[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126747-en":3,"doc-seo-126747-105":30,"detail-sidebar-cat-0-en-105":91},{"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},126747,962084928432,"Emma Wilson","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",7,"Healthcare","An Automated Strabismus Classification Using Machine Learning Algorithm for Binocular Vision Management System - IEEE conference paper abstract summary","Binocular vision supports depth perception by combining both eyes into a single visual image. Strabismus disrupts clear focusing and often requires expert diagnosis, and inexperienced practitioners may contribute to misclassification due to limited clinical experience. An automated approach based on a case-based reasoning machine learning algorithm is developed for strabismus classification. Reported performance reaches 91.8% accuracy, 89.29% precision, 92.59% recall, and 90.91% F1-score, supporting improved diagnostic class classification.","12/26/23 , 2:56 PM Scopus-Print Document  \nDocuments  \nIsyraf Rohismadi, M.A.a , Mat Raffei, A. F.a , Akmar Zulkifli, N.S.a , Ithnin, M. H. b , Othman, S. F.b  \nAn Automated Strabismus Classification Using Machine Learning Algorithm for Binocular Vision Management System  \n(2023) 8th International Conference on Software Engineering and Computer Systems, ICSECS 2023 , pp. 487-492. DOI: 10.1109/ICSECS58457 .2023.10256291  \na Universiti Malaysia Pahang Al-Sultan Abdullah, Faculty of Computing, Pekan, Pahang, 26600, Malaysia b Kulliyah of Medicine International Islamic University Malaysia, Kuantan, Pahang, 25200, Malaysia  \nAbstract  \nBinocular vision is a type of vision that allows an individual to perceive depth and distance using both eyes to create a single image of their environment. However, there is an illness called strabismus, where it is difficult for some people to focus on seeing things clearly at a time. There are many diagnoses that need to be done for doctors to diagnose whether patients suffer from strabismus or not. Besides, a new practitioner could lead to misdiagnosis due to lack of professional experience and knowledge. To overcome these limitations, a machine learning algorithm, which is a case-based reasoning, is developed to automate the strabismus classification. The results showed that the case-based reasoning algorithm provides 91.8% accuracy, 89.29% precision, 92.59% recall and 90.91% F1-Score. This shows that using the case-based reasoning algorithm can give better performance in classifying the class. © 2023 IEEE.  \nAuthor Keywords  \nAccommodative amplitude; case-based reasoning; classification; machine learning; strabismus diagnosis  \nIndex Keywords  \nBinocular vision, Computer aided diagnosis, Learning algorithms, Machine learning, Stereo image processing; Accommodative amplitude, Casebased reasonings (CBR), Machine learning algorithms, Machine-learning, Management systems, Professional experiences, Reasoning algorithms, Single images, Strabismus diagnose, Types of visions; Case based reasoning  \nReferences  \n Repka, M.X. , Lum, F. , Burugapalli, B.  \nStrabismus, Strabismus Surgery, and Reoperation Rate in the United States  \n(2018) Ophthalmology, 125 (10) .  \nOct  \n Chen, Z. , Fu, H. , Lo, W.-L. , Chi, Z.  \nStrabismus Recognition Using Eye-Tracking Data and Convolutional Neural  \nNetworks  \n(2018) J Healthc Eng, 2018.  \n Li, S. , Tang, A. , Yang, B. , Wang, J. , Liu, L.  \nVirtual reality-based vision therapy versus OBVAT in the treatment of convergence insufficiency, accommodative dysfunction: A pilot randomized controlled trial  \n(2022) BMC Ophthalmol, 22 (1) .  \nDec  \n Kanclerz, P. , Pluta, K. , Momeni-Moghaddam, H. , Khoramnia, R.  \nComparison of the Amplitude of Accommodation Measured Using a New-Generation Closed-Field Autorefractor with Conventional Subjective Methods  \n(2022) Diagnostics, 12 (3) .  \nFeb  \n Ebrahimiadib, N. , Hassanpoor, N. , Niyousha, M. , Modjtahedi, B.S.  \nThe effect of scleral buckling on accommodative amplitude  \n(2020) Int J Retina Vitreous, 6 (1) .  \nDec  \n[https://www.scopus.com/citation/print.uri?origin=recordpage&sid=&src=s&stateKey=OFD_1728191502&eid=2-s2.0-85175466425&sort=&clicked](https://www.scopus.com/citation/print.uri?origin=recordpage&sid=&src=s&stateKey=OFD_1728191502&eid=2-s2.0-85175466425&sort=&clicked)… 1/3  \n12/26/23 , 2:56 PM Scopus-Print Document  \n Burns, D.H. , Allen, P.M. , Edgar, D.F. , Evans, B.J.W.  \nSources of error in clinical measurement of the amplitude of accommodation  \n(2020) J Optom , 13 (1), pp. 3-14.  \nJan  \n Tong, Y. , Lu, W. , Yu, Y. , Shen, Y.  \nApplication of machine learning in ophthalmic imaging modalities  \n(2020) Eye and Vision, 7 (1) .  \nDec  \n Ahsan, M.M. , Luna, S.A. , Siddique, Z.  \nMachine-Learning-Based Disease Diagnosis: A Comprehensive Review  \nHealthcare , 10 (3), p. 541.  \nMar. 2022  \n Kim, D. , Joo, J. , Zhu, G. , Seo, J. , Ha, J. , Kim, S.C.  \nStrabismus Classification using Convolutional Neural Networks  \n(2021) 20","cbCaieJyGQTZpLCv","https://ap.wps.com/l/cbCaieJyGQTZpLCv","pdf",135305,1,3,"English","en",105,"# Abstract\n## Problem: strabismus diagnosis and misclassification risk\n## Method: case-based reasoning machine learning\n## Results: accuracy, precision, recall, F1-score","[{\"question\":\"What problem does the study address?\",\"answer\":\"The study addresses automating strabismus classification for binocular vision management, reducing misdiagnosis risk when clinicians have limited professional experience.\"},{\"question\":\"What machine learning approach is used for classification?\",\"answer\":\"A case-based reasoning (CBR) machine learning algorithm is developed to automate strabismus classification.\"},{\"question\":\"How effective is the proposed method?\",\"answer\":\"The reported results show 91.8% accuracy, 89.29% precision, 92.59% recall, and 90.91% F1-score.\"}]","An Automated Strabismus Classification Using Machine Learning Algorithm for Binocular Vision Management System - 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