[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117041-en":3,"doc-seo-117041-105":29,"detail-sidebar-cat-0-en-105":89},{"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":20,"language":21,"language_code":22,"site_id":23,"html_lang":22,"table_of_contents":24,"faqs":25,"seo_title":26,"seo_description":14,"update_tm":27,"read_time":28},117041,962084925782,"Ava Thompson","https://ap-avatar.wpscdn.com/davatar_9964176cb1d06d4a9deccf72a44ae3dc",8,"Research & Report","Machine Learning for Child Oral Health - An Scoping Review","A scoping review maps the current evidence on machine learning (ML) in child oral health, focusing on how ML supports early detection and improved prediction of childhood tooth decay and related conditions. ML systems learn from training data such as radiographs, treatment records, and experimental parameters, enabling diagnostic or predictive tasks without direct human programming. Searches across Medline, Web of Science, EBSCO, Cochrane Library, and Scopus included studies evaluating ML models for children aged 0–9. Findings highlight growing contributions from HIC and UMIC settings and frequent use of neural networks for diagnosis and prediction.","Machine Learning for Child Oral Health: An Scoping Review  \nSamantha Schlaud, Sydnee Spector, Amir Mohajeri, Man Hung  \nRoseman University of Health Sciences College of Dental Medicine, South Jordan, Utah, USA  \nPurpose Results Conclusion  \n● Machine learning is a branch of data science which uses an analysis method giving computers the ability to learn without human instruction.  \n● Training data such as radiographs, treatment records, and/or experimental parameters are imputed into the system allowing the model to make diagnoses or predictions.  \n● ML can be used in dentistry to organize the large quantities of data generated in the clinics each day. Allowing predicting and detecting diseases more efficient and accurate.  \nObjective  \nTooth decay in children is the most common chronic childhood disease and it can be prevented by early detection. We aimed to provide a map of the current evidence on machine learning (ML) in child oral health and provide insight for future research.  \nMethods  \nA scoping review was conducted by using Medline, Web of Science, EBSCO Dentistry & Oral Science Source, the Cochrane Library, and Scopus.  \nArticles in which an ML model was assessed for the diagnoses, prediction, or management of any condition in children (0-9 years old) in any year were included. Data were extracted for year of publication, geographical location, age, number of subjects, disease condition, type of study, and ML algorithms.  \nArticle Selection Global Distribution  \nTime Trend  \nType of Algorithms  \nAlgorithms According to Specialties  \n● The aim was to map the current evidence on machine learning in child oral health  \n● HIC and UMIC contributed most publications  \n● Prediction and diagnosis are the most common categories of ML models  \n● The most common ML algorithm used were neural networks  \n● ML algorithms such as gradient boosting, random forest, and regression algorithms have emerged in more recent years.  \n● ML algorithms hold great importance to the dental field potentially assisting dental providers to complete their diagnosing and predicting of oral health manifestations with high accuracy.  \n● Dental professionals, such as pediatric and general dentists, can use these models to increase their proficiency and decrease clinical errors.  \nLimitations  \n● Our scope was relatively narrowed from the literature available on the topic due to our age limitation.  \n● Does not include data published in a language other than English, so our findings may be affected by that exclusion criteria.  \nAcknowledgements  \n● We would like to thank the following:  \n○ Roseman University College of Dental Medicine  \n○ Clinical Outcomes Research and Education","cbCaiefgIqqtscpL","https://ap.wps.com/l/cbCaiefgIqqtscpL","pdf",525571,1,"English","en",105,"# Purpose\n# Objective\n# Methods\n## Search sources\n## Inclusion criteria\n# Key findings\n## Publication distribution\n## Time trends and algorithm types\n# Applications in dentistry\n# Limitations\n# Acknowledgements","[{\"question\":\"What was the purpose of this scoping review?\",\"answer\":\"To map current evidence on machine learning in child oral health and provide insight for future research.\"},{\"question\":\"What types of children and studies were included?\",\"answer\":\"Studies assessed ML models for diagnoses, prediction, or management of any condition in children aged 0–9, and data were extracted across publication year, location, age, subjects, disease, study type, and algorithms.\"},{\"question\":\"Which ML categories and algorithms were most common?\",\"answer\":\"Prediction and diagnosis were the most common categories, with neural networks being the most frequently used algorithms; gradient boosting, random forest, and regression algorithms appeared more in recent years.\"}]","Machine Learning for Child Oral Health - An Scoping Review | PDF",1785673312,3,{"code":4,"msg":30,"data":31},"ok",{"site_id":23,"language":22,"slug":32,"title":13,"keywords":33,"description":14,"schema_data":34,"social_meta":84,"head_meta":86,"extra_data":88,"updated_unix":27},"machine-learning-for-child-oral-health-an-scoping-review","",{"@graph":35,"@context":83},[36,52,66],{"@type":37,"itemListElement":38},"BreadcrumbList",[39,43,47,49],{"item":40,"name":41,"@type":42,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":44,"name":45,"@type":42,"position":46},"https://docshare.wps.com/document/","Document",2,{"item":48,"name":12,"@type":42,"position":28},"https://docshare.wps.com/document/research-report/",{"item":50,"name":13,"@type":42,"position":51},"https://docshare.wps.com/document/machine-learning-for-child-oral-health-an-scoping-review/117041/",4,{"url":50,"name":13,"@type":53,"author":54,"headline":13,"publisher":56,"fileFormat":59,"inLanguage":22,"description":14,"dateModified":60,"datePublished":60,"encodingFormat":59,"isAccessibleForFree":61,"interactionStatistic":62},"DigitalDocument",{"name":9,"@type":55},"Person",{"url":40,"name":57,"@type":58},"DocShare","Organization","application/pdf","2026-08-02",true,{"@type":63,"interactionType":64,"userInteractionCount":4},"InteractionCounter",{"@type":65},"ViewAction",{"@type":67,"mainEntity":68},"FAQPage",[69,75,79],{"name":70,"@type":71,"acceptedAnswer":72},"What was the purpose of this scoping review?","Question",{"text":73,"@type":74},"To map current evidence on machine learning in child oral health and provide insight for future research.","Answer",{"name":76,"@type":71,"acceptedAnswer":77},"What types of children and studies were included?",{"text":78,"@type":74},"Studies assessed ML models for diagnoses, prediction, or management of any condition in children aged 0–9, and data were extracted across publication year, location, age, subjects, disease, study type, and algorithms.",{"name":80,"@type":71,"acceptedAnswer":81},"Which ML categories and algorithms were most common?",{"text":82,"@type":74},"Prediction and diagnosis were the most common categories, with neural networks being the most frequently used algorithms; 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