[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123859-en":3,"doc-seo-123859-105":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},123859,549758252649,"Ivy","https://ap-avatar.wpscdn.com/avatar/8000253669c5317157?_k=1778319167496531819",7,"Healthcare","Machine Learning to Predict Apical Lesions - A Cross-Sectional and Model Development Study","Background and aim center on identifying factors linked to apical lesions (AL) in panoramic radiographs and determining the predictive value of those factors. Methodology analyzes panoramic images from 1,071 patients and 27,532 teeth, independently assessed by five dentists, applying multiple shallow machine learning models. Results report AL in 522 patients and 1,133 teeth, highlighting root canal treatment, molars, and crown restorations. Logistic regression and simpler models show stronger associations and higher accuracy. Conclusions indicate higher AL presence in root-canal treated teeth, crowns, and molars, with no benefit from more complex models.","Article  \nMachine Learning to Predict Apical Lesions: A Cross-Sectional and Model Development Study  \nSascha Rudolf Herbst, Vinay Pitchika, Joachim Krois , Aleksander Krasowski  and Falk Schwendicke *  \nCitation: Herbst, S.R.; Pitchika, V.; Krois, J.; Krasowski, A.; Schwendicke, F. Machine Learning to Predict Apical Lesions: A Cross-Sectional and  \nModel Development Study. J. Clin. Med. 2023, 12, 5464. [https://doi.org/](https://doi.org/)[ ](https://doi.org/)[10.3390/jcm12175464](10.3390/jcm12175464)  \nAcademic Editor: Juan Martin Palomo  \nReceived: 31 July 2023  \nRevised: 16 August 2023  \nAccepted: 21 August 2023  \nPublished: 23 August 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/)) .  \nDepartment of Oral Diagnostics, Digital Health and Health Services Research, Charit²–Universitätsmedizin Berlin, Aßmannshauser Street 4-6, 14197 Berlin, Germany; [sascha.herbst@charite.de](sascha.herbst@charite.de) (S.R.H.);  \n[vinay.pitchika@charite.de](vinay.pitchika@charite.de) (V.P.); [joachim.krois@charite.de](joachim.krois@charite.de) (J.K.); aleksander.krasowski@charite.de (A.K.)  \n* Correspondence: [falk.schwendicke@charite.de](falk.schwendicke@charite.de); Tel.: +49-30-450662556  \nAbstract: (1) Background: We aimed to identify factors associated with the presence of apical lesions (AL) in panoramic radiographs and to evaluate the predictive value of the identified factors.  \n(2) Methodology: Panoramic radiographs from 1071 patients (age: 11–93 a, mean: 50.6 a 􀀆 19.7 a) with 27,532 teeth were included. Each radiograph was independently assessed by five experienced dentists for AL. A range of shallow machine learning algorithms (logistic regression, k-nearest neighbor, decision tree, random forest, support vector machine, adaptive and gradient boosting) were employed to identify factors at both the patient and tooth level associated with AL and to predict AL.  \n(3) Results: AL were detected in 522 patients (48.7%) and 1133 teeth (4.1%), whereas males showed asignificantly higher prevalence than females (52.5%/44.8%; p \u003C 0.05) . Logistic regression found that an existing root canal treatment was the most important risk factor (adjusted Odds Ratio 16.89; 95% CI: 13.98–20.41), followed by the tooth type `molar' (2.54; 2.1–3.08) and the restoration with a crown (2.1; 1.67–2.63) . Associations between factors and AL were stronger and accuracy higher when using fewer complex models like decision tree (F1 score: 0.9 (0.89–0.9)) . (4) Conclusions: The presence of AL was higher in root-canal treated teeth, those with crowns and molars. More complex machine learning models did not outperform less-complex ones.  \nKeywords: cross-sectional study; epidemiology; panoramic radiography; periapical lesions; prevalence  \n1. Introduction  \nApical lesions (AL) are a radiographic sign of a dental condition, mainly an endodontic infection [1,2] . These infections are thought to have an impact on systemic health [3] and can compromise the survival of affected teeth [4], which is why clinicians should detect and manage such lesions appropriately.  \nTo optimize the diagnostics and treatment planning of AL, a priori knowledge on the baseline risk of a tooth or a patient suffering from AL is helpful, allowing to tailor diagnostic efforts and therapy. Cross-sectional studies based on different types of radiographs like panoramic radiographs (OPG), cone beam tomography (CBCT)) or periapical radiographs (PR) provide valuable information about the prevalence and the associated risk factors of AL. In general, prevalence of AL is assessed on two levels. (1) Patient-level prevalence is calculated by dividing the number of patients with at least one AL by the total","cbCaie4LsZfWkn3b","https://ap.wps.com/l/cbCaie4LsZfWkn3b","pdf",535891,1,10,"English","en",105,"# Introduction\n## Apical lesions and systemic relevance\n## Prevalence measures at patient and tooth levels\n## Reported prevalence variability across regions\n## Identified risk factors from prior studies\n# Methodology and Modeling Approach\n## Study design and dataset\n## Radiographic assessment\n## Machine learning models and prediction targets\n# Results\n## Prevalence findings\n## Risk factors and model performance\n# Conclusions","[{\"question\":\"What was the study designed to achieve regarding apical lesions?\",\"answer\":\"The study aimed to identify factors associated with apical lesions (AL) on panoramic radiographs and evaluate how well the identified factors predict AL.\"},{\"question\":\"How were panoramic radiographs assessed and what data were used?\",\"answer\":\"Panoramic radiographs from 1,071 patients (27,532 teeth) were included, and each radiograph was independently assessed by five experienced dentists for the presence of AL.\"},{\"question\":\"Which factors showed the strongest association with apical lesions, and did complex models help?\",\"answer\":\"Root canal treatment was the most important risk factor, followed by tooth type (molar) and crown restorations. More complex machine learning models did not outperform less-complex ones, with simpler models showing higher accuracy and stronger associations.\"}]","Machine Learning to Predict Apical Lesions - A Cross-Sectional and Model Development Study | PDF",1785818931,25,{"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-to-predict-apical-lesions-a-cross-sectional-and-model-development-study","",{"@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/machine-learning-to-predict-apical-lesions-a-cross-sectional-and-model-development-study/123859/",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 was the study designed to achieve regarding apical lesions?","Question",{"text":75,"@type":76},"The study aimed to identify factors associated with apical lesions (AL) on panoramic radiographs and evaluate how well the identified factors predict AL.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How were panoramic radiographs assessed and what data were used?",{"text":80,"@type":76},"Panoramic radiographs from 1,071 patients (27,532 teeth) were included, and each radiograph was independently assessed by five experienced dentists for the presence of AL.",{"name":82,"@type":73,"acceptedAnswer":83},"Which factors showed the strongest association with apical lesions, and did complex models help?",{"text":84,"@type":76},"Root canal treatment was the most important risk factor, followed by tooth type (molar) and crown restorations. 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