[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118799-en":3,"doc-seo-118799-105":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":4,"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},118799,549758146520,"Patrick","https://ap-avatar.wpscdn.com/avatar/80002397d8c0411e94?_k=1775819394049821470",7,"Healthcare","Digital Diaphanoscopy of Maxillary Sinus Pathologies Supported by Machine Learning","Maxillary sinus pathologies are among the most common ENT conditions and require timely diagnosis for effective treatment. Conventional ENT inspection methods show limited sensitivity for detecting maxillary sinus abnormalities. This study evaluates digital diaphanoscopy combined with machine learning for pathology detection and compares convolutional neural networks with linear discriminant analysis on digital diaphanograms. Both approaches achieve performance beyond traditional screening methods, supporting their potential for screening maxillary sinus alterations, with linear discriminant analysis reaching sensitivity 0.88 and specificity 0.98.","phys. stat. sol. (a) 201, No. 13 (2004) / [www.pss-rapid.com](www.pss-rapid.com) R  \nRESEARCH ARTICLE  \nDigital diaphanoscopy of maxillary sinus pathologies supported by machine learning  \nEkaterina O. Bryanskaya *,1 | Viktor V. Dremin 1 | Valery V. Shupletsov 1 | Alexey V. Kornaev2 | Mikhail Yu. Kirillin3,4 | Anna  \nV Bakotina5 | Dmitry N. Panchenkov5 | Konstantin V. Podmasteryev 1 | Viacheslav G. Artyushenko6 | Andrey V. Dunaev 1  \n1Research and Development Center of Biomedical Photonics, Orel State University, Orel, Russia  \n2 Research Center for Artificial Intelligence, Innopolis University, Innopolis, Russia  \n3 Institute of Applied Physics RAS, Nizhny Novgorod, Russia  \n4 N I Lobachevsky State University of Nizhny Novgorod, Russia  \nYevdokimov A.I. Moscow State University of Medicine and Dentistry, Moscow, Russia  \n6art photonics GmbH, Berlin, 12489, Germany  \n* Correspondence  \nEkaterina O. Bryanskaya, Research and Development Center of Biomedical Photonics, Orel State University, 95  \nKomsomolskaya st., Orel 302026, Russia, [Email: ](Email: katrinbryanya@gmail.com)[katrinbryanya@gmail.com](Email: katrinbryanya@gmail.com)  \nAbstract  \nMaxillary sinus pathologies remain among the most common ENT diseases requiring timely diagnosis for successful treatment. Standard ENT inspection approaches indicate low sensitivity in detecting maxillary sinus pathologies. In this paper, we report on capabilities of digital diaphanoscopy combined with machine learning tools in the detection of such pathologies. We provide a comparative analysis of two machine learning approaches applied to digital diapahnoscopy data, namely, convolutional neural networks and linear discriminant analysis. The sensitivity and specificity values obtained for both employed approaches exceed the reported accuracy indicators for traditional screening diagnosis methods (such as nasal endoscopy or ultrasound), suggesting the prospects of their usage for screening maxillary sinuses alterations. The analysis of the obtained values showed that the linear discriminant analysis, being a simpler approach as compared to neural networks, allows one to detect the maxillary sinus pathologies with the sensitivity and specificity of 0.88 and 0.98, respectively.  \nKEYWORDS  \ndigital diaphanoscopy, maxillary sinuses, optical diagnostics, linear discriminant analysis, convolutional neural networks  \nThis article has been accepted for publication and undergone full peer review but has not been through the copyediting, typesetting, pagination and proofreading process which may lead to differences between this version and the Version of Record. Please cite this article as doi: 10.1002/jbio.202300138  \nThis article is protected by copyright. All rights reserved.  \n1 | INTRODUCTION  \nNowadays, maxillary sinus diseases play an essential role in the socioeconomic situation in the world. The relevance of studying the problem of diagnosis of such diseases is due to a significant number of temporary and permanent disabilities [1] .  \nThere is an annual increase in the incidence of respiratory organs in the world. Among patients hospitalized in ENT departments, the annual increase in patients with diseases of the nose and paranasal sinuses reaches more than 50% . Rhinological patients have become the main focus for ENT clinicians. Relapses in diseases after surgical treatment of sinusitis range from 20-60%[2,3] . For example, in Russia, 10 million people suffer from these diseases annually, 6.3 million people in Germany suffer from acute sinusitis and 2.6 million people from chronic sinusitis every year [4] . Sinusitis affects approximately 29 million adults in the United States annually [5] . Approximately 90% of patients with colds show a component of viral sinusitis [6] . Under the conditions of the COVID-19 pandemic, several studies were conducted, highlighting an increase in the development of diseases of the nasal mucosa, as well as of the paranasal sinuses caused by the disease [7","cbCaial0TrS3HD30","https://ap.wps.com/l/cbCaial0TrS3HD30","pdf",1201163,1,9,"English","en",105,"# Introduction\n## Current diagnosis methods and limitations\n## Need for fast screening tools\n## Digital diaphanoscopy and its rationale","[{\"question\":\"Why is diagnosing maxillary sinus pathologies challenging with standard ENT inspection?\",\"answer\":\"Standard approaches have low sensitivity for detecting maxillary sinus pathologies, making early and reliable detection difficult for successful treatment.\"},{\"question\":\"What digital diagnostic approach is studied in the paper?\",\"answer\":\"The paper investigates digital diaphanoscopy, an optical imaging technique that probes the maxillary sinuses and records diaphanograms using visible and near-infrared light.\"},{\"question\":\"Which machine learning methods are compared, and what results are reported?\",\"answer\":\"Convolutional neural networks and linear discriminant analysis are compared. The reported sensitivity and specificity for linear discriminant analysis are 0.88 and 0.98, respectively, exceeding traditional screening indicators.\"}]","Digital Diaphanoscopy of Maxillary Sinus Pathologies Supported by Machine Learning | PDF",1785720322,23,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"digital-diaphanoscopy-of-maxillary-sinus-pathologies-supported-by-machine-learning","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/healthcare/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/digital-diaphanoscopy-of-maxillary-sinus-pathologies-supported-by-machine-learning/118799/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-03",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why is diagnosing maxillary sinus pathologies challenging with standard ENT inspection?","Question",{"text":75,"@type":76},"Standard approaches have low sensitivity for detecting maxillary sinus pathologies, making early and reliable detection difficult for successful treatment.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What digital diagnostic approach is studied in the paper?",{"text":80,"@type":76},"The paper investigates digital diaphanoscopy, an optical imaging technique that probes the maxillary sinuses and records diaphanograms using visible and near-infrared light.",{"name":82,"@type":73,"acceptedAnswer":83},"Which machine learning methods are compared, and what results are reported?",{"text":84,"@type":76},"Convolutional neural networks and linear discriminant analysis are compared. The reported sensitivity and specificity for linear discriminant analysis are 0.88 and 0.98, respectively, exceeding traditional screening indicators.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,118,123,127,130,134],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":116,"slug":117},40,"healthcare",{"id":119,"doc_module":4,"doc_module_name":46,"category_name":120,"show_sort_weight":121,"slug":122},8,"Research & Report",30,"research-report",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]