[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127757-en":3,"doc-seo-127757-105":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},127757,1099523882182,"Eliana","https://ap-avatar.wpscdn.com/davatar_6f874abed73319feea01a86fa6f0fab8",7,"Healthcare","Preinterventional Third-Molar Assessment Using Robust Machine Learning","Machine learning models—especially deep neural networks—are increasingly used to analyze medical images and support clinical decision-making. This study develops an artificial intelligence system to aid dental decisions on mandibular third molar removal using 2D orthopantograms and risk assessment. Training used 4,516 panoramic radiographs; a spatially dependent U-Net detected the third molar region and the inferior alveolar nerve, followed by deep networks for superimposition and root-development tasks. Performance was evaluated with 10-fold cross-validation and an independent control set labeled by five practitioners, and diagnostic guidance for additional 3D cone beam imaging was proposed.","Zurich Open Repository and Archive  \nUniversity of Zurich  \nUniversity Library Strickhofstrasse 39  \nCH-8057 Zurich [www.zora.uzh.ch](www.zora.uzh.ch)  \nYear: 2023  \nPreinterventional Third-Molar Assessment Using Robust Machine Learning Carvalho, J S ; Lotz, M ; Rubi, L ; Unger, S ; Pfister, T ; Buhmann, J M ; Stadlinger, B  \nDOI: [https://doi.org/10.1177/00220345231200786](https://doi.org/10.1177/00220345231200786)  \nPosted at the Zurich Open Repository and Archive, University of Zurich ZORA URL: [https://doi.org/10.5167/uzh-258609](https://doi.org/10.5167/uzh-258609)  \nJournal Article Published Version  \nThe following work is licensed under a Creative Commons: Attribution-NonCommercial 4.0 International (CC BY-NC 4 .0) License.  \nOriginally published at:  \nCarvalho, J S; Lotz, M; Rubi, L; Unger, S; Pfister, T; Buhmann, J M; Stadlinger, B (2023) . Preinterventional ThirdMolar Assessment Using Robust Machine Learning. Journal of Dental Research, 102(13):1452-1459 .  \nDOI: [https://doi.org/10.1177/00220345231200786](https://doi.org/10.1177/00220345231200786)  \nResearch Reports: Biomaterials & Bioengineering   \nPreinterventional Third-Molar Assessment Using Robust Machine Learning  \nJ.S. Carvalho 1,3*, M. Lotz2*, L. Rubi 1, S. Unger2, T. Pfister2, J.M. Buhmann 1,3°, and B. Stadlinger2,3°  \nJournal of Dental Research 2023, Vol. 102(13) 1452–1459  \n© International Association for Dental, Oral, and Craniofacial Research and American Association for Dental, Oral, and Craniofacial Research 2023  \nArticle reuse guidelines: [sagepub.com/journals-permissions](sagepub.com/journals-permissions)[ ](sagepub.com/journals-permissions)[DOI: 10.1177/00220345231200786](DOI: 10.1177/00220345231200786)[ ](DOI: 10.1177/00220345231200786)[journals.sagepub.com/home/jdr](journals.sagepub.com/home/jdr)  \nAbstract  \nMachine learning (ML) models, especially deep neural networks, are increasingly being used for the analysis of medical images and as a supporting tool for clinical decision-making. In this study, we propose an artificial intelligence system to facilitate dental decision-making for the removal of mandibular third molars (M3M) based on 2-dimensional orthopantograms and the risk assessment of such a procedure. A total of 4,516 panoramic radiographic images collected at the Center of Dental Medicine at the University of Zurich, Switzerland, were used for training the ML model. After image preparation and preprocessing, a spatially dependent U-Net was employed to detect and retrieve the region of the M3M and inferior alveolar nerve (IAN). Image patches identified to contain a M3M were automatically processed by a deep neural network for the classification of M3M superimposition over the IAN (task 1) and M3M root development (task 2). A control evaluation set of 120 images, collected from a different data source than the training data and labeled by 5 dental practitioners, was leveraged to reliably evaluate model performance. By 10-fold cross-validation, we achieved accuracy values of 0.94 and 0.93 for the M3M–IAN superimposition task and the M3M root development task, respectively, and accuracies of 0.9 and 0.87 when evaluated on the control data set, using a ResNet-101 trained in a semisupervised fashion. Matthew’s correlation coefficient values of 0.82 and 0.75 for task 1 and task 2, evaluated on the control data set, indicate robust generalization of our model. Depending on the different label combinations of task 1 and task 2, we propose a diagnostic table that suggests whether additional imaging via 3-dimensional cone beam tomography is advisable. Ultimately, computer-aided decision-making tools benefit clinical practice by enabling efficient and risk-reduced decision-making and by supporting less experienced practitioners before the surgical removal of the M3M.  \nKeywords: deep learning, algorithms, radiography, panoramic, mandible / diagnostic imaging, humans  \nIntroduction  \nThe use of machine learning (ML) to support decision-making and ","cbCaii4mNWY4798M","https://ap.wps.com/l/cbCaii4mNWY4798M","pdf",873785,1,9,"English","en",105,"# Abstract\n# Introduction\n## Machine learning for medical decision support\n## Dental risks in mandibular third molar removal\n# Methods (implied from abstract)\n## Data and image preprocessing\n## U-Net detection and deep neural classification\n## Evaluation strategy","[{\"question\":\"What dental problem does the study address?\",\"answer\":\"The study supports decisions about removing mandibular third molars by assessing risk based on radiographic imaging. It focuses on relationships between the third molar and the inferior alveolar nerve.\"},{\"question\":\"Which imaging data and tasks are used for the AI model?\",\"answer\":\"The model uses 2D orthopantograms. It detects and retrieves the third molar and inferior alveolar nerve region, then classifies (1) M3M superimposition over the IAN and (2) M3M root development.\"},{\"question\":\"How was model performance evaluated?\",\"answer\":\"A training set of 4,516 panoramic images was used, and an independent control set of 120 images from a different source was labeled by five dental practitioners. Ten-fold cross-validation and metrics such as accuracy and Matthew’s correlation coefficient were reported.\"}]","Preinterventional Third-Molar Assessment Using Robust Machine Learning | PDF",1785941436,23,{"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":87,"head_meta":89,"extra_data":91,"updated_unix":28},"preinterventional-third-molar-assessment-using-robust-machine-learning","",{"@graph":36,"@context":86},[37,54,69],{"@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/preinterventional-third-molar-assessment-using-robust-machine-learning/127757/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-23","2026-08-05",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"What dental problem does the study address?","Question",{"text":76,"@type":77},"The study supports decisions about removing mandibular third molars by assessing risk based on radiographic imaging. It focuses on relationships between the third molar and the inferior alveolar nerve.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"Which imaging data and tasks are used for the AI model?",{"text":81,"@type":77},"The model uses 2D orthopantograms. It detects and retrieves the third molar and inferior alveolar nerve region, then classifies (1) M3M superimposition over the IAN and (2) M3M root development.",{"name":83,"@type":74,"acceptedAnswer":84},"How was model performance evaluated?",{"text":85,"@type":77},"A training set of 4,516 panoramic images was used, and an independent control set of 120 images from a different source was labeled by five dental practitioners. Ten-fold cross-validation and metrics such as accuracy and Matthew’s correlation coefficient were reported.","https://schema.org",{"og:url":52,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,119,124,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":46,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":46,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":117,"slug":118},40,"healthcare",{"id":120,"doc_module":4,"doc_module_name":46,"category_name":121,"show_sort_weight":122,"slug":123},8,"Research & Report",30,"research-report",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":107,"slug":138},19,"General","general"]