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A longitudinal cohort study analyzed 90 adolescents aged 11–16, grouped into horizontal, vertical, and average growth patterns. AI algorithms superimposed lateral cephalograms from baseline to 18 months and computed linear and angular cephalometric measures. Statistical testing used ANOVA with Tukey’s post hoc comparisons.",{"@graph":69,"@context":122},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":35,"@type":76,"position":81},"https://docshare.wps.com/document/healthcare/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/comparative-evaluation-of-craniofacial-growth-patterns-using-ai-driven-longitudinal-cephalometric-superimpositions-in-adolescents/460353/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/comparative-evaluation-of-craniofacial-growth-patterns-using-ai-driven-longitudinal-cephalometric-superimpositions-in-adolescents/460353.png","ImageObject",300,407,{"name":92,"@type":93},"dhado","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-11-08","2026-09-30",true,{"@type":102,"interactionType":103,"userInteractionCount":14},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"What problem does the study target in craniofacial growth assessment?","Question",{"text":112,"@type":113},"The study targets the limitations of traditional longitudinal cephalometric analysis, which depends on manual tracing and can be slow and inconsistent due to landmark identification variability.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"How was the AI method used in the research?",{"text":117,"@type":113},"AI algorithms were used to superimpose lateral cephalograms taken at baseline and after 18 months, enabling linear and angular measurements such as S-N, ANB, Co-Gn, and ANS-Me.",{"name":119,"@type":110,"acceptedAnswer":120},"What key findings were reported about growth pattern differences?",{"text":121,"@type":113},"AI-based analysis found significant differences among horizontal, vertical, and average groups, with vertical growers showing the highest increase in lower anterior facial height and reproducibility indicated by an intra-class correlation greater than 0.94.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},460353,1790758282,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":34,"category_name":35,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":14,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":81,"language":139,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":140,"faqs":141,"seo_title":142,"seo_description":67,"update_tm":129,"read_time":39,"preview_page_count":4},5909893218464,"https://ap-avatar.wpscdn.com/davatar_085a072bc5b1113ac321206ff7593b45","Original Research  \nComparative Evaluation of Craniofacial Growth Patterns Using  \nAI‑Driven Longitudinal Cephalometric Superimpositions in Adolescents  \nMohammad K. Alam1,2,3, Mohammad Y. Hajeer4, Renad N. Abahussain1, Reuof A. Algadri1, Dalia M. Alamri1, Mohammed E. Rashid5  \n1Department of Preventive Dentistry, College of  \nDentistry, Jouf University, Sakaka, Saudi Arabia, 2Department of Dental Research Cell, Saveetha Institute of Medical and Technical Sciences, Saveetha Dental College and Hospitals, Chennai, Tamil Nadu, India, 3Department of Public Health, Faculty of Allied Health Sciences, Daffodil International University, Dhaka, Bangladesh, 4Department of Orthodontics, Faculty of Dentistry, University of Damascus, Damascus, Syria, 5Department of Oral and Maxillofacial Diagnostic Sciences, College of Dentistry, Taibah University, Al Madinah Al Munawara, Saudi Arabia  \nSubmitted: 04‑Aug‑2025  \nRevised: 10‑Aug‑2025  \nAccepted: 18‑Aug‑2025  \nPublished: 17‑Dec‑2025  \nAbstract  \nBackground: Accurate analysis of craniofacial growth is essential for individualized orthodontic treatment planning. Traditional cephalometric methods often rely on manual tracing, which can be time‑consuming and subject to interobserver variability. Recent advances in artificial intelligence (AI) offer promising tools for precise and reproducible assessment of growth changes. Methods: A longitudinal cohort of 90 adolescents (aged between 11 and 16 years) was analyzed, comprising 30 each from horizontal, vertical, and average growth pattern groups, determined by initial cephalometric assessment. AI algorithms were used to superimpose lateral cephalograms taken at baseline and after 18 months. Linear and angular measurements, including S‑N, ANB, mandibular length (Co‑Gn), and lower anterior facial height (ANS‑Me), were computed. Statistical analysis involved ANOVA and post hoc Tukey’s test (P \u003C 0.05 considered significant) . Results: The AI‑based analysis revealed significant differences in mandibular length increase among the groups (horizontal: 5.2 ± 1.3 mm, vertical:  \n3.1 ± 1.2 mm, average: 4.1 ± 1.1 mm; P = 0.003) . Vertical growers exhibited the highest increase in lower anterior facial height (6.7 ± 1.5 mm), compared to horizontal (3.5 ± 1.0 mm) and average growers (4.2 ± 1.2 mm) (P \u003C 0.001) . The AI superimpositions demonstrated high reproducibility (intra‑class correlation > 0.94) . Conclusion: AI‑driven cephalometric superimposition provides a reliable and efficient method for assessing craniofacial growth in adolescents. Vertical growers show greater vertical skeletal development, while horizontal growers exhibit more prominent mandibular lengthening. AI integration into routine cephalometric evaluation can significantly enhance diagnostic accuracy and growth prediction. Keywords: AI cephalometrics, craniofacial growth, growth pattern analysis   \nIntroduction  \nCraniofacial growth assessment  \nin orthodontic diagnosis and particularly during adolescence  \nplays a pivotal role treatment planning, when substantial  \nskeletal changes occur.[1] Traditionally, longitudinal cephalometric superimpositions have been employed to track these changes, but manual tracing is prone to errors due to landmark identification variability and human fatigue.[2]  \nRecent developments in artificial intelligence (AI), particularly in deep learning and image recognition, have enabled the automation of cephalometric  \n\n| Access this article online |  |\n| --- | --- |\n| Quick Response Code:\u003Cbr>| Website: [https://journals.lww.com/jpbs](https://journals.lww.com/jpbs) |\n|  | DOI: 10.4103/jpbs.jpbs_1346_25 |\n\nlandmark identification with improved precision and consistency.[3] AI‑based cephalometric tools have demonstrated accuracy comparable to expert orthodontists, with significant time‑saving benefits.[4] In addition, longitudinal superimposition using AI facilitates reproducible comparison of skeletal changes over time.[5]  \nAddress for correspondence: Dr. Mohammad Kh","cbCaiuiYTz6mbJTs","https://ap.wps.com/l/cbCaiuiYTz6mbJTs","pdf",560548,"English","# Abstract\n## Background\n## Methods\n## Results\n## Conclusion\n# Introduction\n## Craniofacial growth assessment in orthodontic diagnosis\n## AI for cephalometric landmark identification\n# Aim\n# Materials and Methods\n## Study design and participants","[{\"question\":\"What problem does the study target in craniofacial growth assessment?\",\"answer\":\"The study targets the limitations of traditional longitudinal cephalometric analysis, which depends on manual tracing and can be slow and inconsistent due to landmark identification variability.\"},{\"question\":\"How was the AI method used in the research?\",\"answer\":\"AI algorithms were used to superimpose lateral cephalograms taken at baseline and after 18 months, enabling linear and angular measurements such as S-N, ANB, Co-Gn, and ANS-Me.\"},{\"question\":\"What key findings were reported about growth pattern differences?\",\"answer\":\"AI-based analysis found significant differences among horizontal, vertical, and average groups, with vertical growers showing the highest increase in lower anterior facial height and reproducibility indicated by an intra-class correlation greater than 0.94.\"}]","Comparative Evaluation of Craniofacial Growth Patterns Using AI-Driven Longitudinal Cephalometric Superimpositions in Adolescents | PDF"]