.
    [91] A. Richter, T. Steinmann, J.-C. Rosenthal, and S. J. Rupitsch, "Advances in real-time 3d reconstruction for medical endoscopy," Journal of imaging, vol. 10, no. 5, p. 120, 2024.
    [92] M. Beghini, T. L. Pereira, J. M. C. Montes, D. De Moura, T. U. Dezem, and R. d. Silva, "Morphometric analysis of tongue in individuals of European and African ancestry," Journal of Forensic Investigation, vol. 5, no. 1, pp. 2330-0396.1000038, 2017.
    [93] F. Tamura, T. Kikutani, T. Tohara, M. Yoshida, and K. Yaegaki, "Tongue thickness relates to nutritional status in the elderly," Dysphagia, vol. 27, no. 4, pp. 556-561, 2012.
    [94] L. Ouyoung, "Videofluoroscopy swallow study: technique and protocol," in Dysphagia Management in Head and Neck Cancers: A Manual and Atlas: Springer, 2018, pp. 67-72.
    [95] B. Rosner, Fundamentals of Biostatistics, 7 th ed. Boston: Brooks/Cole, 2010, p. 566.

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