[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126376-en":3,"doc-seo-126376-105":30,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},126376,962085564549,"Genevieve","https://ap-avatar.wpscdn.com/davatar_085a072bc5b1113ac321206ff7593b45",8,"Research & Report","Exercise-induced Laryngeal Obstruction Diagnostics Using Machine Learning","Exercise-induced laryngeal obstruction (EILO) is a condition where the larynx narrows during physical exercise, creating major difficulties for athletes and active youth by affecting performance and quality of life. The continuous laryngoscopy exercise test (CLE-test) is the gold standard, but clinician scoring is limited by subjectivity. This thesis investigates state-of-the-art machine learning and proposes a transformer-based approach, LarynxFormer, to improve segmentation accuracy and efficiency of laryngeal structures.","Exercise-induced Laryngeal Obstruction Diagnostics Using Machine Learning  \nRune Mæstad  \nSupervisor: Reza Arghandeh  \nMaster’s thesis in Software Engineering  \nDepartment of Computer science, Electrical engineering and Mathematical sciences, Western Norway University of Applied Sciences  \nDepartment of Informatics, University of Bergen  \nMay 2024  \nAcknowledgements  \nI want to start by thanking the Ci2Lab team, including Abdul, Amir, and Mehak, for their fantastic assistance with my thesis. I am especially grateful to my supervisor, Reza Arghandeh, for his exceptional guidance and feedback. My thanks also go to the Bergen ILO group and Hege Clemm, at Haukeland University Hospital, for the opportunity to collaborate and for providing an exciting project. I also owe a special thanks to Haakon Kristian Kvidaland for his invaluable assistance with my thesis and his support with data and technical matters.  \nContents  \nSummary 1  \nI Background 3  \n1 Introduction 4  \n1.1 Motivation and Objective ...................... 4  \n1.2 Methodology and Research Questions ............... 4  \n1.2.1 Ethical considerations .................... 5  \n1.3 Thesis Structure ........................... 5  \n2 Overview of Exercise-induced Laryngeal Obstruction Diagnostics and Machine Learning 7  \n2.1 EILO Diagnostics ........................... 7  \n2.2 Machine learning ........................... 8  \n2.2.1 Convolutional Neural Networks ............... 9  \n2.2.2 Transformers ......................... 9  \n2.2.3 Image Segmentation ..................... 11  \n3 Paper A – Diagnostics of Exercise-Induced Laryngeal Obstruction Using Machine Learning: A Narrative Review 12  \n3.1 Context ................................ 12  \n3.2 Results ................................. 12  \n4 Paper B – LarynxFormer: A Transformer-based Framework for Processing and Segmenting Laryngeal Images 14  \n4.1 Context ................................ 14  \n4.2 Results ................................. 14  \n5 Conclusion & Future Works 16  \n5.1 Conclusion .............................. 16  \n5.2 Future Works ............................. 17  \nII Publications 21  \n6 Diagnostics of Exercise-Induced Laryngeal Obstruction Using  \nMachine Learning: A Narrative Review 22  \n7 LarynxFormer: A Transformer-based Framework for Process  \ning and Segmenting Laryngeal Images 34  \nList of Figures  \n1 LarynxFormer Framework ...................... 2  \n2.1 Larynx Anatomy ........................... 7  \n2.2 CLE-scoring .............................. 8  \n2.3 Transformer Architecture ...................... 10  \n4.1 LarynxFormer Framework ...................... 15  \nSummary  \nExercise-induced laryngeal obstruction (EILO), characterized by laryngeal narrowing during physical exercise, poses a significant challenge, especially for athletes and active youth, impacting performance and quality of life [1] [2] . The continuous laryngoscopy exercise test (CLE-test) is currently the gold standard for assessing EILO [3] [4] . This procedure involves filming the larynx with alaryngoscope. The test is often followed by an evaluation by a clinician, scoring the patient’s severity of EILO [5] . This score corresponds to the amount of adduction of the laryngeal structures.  \nManual scoring methods for EILO diagnostics preserve challenges [6], mainly the problem of subjectivity. Several studies have proposed machine learning (ML) methods for image segmentation of the laryngeal structures, aiming to develop more efficient and objective diagnostic tools [7], [8], [9] . In recent years, ML, particularly deep learning, has made significant advancements [10] and presentsa promising area for further exploration in EILO diagnostics.  \nThis thesis explores state-of-the-art ML approaches for segmenting laryngeal structures and proposes a new framework to improve segmentation performance and efficiency. Two scientific papers have been developed: Paper A explores the current manual methods for assessing EILO and cutting-edge ML methods focus","cbCairoHAoE5WNzv","https://ap.wps.com/l/cbCairoHAoE5WNzv","pdf",16507858,1,46,"English","en",105,"# Summary\n# Introduction\n## Motivation and Objective\n## Methodology and Research Questions\n## Ethical considerations\n# Overview of EILO Diagnostics and Machine Learning\n## EILO Diagnostics\n## Machine learning\n### Convolutional Neural Networks\n### Transformers\n### Image Segmentation\n# Paper A: Narrative Review\n## Context\n## Results\n# Paper B: LarynxFormer\n## Context\n## Results\n# Conclusion & Future Works\n## Conclusion\n## Future Works\n# Publications","[{\"question\":\"What is the main clinical problem addressed in this thesis?\",\"answer\":\"The thesis focuses on exercise-induced laryngeal obstruction (EILO), where the larynx narrows during exercise and limits performance and quality of life.\"},{\"question\":\"Why is the CLE-test not fully sufficient for EILO diagnostics?\",\"answer\":\"Although the CLE-test is the gold standard, it often relies on manual clinician scoring, which introduces subjectivity and motivates more objective methods.\"},{\"question\":\"What does the proposed LarynxFormer framework do?\",\"answer\":\"LarynxFormer provides a transformer-based pipeline for processing and segmenting laryngeal images, including pre-processing, segmentation, and post-processing steps.\"}]","Exercise-induced Laryngeal Obstruction Diagnostics Using Machine Learning | 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is the main clinical problem addressed in this thesis?","Question",{"text":76,"@type":77},"The thesis focuses on exercise-induced laryngeal obstruction (EILO), where the larynx narrows during exercise and limits performance and quality of life.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"Why is the CLE-test not fully sufficient for EILO diagnostics?",{"text":81,"@type":77},"Although the CLE-test is the gold standard, it often relies on manual clinician scoring, which introduces subjectivity and motivates more objective methods.",{"name":83,"@type":74,"acceptedAnswer":84},"What does the proposed LarynxFormer framework do?",{"text":85,"@type":77},"LarynxFormer provides a transformer-based pipeline for processing and segmenting laryngeal images, including pre-processing, segmentation, and post-processing 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