[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127837-en":3,"doc-seo-127837-105":31,"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":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},127837,1099523885336,"Violet","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Evaluation of machine learning algorithms and relevant biomarkers for the diagnosis of multiple sclerosis","Multiple sclerosis (MS) is a prevalent neurodegenerative disease with significant visual pathway-related symptoms. Optical coherence tomography (OCT) has emerged as a valuable tool, and machine learning (ML) techniques hold promise for MS diagnosis. Existing studies often underexploit OCT features and lack interpretable model analysis needed for clearer clinical insights. This study evaluates five ML algorithms using macular and optic-disc parameters from OCT imaging to classify healthy controls versus MS patients, identifying 19 significant discriminative features (p \u003C 0.001). Patient-wise cross-validation shows Gaussian Naive Bayes achieving the best AUC (87.9% ± 7.7%). SHAP interpretation links top features—especially minimum ganglion cell thickness—to MS-related visual pathway and ganglion cell layer degeneration, supporting OCT-ML for early diagnosis and personalized management.","Evaluation of machine learning algorithms and relevant biomarkers for the diagnosis of multiple sclerosis based on  \noptical coherence tomography  \nPablo Garc´ıa Mesa 1 , Pilar Rojas Lozano2 , Nuria D´ıaz Guti´errez2 , Manuel Cadena Santoyo2 ,  \nAlberto J. Beltr´an Carrero 1 , Juan J. G´omez-Valverde 1 ,3  \n1 Biomedical Image Technologies, ETSI Telecomunicaci´on, Universidad Polit´ecnica de Madrid, Madrid, Spain.  \n2 Instituto Provincial de Oftalmolog´ıa, Hospital General Universitario Gregorio Mara˜n´on, Madrid, Spain.  \n3 Centro de Investigaci´on Biom´edica en Red de Bioingenier´ıa, Biomateriales y Nanomedicina (CIBER-BBN), Madrid, Spain.  \nAbstract  \nMultiple sclerosis (MS) is a prevalent neurodegenerative disease with significant visual pathway-related symptoms. Optical coherence tomography (OCT) has emerged as a valuable tool, and machine learning (ML) techniques hold promise for MS diagnosis. However, existing studies often lack comprehensive feature exploitation and require interpretable model analysis to improve clinical insights and diagnostic criteria. This study evaluates machine learning models for classification of healthy controls and MS patients using a comprehensive set of macular and optic-disc parameters from OCT imaging. The study included a dataset of 77 MS eyes and 54 control eyes, obtained by ophthalmic examination and OCT measurements from Optic Disc and Macular Cube scan protocols of a Cirrus HD-OCT 5000 (Carl Zeiss, Meditec, Dublin, CA, USA) . Our results identified 19 features, validated by p-values (p \u003C 0.001), as effective discriminators between MS patients and healthy controls. Patient-wise cross-validation is used to evaluate the performance of five ML algorithms. Gaussian Naive Bayes achieved the best AUC (87.9% ± 7.7%), while SHAP analysis reinforced the alignment with clinical observations of MS-related visual pathway changes and ganglion cell layer degeneration, with minimum ganglion cell thickness being the feature with the highest impact on classification. These findings underscore the potential of OCT-ML for early diagnosis and personalized treatment of MS.  \n1. Introduction  \nMultiple sclerosis (MS) is an autoimmune and neurodegenerative disease in which the myelin sheath surrounding nerve cells in the brain and spinal cord is damaged. This condition causes the improper transmission of nerve impulses, leading to various potential disabilities, with partial or total blindness, sensory loss, and motor disorders being the most common [1] . It is estimated that 2.8 million people worldwide are living with MS, with a higher prevalence of 140 cases per 100,000 population in Europe and the Americas [2] .  \nDiagnosis and monitoring of MS rely on the integration of clinical, imaging and laboratory evidence, which may involve invasive procedures such as contrast-enhanced magnetic resonance imaging (MRI) and lumbar punc-  \nture [3] . Several important symptoms of MS are related to visual pathway disorders, such as: optic neuritis, diplopia and oscillopsia. The assessment of ophthalmic patients has been revolutionized by optical coherence tomography (OCT), a rapid and reproducible imaging technique that employs low-coherence interferometry to generate cross-sectional images of the retina and optic nerve head (ONH) [4] . In recent years, several studies have revealed the presence of biomarkers associated with MS as well as other neurodegenerative diseases such as Parkinson’s and Alzheimer’s in the retina and optic disc. These biomarkers include specific structures like the ganglion cell layer (GCL), the retinal nerve fiber layer (RNFL) of the optic disc, and the optic nerve head (ONH) . [5, 6, 7] . Furthermore, with the increasing popularity of artificial intelligence (AI) techniques, several studies have employed parameters from OCT to train machine learning (ML) algorithms for the diagnosis of MS [8, 9, 10] . However, most studies do not use a wide range of features extracted from both the macular an","cbCaisW69x7ou6yC","https://ap.wps.com/l/cbCaisW69x7ou6yC","pdf",1664965,2,1,4,"English","en",105,"# Abstract\n# 1. Introduction\n# 2. Materials\n## 2.1. Dataset","[{\"question\":\"What role does OCT play in this MS diagnosis study?\",\"answer\":\"OCT provides cross-sectional retinal and optic nerve head imaging that enables extraction of macular and optic-disc parameters used for ML-based classification.\"},{\"question\":\"How were the machine learning models evaluated?\",\"answer\":\"Performance was assessed using patient-wise cross-validation across five ML algorithms to distinguish healthy controls from MS patients.\"},{\"question\":\"Which features and interpretability method were most influential?\",\"answer\":\"Nineteen OCT-derived features significantly discriminated groups (p \\u003c 0.001). SHAP analysis showed minimum ganglion cell thickness as the feature with the highest impact on classification while aligning with clinical expectations of MS-related degeneration.\"}]","Evaluation of machine learning algorithms and relevant biomarkers for the diagnosis of multiple sclerosis | PDF",1785942266,10,{"code":4,"msg":32,"data":33},"ok",{"site_id":25,"language":24,"slug":34,"title":13,"keywords":35,"description":14,"schema_data":36,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":29},"evaluation-of-machine-learning-algorithms-and-relevant-biomarkers-for-the-diagnosis-of-multiple-sclerosis","",{"@graph":37,"@context":85},[38,53,68],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,48,51],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":20},"https://docshare.wps.com/document/","Document",{"item":49,"name":12,"@type":44,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":44,"position":22},"https://docshare.wps.com/document/evaluation-of-machine-learning-algorithms-and-relevant-biomarkers-for-the-diagnosis-of-multiple-sclerosis/127837/",{"url":52,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":24,"description":14,"dateModified":61,"datePublished":62,"encodingFormat":60,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":42,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-08-23","2026-08-05",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What role does OCT play in this MS diagnosis study?","Question",{"text":75,"@type":76},"OCT provides cross-sectional retinal and optic nerve head imaging that enables extraction of macular and optic-disc parameters used for ML-based classification.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How were the machine learning models evaluated?",{"text":80,"@type":76},"Performance was assessed using patient-wise cross-validation across five ML algorithms to distinguish healthy controls from MS patients.",{"name":82,"@type":73,"acceptedAnswer":83},"Which features and interpretability method were most influential?",{"text":84,"@type":76},"Nineteen OCT-derived features significantly discriminated groups (p \u003C 0.001). SHAP analysis showed minimum ganglion cell thickness as the feature with the highest impact on classification while aligning with clinical expectations of MS-related degeneration.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":58,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":25},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,134],{"id":21,"doc_module":4,"doc_module_name":47,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":20,"doc_module":4,"doc_module_name":47,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":22,"doc_module":4,"doc_module_name":47,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":47,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":47,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":47,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":47,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":47,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":47,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":30,"doc_module":4,"doc_module_name":47,"category_name":132,"show_sort_weight":30,"slug":133},"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":47,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]