Thanatchaporn Jindanil, Oana-Elena Burlacu-Vatamanu, Benedetta Baldini, Joeri Meyns, Jeroen Meewis, Rocharles Cavalcante Fontenele, Maria Cadenas de Llano Perula, Reinhilde Jacobs
{"title":"使用MSCT和CBCT扫描的两个队列自动创建口面部虚拟患者。","authors":"Thanatchaporn Jindanil, Oana-Elena Burlacu-Vatamanu, Benedetta Baldini, Joeri Meyns, Jeroen Meewis, Rocharles Cavalcante Fontenele, Maria Cadenas de Llano Perula, Reinhilde Jacobs","doi":"10.1186/s13005-025-00500-1","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>Virtual simulation has advanced in dental healthcare, but the impact of different tomographic techniques on virtual patient (VP) creation remains unclear. This study primarily aimed to automatically create VP from facial scans (FS), intraoral scans (IOS), multislice (MSCT), and cone beam computed tomography (CBCT); Secondarily, to quantitatively compare artificial intelligence (AI)-driven, AI-refined and semi automatically registered (SAR) VP creation from MSCT and CBCT and to compare the effect of soft tissue on the registration with MSCT and CBCT.</p><p><strong>Methods: </strong>A dataset of 20 FS, IOS, and (MS/CB)CT scans was imported into the Virtual Patient Creator platform to generate automated VPs. The accuracy (percentage of corrections required), consistency, and time efficiency of the AI-driven VP registration were then compared to those of the AI-refined and SAR (clinical reference) using Mimics software. The surface distance between the registered FS and the (MS/CB)CT surface rendering using SAR and AI-driven methods was measured to assess the effect of soft tissue on registration.</p><p><strong>Results: </strong>All three registration methods achieved 100% accuracy for VP creation with both MSCT and CBCT (p > 0.999), with no significant differences between tomographic techniques either (p > 0.999). Perfect consistency (1.00) was obtained with AI-driven and AI-refined methods, and slightly lower for SAR (0.977 for MSCT and 0.895 for CBCT). Average registration times were 24.9 and 28.5 s for AI-driven and AI-refined, and 242.3 and 275.7 s for SAR with MSCT and CBCT respectively. The total time was significantly shorter for MSCT (313.7 s) compared to CBCT (850.3 s) (p < 0.001). While the average surface distance between MSCT- and CBCT-based VP showed no significant difference (p > 0.05), AI-driven resulted in a smaller surface distance than SAR (p < 0.05).</p><p><strong>Conclusions: </strong>AI enables fast, accurate, and consistent VP creation using FS, IOS, and (MS/CB)CT data. AI-driven, AI-refined, and semi-automated methods all achieve good accuracy. Additionally, soft tissue registration shows no significant difference between MSCT and CBCT.</p>","PeriodicalId":12994,"journal":{"name":"Head & Face Medicine","volume":"21 1","pages":"21"},"PeriodicalIF":2.4000,"publicationDate":"2025-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11951535/pdf/","citationCount":"0","resultStr":"{\"title\":\"Automated orofacial virtual patient creation using two cohorts of MSCT vs. CBCT scans.\",\"authors\":\"Thanatchaporn Jindanil, Oana-Elena Burlacu-Vatamanu, Benedetta Baldini, Joeri Meyns, Jeroen Meewis, Rocharles Cavalcante Fontenele, Maria Cadenas de Llano Perula, Reinhilde Jacobs\",\"doi\":\"10.1186/s13005-025-00500-1\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Background: </strong>Virtual simulation has advanced in dental healthcare, but the impact of different tomographic techniques on virtual patient (VP) creation remains unclear. This study primarily aimed to automatically create VP from facial scans (FS), intraoral scans (IOS), multislice (MSCT), and cone beam computed tomography (CBCT); Secondarily, to quantitatively compare artificial intelligence (AI)-driven, AI-refined and semi automatically registered (SAR) VP creation from MSCT and CBCT and to compare the effect of soft tissue on the registration with MSCT and CBCT.</p><p><strong>Methods: </strong>A dataset of 20 FS, IOS, and (MS/CB)CT scans was imported into the Virtual Patient Creator platform to generate automated VPs. The accuracy (percentage of corrections required), consistency, and time efficiency of the AI-driven VP registration were then compared to those of the AI-refined and SAR (clinical reference) using Mimics software. The surface distance between the registered FS and the (MS/CB)CT surface rendering using SAR and AI-driven methods was measured to assess the effect of soft tissue on registration.</p><p><strong>Results: </strong>All three registration methods achieved 100% accuracy for VP creation with both MSCT and CBCT (p > 0.999), with no significant differences between tomographic techniques either (p > 0.999). Perfect consistency (1.00) was obtained with AI-driven and AI-refined methods, and slightly lower for SAR (0.977 for MSCT and 0.895 for CBCT). Average registration times were 24.9 and 28.5 s for AI-driven and AI-refined, and 242.3 and 275.7 s for SAR with MSCT and CBCT respectively. The total time was significantly shorter for MSCT (313.7 s) compared to CBCT (850.3 s) (p < 0.001). While the average surface distance between MSCT- and CBCT-based VP showed no significant difference (p > 0.05), AI-driven resulted in a smaller surface distance than SAR (p < 0.05).</p><p><strong>Conclusions: </strong>AI enables fast, accurate, and consistent VP creation using FS, IOS, and (MS/CB)CT data. AI-driven, AI-refined, and semi-automated methods all achieve good accuracy. Additionally, soft tissue registration shows no significant difference between MSCT and CBCT.</p>\",\"PeriodicalId\":12994,\"journal\":{\"name\":\"Head & Face Medicine\",\"volume\":\"21 1\",\"pages\":\"21\"},\"PeriodicalIF\":2.4000,\"publicationDate\":\"2025-03-28\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11951535/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Head & Face Medicine\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1186/s13005-025-00500-1\",\"RegionNum\":2,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"DENTISTRY, ORAL SURGERY & MEDICINE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Head & Face Medicine","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1186/s13005-025-00500-1","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"DENTISTRY, ORAL SURGERY & MEDICINE","Score":null,"Total":0}
Automated orofacial virtual patient creation using two cohorts of MSCT vs. CBCT scans.
Background: Virtual simulation has advanced in dental healthcare, but the impact of different tomographic techniques on virtual patient (VP) creation remains unclear. This study primarily aimed to automatically create VP from facial scans (FS), intraoral scans (IOS), multislice (MSCT), and cone beam computed tomography (CBCT); Secondarily, to quantitatively compare artificial intelligence (AI)-driven, AI-refined and semi automatically registered (SAR) VP creation from MSCT and CBCT and to compare the effect of soft tissue on the registration with MSCT and CBCT.
Methods: A dataset of 20 FS, IOS, and (MS/CB)CT scans was imported into the Virtual Patient Creator platform to generate automated VPs. The accuracy (percentage of corrections required), consistency, and time efficiency of the AI-driven VP registration were then compared to those of the AI-refined and SAR (clinical reference) using Mimics software. The surface distance between the registered FS and the (MS/CB)CT surface rendering using SAR and AI-driven methods was measured to assess the effect of soft tissue on registration.
Results: All three registration methods achieved 100% accuracy for VP creation with both MSCT and CBCT (p > 0.999), with no significant differences between tomographic techniques either (p > 0.999). Perfect consistency (1.00) was obtained with AI-driven and AI-refined methods, and slightly lower for SAR (0.977 for MSCT and 0.895 for CBCT). Average registration times were 24.9 and 28.5 s for AI-driven and AI-refined, and 242.3 and 275.7 s for SAR with MSCT and CBCT respectively. The total time was significantly shorter for MSCT (313.7 s) compared to CBCT (850.3 s) (p < 0.001). While the average surface distance between MSCT- and CBCT-based VP showed no significant difference (p > 0.05), AI-driven resulted in a smaller surface distance than SAR (p < 0.05).
Conclusions: AI enables fast, accurate, and consistent VP creation using FS, IOS, and (MS/CB)CT data. AI-driven, AI-refined, and semi-automated methods all achieve good accuracy. Additionally, soft tissue registration shows no significant difference between MSCT and CBCT.
期刊介绍：
Head & Face Medicine is a multidisciplinary open access journal that publishes basic and clinical research concerning all aspects of cranial, facial and oral conditions.
The journal covers all aspects of cranial, facial and oral diseases and their management. It has been designed as a multidisciplinary journal for clinicians and researchers involved in the diagnostic and therapeutic aspects of diseases which affect the human head and face. The journal is wide-ranging, covering the development, aetiology, epidemiology and therapy of head and face diseases to the basic science that underlies these diseases. Management of head and face diseases includes all aspects of surgical and non-surgical treatments including psychopharmacological therapies.
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