[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121750-en":3,"doc-seo-121750-105":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},121750,34359740700684,"Finn","https://ap-avatar.wpscdn.com/avatar/1f400023980c374ae676?_k=1777273430885731487",8,"Research & Report","Machine Learning in Dentistry: A Scoping Review","Machine learning is increasingly used in dental research and clinical applications. The review systematically compiles ML studies in dentistry and evaluates methodological quality, including risk of bias and reporting standards. Studies published from 1 January 2015 to 31 May 2021 were screened across MEDLINE, IEEE Xplore, and arXiv. The analysis characterizes publication trends and ML task distribution across clinical fields, and assesses risk of bias using QUADAS-2 and reporting adherence using TRIPOD. Among 168 included studies, classification dominates, multiple metrics are used, and inconsistency in outcomes limits replication and cross-study comparison, highlighting the need for a core outcome set.","Review  \nMachine Learning in Dentistry: A Scoping Review  \nLubaina T. Arsiwala-Scheppach 1,2, *, Akhilanand Chaurasia 2,3, Anne Müller 4, Joachim Krois 1,2 and Falk Schwendicke 1,2  \nCitation: Arsiwala-Scheppach, L.T.; Chaurasia, A.; Müller, A.; Krois, J.; Schwendicke, F. Machine Learning in Dentistry: A Scoping Review. J. Clin.  \nMed. 2023, 12, 937. [https://doi.org/](https://doi.org/)[ ](https://doi.org/)[10.3390/jcm12030937](10.3390/jcm12030937)  \nAcademic Editor: Paul R. Cooper  \nReceived: 10 December 2022  \nRevised: 6 January 2023  \nAccepted: 23 January 2023  \nPublished: 25 January 2023  \nCopyright: © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://](https://)[ ](https://)[creativecommons.org/licenses/by/](creativecommons.org/licenses/by/)[ ](creativecommons.org/licenses/by/)[4.0/](4.0/)) .  \n1 Department of Oral Diagnostics, Digital Health and Health Services Research, Charit²—Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin,  \n14197 Berlin, Germany  \n2 ITU/WHO Focus Group AI on Health, Topic Group Dental Diagnostics and Digital Dentistry, CH-1211 Geneva 20, Switzerland  \n3 Department of Oral Medicine and Radiology, King George's Medical University, Lucknow 226003, India  \n4 Pharmacovigilance Institute (Pharmakovigilanz-und Beratungszentrum, PVZ) for Embryotoxicology, Institute of Clinical Pharmacology and Toxicology, Charit²—Universitätsmedizin Berlin,  \n13353 Berlin, Germany  \n* Correspondence: [lubaina.arsiwala@charite.de](lubaina.arsiwala@charite.de)  \nAbstract: Machine learning (ML) is being increasingly employed in dental research and application. We aimed to systematically compile studies using ML in dentistry and assess their methodological quality, including the risk of bias and reporting standards. We evaluated studies employing ML in dentistry published from 1 January 2015 to 31 May 2021 on MEDLINE, IEEE Xplore, and arXiv. We assessed publication trends and the distribution of ML tasks (classification, object detection, semantic segmentation, instance segmentation, and generation) in different clinical fields. We appraised the risk of bias and adherence to reporting standards, using the QUADAS-2 and TRIPOD checklists, respectively. Out of 183 identified studies, 168 were included, focusing on various ML tasks and employing a broad range of ML models, input data, data sources, strategies to generate reference tests, and performance metrics. Classification tasks were most common. Forty-two different metrics were used to evaluate model performances, with accuracy, sensitivity, precision, and intersectionover-union being the most common. We observed considerable risk of bias and moderate adherence to reporting standards which hampers replication of results. A minimum (core) set of outcome and outcome metrics is necessary to facilitate comparisons across studies.  \nKeywords: dental radiography; dentistry; machine learning; neural networks; scoping review  \n1. Introduction  \nWith the advent of the big data era, machine learning (ML) methods like Support Vector Machine, Naïve Bayesian Classifier, Decision Tree, Random Forest (RF), K-Nearest Neighbor, and Deep Learning involving Convolutional Neural Network (CNN), etc., have been increasingly adopted in fields such as finance, spatial sciences, and speech recognition [1] . Additionally, in medicine and dentistry, ML has been employed for a range of applications, for example, image analysis in dermatology, ophthalmology, or radiology, with accuracy values similar or better than that of experienced clinicians [1,2] .  \nIn the field of ML, mathematical models are employed to enable computers to learn inherent structures in data and to use the learned understanding for predicting on new, unseen data [3] . For deep learning models, specifically CNNs, different types of model","cbCaiiKp4rQThvFn","https://ap.wps.com/l/cbCaiiKp4rQThvFn","pdf",1026139,1,23,"English","en",105,"# Introduction\n## Machine learning in dentistry and related background\n## ML workflow, tasks, and evaluation metrics","[{\"question\":\"What is the main objective of the scoping review?\",\"answer\":\"To systematically compile studies using machine learning in dentistry and assess methodological quality, including risk of bias and reporting standards.\"},{\"question\":\"Which time range and databases were included in the study selection?\",\"answer\":\"Studies published from 1 January 2015 to 31 May 2021 were evaluated using MEDLINE, IEEE Xplore, and arXiv.\"},{\"question\":\"What did the review find about common ML tasks and evaluation metrics?\",\"answer\":\"Classification tasks were the most common, and accuracy-related measures (such as sensitivity and precision) were frequently used, along with metrics like intersection-over-union.\"}]","Machine Learning in Dentistry: A Scoping Review | PDF",1785806644,58,{"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},"machine-learning-in-dentistry-a-scoping-review","",{"@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/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/machine-learning-in-dentistry-a-scoping-review/121750/",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","2026-08-04",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},"What is the main objective of the scoping review?","Question",{"text":75,"@type":76},"To systematically compile studies using machine learning in dentistry and assess methodological quality, including risk of bias and reporting standards.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which time range and databases were included in the study selection?",{"text":80,"@type":76},"Studies published from 1 January 2015 to 31 May 2021 were evaluated using MEDLINE, IEEE Xplore, and arXiv.",{"name":82,"@type":73,"acceptedAnswer":83},"What did the review find about common ML tasks and evaluation metrics?",{"text":84,"@type":76},"Classification tasks were the most common, and accuracy-related measures (such as sensitivity and precision) were frequently used, along with metrics like intersection-over-union.","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},[93,97,101,105,110,115,120,123,128,131,135],{"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":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"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":106,"slug":138},19,"General","general"]