[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125209-en":3,"doc-seo-125209-105":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},125209,1099514067415,"Rowan","https://ap-avatar.wpscdn.com/avatar/100002539d78ffe74a7?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779092875211072502",8,"Research & Report","Machine learning models for improving the diagnosing efficiency of skeletal class I and III in German orthodontic patients","Accurate, efficient identification of skeletal class is essential in orthodontics to support correct and stable treatment planning, yet reliable determination is challenging due to correlations among craniofacial anatomical structures. This prospective cross-sectional study developed and tested machine learning classifiers to distinguish skeletal class I from class III using pre-treatment lateral cephalograms of 509 German patients. Multiple models were evaluated, cephalometric associations were analyzed with correlation and PCA, and a simplified GLM approach using SNA, SNB, and ML-NSL achieved high diagnostic accuracy.","[www. nature.com/scientificreports](www. nature.com/scientificreports)  \nOPEN  \nMachine learning models for improving the diagnosing efficiency of skeletal class I and III in German orthodontic patients  \nEva Paddenberg-Schubert1,7, Kareem Midlej2,7, Sebastian Krohn1,7, Agnes Schröder1, Obaida Awadi3, Samir Masarwa3, Iqbal M. Lone2, Osayd Zohud2, Christian Kirschneck4, Nezar Watted3,5,6, Peter Proff1,7 & FuadA. Iraqi1,2,6,7􀀍  \nThe precise and efficient diagnosis of an individual’s skeletal class is necessary in orthodontics to ensure correct and stable treatment planning. However, it is difficult to efficiently determine the true skeletal class due to several correlations between various anatomic structures. The primary outcome of this prospective cross-sectional study was developing a machine learning model for classifying patients as skeletal class I and III. Furthermore, the investigation intended to compare cephalometric variables between skeletal class I and III as well as between age and sex-specific subgroups to analyse correlations between cephalometric parameters and to perform Principal Component Analysis (PCA) to identify the most important variables contributing to skeletal class I and III variances. This study was based on the pre-treatment lateral cephalograms of 509 German orthodontic patients diagnosed as skeletal class I (n = 341) or III (n = 168) according to the individualisedANB of Panagiotidis and Witt, following descriptive analyses of cephalometric parameters, correlation analyses followed by Principal Component Analysis (PCA) to identify key cephalometric variables. Machine learning models, including Random Forest (RF), Classification and Regression Trees (CART), k-nearest Neighbors (KNN), Linear Discriminant Analysis (LDA), Support Vector Machines (SVM), and Generalized Linear Model (GLM), were evaluated for accuracy. Within the same skeletal class, age influenced cephalometric parameters:  \nin skeletal class I, adolescents presented a more horizontal pattern (PFH/AFH, Gonial angle, NL  \nML) and prominent mandible (SNB, SN-Pg) than children. In skeletal class III, the degree of sagittal discrepancy between jaw bases was most notable in adults (ANB: III_Age > 21-III_ 14 \u003C Age \u003C 20 − 1.78°) . Comparing skeletal class I and III, the latter had more prognathic mandibles (SNB) and compensated incisors’ inclination (proclination of the upper (+ 1/NA: 9.01°), retroinclination of the lower incisors (− 1/ ML: 8.99°). Among others, a correlation was found between the sagittal (degree of prognathism, SNB)  \nand vertical (inclination, ML-NSL) orientation of the mandible (skeletal class I: p \u003C 0.001, ρ = − 0.742; skeletal class III: p \u003C 0.001, ρ = − 0.665). PCA revealed that the first four principal components explain 93% of the variance in skeletal class I/III diagnosis and that these parameters had the most influence loading score on the first component-PFH/AFH ratio (0.35), SNB angle (0.35), SN-Pg (0.37), and ML  \nNSL (− 0.35). Evaluating machine learning models, the general model, including all cephalometric parameters, age, and sex, resulted in perfect (1.00) accuracy and kappa scores compared to the gold standard Calculated_ANB with the model’s RF and CART. In model 2 the amount of input variables was reduced (Wits, SNB only), but the accuracy (0.88), and kappa (0.73) were still good in the KNN model. In the last section of this study, we applied different machine learning classification models. We examined the ability of the parameters—SNA, SNB, and ML-NSL angles to predict the classification as skeletal class I or III. The results demonstrated that the GLM model gained an accuracy of 0.99 (Accuracy = 0.99, Kappa = 0.97). The precise diagnosis of skeletal class I/III can be simplified by applying the machine learning model GLM with the input variables SNA, SNB, and ML-NSL only. This stresses the importance of their correct identification. However, considering all skeletal classes, a larger population is need","cbCainaYT260Kfjv","https://ap.wps.com/l/cbCainaYT260Kfjv","pdf",2294885,1,15,"English","en",105,"# Study aim and clinical rationale\n## Machine learning model development and evaluation\n# Dataset and cephalometric variables\n## Patient grouping and ANB-based classification\n## Correlation analysis and PCA\n# Results across skeletal classes\n## Age and sex effects\n## Key contributing variables\n# Model performance and simplified diagnostic approach\n## GLM with selected input parameters\n# Limitations and validation needs","[{\"question\":\"What was the primary goal of the study?\",\"answer\":\"To develop a machine learning model that classifies orthodontic patients as skeletal class I or skeletal class III using pre-treatment lateral cephalograms.\"},{\"question\":\"Which cephalometric analysis methods were used to identify important variables?\",\"answer\":\"The study used correlation analyses followed by Principal Component Analysis (PCA) to determine which cephalometric parameters most contributed to variance in skeletal class I/III.\"},{\"question\":\"How can diagnosis be simplified according to the results?\",\"answer\":\"The study found that a GLM model using only SNA, SNB, and ML-NSL can achieve high accuracy for predicting skeletal class I versus III, emphasizing the need for correct identification of these inputs.\"}]","Machine learning models for improving the diagnosing efficiency of skeletal class I and III in German orthodontic patients | PDF",1785897447,38,{"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-models-for-improving-the-diagnosing-efficiency-of-skeletal-class-i-and-iii-in-german-orthodontic-patients","",{"@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-models-for-improving-the-diagnosing-efficiency-of-skeletal-class-i-and-iii-in-german-orthodontic-patients/125209/",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","2026-08-05",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 primary goal of the study?","Question",{"text":75,"@type":76},"To develop a machine learning model that classifies orthodontic patients as skeletal class I or skeletal class III using pre-treatment lateral cephalograms.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which cephalometric analysis methods were used to identify important variables?",{"text":80,"@type":76},"The study used correlation analyses followed by Principal Component Analysis (PCA) to determine which cephalometric parameters most contributed to variance in skeletal class I/III.",{"name":82,"@type":73,"acceptedAnswer":83},"How can diagnosis be simplified according to the results?",{"text":84,"@type":76},"The study found that a GLM model using only SNA, SNB, and ML-NSL can achieve high accuracy for predicting skeletal class I versus III, emphasizing the need for correct identification of these inputs.","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"]