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1.
J Evid Based Dent Pract ; 24(2): 101965, 2024 06.
Artigo em Inglês | MEDLINE | ID: mdl-38821652

RESUMO

ARTICLE TITLE AND BIBLIOGRAPHIC INFORMATION: Artificial Intelligence for Detecting Cephalometric Landmarks: A Systematic Review and Meta-analysis. J Digit Imaging. 2023 Jun;36(3):1158-1179. doi:10.1007/s10278-022-00766-w. SOURCE OF FUNDING: The study was financed in part by the Coordenacao de Aperfeicoamentode Pessoal de Nivel Superior-Brazil (CAPES)-Finance Code 001. TYPE OF STUDY/DESIGN: Systematic review and meta-analysis.


Assuntos
Pontos de Referência Anatômicos , Inteligência Artificial , Cefalometria , Humanos , Revisões Sistemáticas como Assunto , Metanálise como Assunto
2.
Orthod Craniofac Res ; 27(4): 535-543, 2024 Aug.
Artigo em Inglês | MEDLINE | ID: mdl-38321788

RESUMO

OBJECTIVE: To investigate the accuracy of artificial intelligence-assisted growth prediction using a convolutional neural network (CNN) algorithm and longitudinal lateral cephalograms (Lat-cephs). MATERIALS AND METHODS: A total of 198 Japanese preadolescent children, who had skeletal Class I malocclusion and whose Lat-cephs were available at age 8 years (T0) and 10 years (T1), were allocated into the training, validation, and test phases (n = 161, n = 17, n = 20). Orthodontists and the CNN model identified 28 hard-tissue landmarks (HTL) and 19 soft-tissue landmarks (STL). The mean prediction error values were defined as 'excellent,' 'very good,' 'good,' 'acceptable,' and 'unsatisfactory' (criteria: 0.5 mm, 1.0 mm, 1.5 mm, and 2.0 mm, respectively). The degree of accurate prediction percentage (APP) was defined as 'very high,' 'high,' 'medium,' and 'low' (criteria: 90%, 70%, and 50%, respectively) according to the percentage of subjects that showed the error range within 1.5 mm. RESULTS: All HTLs showed acceptable-to-excellent mean PE values, while the STLs Pog', Gn', and Me' showed unsatisfactory values, and the rest showed good-to-acceptable values. Regarding the degree of APP, HTLs Ba, ramus posterior, Pm, Pog, B-point, Me, and mandibular first molar root apex exhibited low APPs. The STLs labrale superius, lower embrasure, lower lip, point of lower profile, B', Pog,' Gn' and Me' also exhibited low APPs. The remainder of HTLs and STLs showed medium-to-very high APPs. CONCLUSION: Despite the possibility of using the CNN model to predict growth, further studies are needed to improve the prediction accuracy in HTLs and STLs of the chin area.


Assuntos
Pontos de Referência Anatômicos , Inteligência Artificial , Cefalometria , Má Oclusão Classe I de Angle , Redes Neurais de Computação , Humanos , Cefalometria/métodos , Criança , Feminino , Masculino , Pontos de Referência Anatômicos/diagnóstico por imagem , Má Oclusão Classe I de Angle/diagnóstico por imagem , Algoritmos , Desenvolvimento Maxilofacial , Previsões , Mandíbula/diagnóstico por imagem , Mandíbula/crescimento & desenvolvimento
3.
J Maxillofac Oral Surg ; 22(4): 806-812, 2023 Dec.
Artigo em Inglês | MEDLINE | ID: mdl-38105853

RESUMO

Introduction: Two-dimensional cephalometric image analysis plays a crucial role in orthodontic diagnosis and treatment planning. While deep learning-based algorithms have emerged to automate the laborious task of anatomical landmark annotation, their effectiveness is hampered by the challenges of acquiring and labelling clinical data. In this study, we propose a model that leverages conventional machine learning techniques to enhance the accuracy of landmark detection using limited dataset. Materials and methods: Our methodology involves coarse localization through region of interest (ROI) extraction and fine localization utilizing histogram-oriented gradient (HOG) feature. The image patch containing landmark pixels is classified using the light gradient boosting machine (LGBM) algorithm. To evaluate our model's performance, we conducted rigorous tests on the ISBI Cephalometric dataset and Dental Cepha dataset, aiming to achieve accuracy within a 2 mm radial precision range. We also employed cross-validation to assess our approach, providing a robust evaluation. Results: Our model's performance on the ISBI Cephalometric dataset showed an accuracy rate of 77.11% within the desired 2 mm radial precision range. The cross-validation results further confirmed the effectiveness of our approach, yielding a mean accuracy of 78.17%. Additionally, we applied our model to the Dental Cepha dataset, where we achieved a remarkable landmark detection accuracy of 84%. Conclusion: The results demonstrate that traditional machine learning techniques can be effective for accurate landmark detection in cephalometric images, even with limited data. Our findings highlight the potential of these techniques for clinical applications, where large datasets of labelled images may not be available.

4.
J Med Syst ; 47(1): 92, 2023 Aug 24.
Artigo em Inglês | MEDLINE | ID: mdl-37615881

RESUMO

The accuracy of cephalometric landmark identification for malocclusion classification is essential for diagnosis and treatment planning. Identifying these landmarks is often complex and time-consuming for orthodontists. An AI model for classification was recently developed. This model was investigated based on current regulatory considerations as a result of the strict regulations on software systems and the lack of information on artificial intelligence (AI) requirements in this publication. The platform developed by the ITU/WHO for AI is used to assess the models of the application. The auditing procedure assessed the development process concerning medical device regulations, data protection regulations, and ethical considerations. Upon that, the major tasks during the development were evaluated, such as qualification, annotation procedure, and data set attributes. The AI models were investigated under consideration of technical, clinical, regulatory, and ethical considerations. The risk to the patient and user's health can be considered low according to the International Medical Device Regulators Forum (IMDRF) definition. This application facilitates the decision and planning of malocclusion treatment based on lateral cephalograms without cephalometric landmarks. It is comparable with common standards in orthodontic diagnosis.


Assuntos
Inteligência Artificial , Má Oclusão , Humanos , Software
5.
J Orofac Orthop ; 2023 Aug 29.
Artigo em Inglês | MEDLINE | ID: mdl-37642657

RESUMO

PURPOSE: The aim of this investigation was to evaluate the accuracy of various skeletal and dental cephalometric parameters as produced by different commercial providers that make use of artificial intelligence (AI)-assisted automated cephalometric analysis and to compare their quality to a gold standard established by orthodontic experts. METHODS: Twelve experienced orthodontic examiners pinpointed 15 radiographic landmarks on a total of 50 cephalometric X­rays. The landmarks were used to generate 9 parameters for orthodontic treatment planning. The "humans' gold standard" was defined by calculating the median value of all 12 human assessments for each parameter, which in turn served as reference values for comparisons with results given by four different commercial providers of automated cephalometric analyses (DentaliQ.ortho [CellmatiQ GmbH, Hamburg, Germany], WebCeph [AssembleCircle Corp, Seongnam-si, Korea], AudaxCeph [Audax d.o.o., Ljubljana, Slovenia], CephX [Orca Dental AI, Herzliya, Israel]). Repeated measures analysis of variances (ANOVAs) were calculated and Bland-Altman plots were generated for comparisons. RESULTS: The results of the repeated measures ANOVAs indicated significant differences between the commercial providers' predictions and the humans' gold standard for all nine investigated parameters. However, the pairwise comparisons also demonstrate that there were major differences among the four commercial providers. While there were no significant mean differences between the values of DentaliQ.ortho and the humans' gold standard, the predictions of AudaxCeph showed significant deviations in seven out of nine parameters. Also, the Bland-Altman plots demonstrate that a reduced precision of AI predictions must be expected especially for values attributed to the inclination of the incisors. CONCLUSION: Fully automated cephalometric analyses are promising in terms of timesaving and avoidance of individual human errors. At present, however, they should only be used under supervision of experienced clinicians.

6.
Heliyon ; 9(6): e17459, 2023 Jun.
Artigo em Inglês | MEDLINE | ID: mdl-37416642

RESUMO

The identification of head landmarks in cephalometric analysis significantly contributes in the anatomical localization of maxillofacial tissues for orthodontic and orthognathic surgery. However, the existing methods face the limitations of low accuracy and cumbersome identification process. In this pursuit, the present study proposed an automatic target recognition algorithm called Multi-Scale YOLOV3 (MS-YOLOV3) for the detection of cephalometric landmarks. It was characterized by multi-scale sampling strategies for shallow and deep features at varied resolutions, and especially contained the module of spatial pyramid pooling (SPP) for highest resolution. The proposed method was quantitatively and qualitatively compared with the classical YOLOV3 algorithm on the two data sets of public lateral cephalograms, undisclosed anterior-posterior (AP) cephalograms, respectively, for evaluating the performance. The proposed MS-YOLOV3 algorithm showed better robustness with successful detection rates (SDR) of 80.84% within 2 mm, 93.75% within 3 mm, and 98.14% within 4 mm for lateral cephalograms, and 85.75% within 2 mm, 92.87% within 3 mm, and 96.66% within 4 mm for AP cephalograms, respectively. It was concluded that the proposed model could be robustly used to label the cephalometric landmarks on both lateral and AP cephalograms for the clinical application in orthodontic and orthognathic surgery.

7.
Dentomaxillofac Radiol ; 52(6): 20220362, 2023 Sep.
Artigo em Inglês | MEDLINE | ID: mdl-37427581

RESUMO

OBJECTIVES: To compare the precision of two cephalometric landmark identification methods, namely a computer-assisted human examination software and an artificial intelligence program, based on South African data. METHODS: This retrospective quantitative cross-sectional analytical study utilized a data set consisting of 409 cephalograms obtained from a South African population. 19 landmarks were identified in each of the 409 cephalograms by the primary researcher using the two programs [(409 cephalograms x 19 landmarks) x 2 methods = 15,542 landmarks)]. Each landmark generated two coordinate values (x, y), making a total of 31,084 landmarks. Euclidean distances between corresponding pairs of observations was calculated. Precision was determined by using the standard deviation and standard error of the mean. RESULTS: The primary researcher acted as the gold-standard and was calibrated prior to data collection. The inter- and intrareliability tests yielded acceptable results. Variations were present in several landmarks between the two approaches; however, they were statistically insignificant. The computer-assisted examination software was very sensitive to several variables. Several incidental findings were also discovered. Attempts were made to draw valid comparisons and conclusions. CONCLUSIONS: There was no significant difference between the two programs regarding the precision of landmark detection. The present study provides a basis to: (1) support the use of automatic landmark detection to be within the range of computer-assisted examination software and (2) determine the learning data required to develop AI systems within an African context.


Assuntos
Inteligência Artificial , Processamento de Imagem Assistida por Computador , Humanos , Estudos Transversais , Estudos Retrospectivos , Processamento de Imagem Assistida por Computador/métodos , Cefalometria/métodos , Reprodutibilidade dos Testes
8.
BMC Oral Health ; 23(1): 274, 2023 05 10.
Artigo em Inglês | MEDLINE | ID: mdl-37165409

RESUMO

BACKGROUND: One of the main uses of artificial intelligence in the field of orthodontics is automated cephalometric analysis. Aim of the present study was to evaluate whether developmental stages of a dentition, fixed orthodontic appliances or other dental appliances may affect detection of cephalometric landmarks. METHODS: For the purposes of this study a Convolutional Neural Network (CNN) for automated detection of cephalometric landmarks was developed. The model was trained on 430 cephalometric radiographs and its performance was then tested on 460 new radiographs. The accuracy of landmark detection in patients with permanent dentition was compared with that in patients with mixed dentition. Furthermore, the influence of fixed orthodontic appliances and orthodontic brackets and/or bands was investigated only in patients with permanent dentition. A t-test was performed to evaluate the mean radial errors (MREs) against the corresponding SDs for each landmark in the two categories, of which the significance was set at p < 0.05. RESULTS: The study showed significant differences in the recognition accuracy of the Ap-Inferior point and the Is-Superior point between patients with permanent dentition and mixed dentition, and no significant differences in the recognition process between patients without fixed orthodontic appliances and patients with orthodontic brackets and/or bands and other fixed orthodontic appliances. CONCLUSIONS: The results indicated that growth structures and developmental stages of a dentition had an impact on the performance of the customized CNN model by dental cephalometric landmarks. Fixed orthodontic appliances such as brackets, bands, and other fixed orthodontic appliances, had no significant effect on the performance of the CNN model.


Assuntos
Braquetes Ortodônticos , Ortodontia , Humanos , Inteligência Artificial , Redes Neurais de Computação , Aparelhos Ortodônticos , Cefalometria/métodos
9.
J Digit Imaging ; 36(3): 1158-1179, 2023 06.
Artigo em Inglês | MEDLINE | ID: mdl-36604364

RESUMO

Using computer vision through artificial intelligence (AI) is one of the main technological advances in dentistry. However, the existing literature on the practical application of AI for detecting cephalometric landmarks of orthodontic interest in digital images is heterogeneous, and there is no consensus regarding accuracy and precision. Thus, this review evaluated the use of artificial intelligence for detecting cephalometric landmarks in digital imaging examinations and compared it to manual annotation of landmarks. An electronic search was performed in nine databases to find studies that analyzed the detection of cephalometric landmarks in digital imaging examinations with AI and manual landmarking. Two reviewers selected the studies, extracted the data, and assessed the risk of bias using QUADAS-2. Random-effects meta-analyses determined the agreement and precision of AI compared to manual detection at a 95% confidence interval. The electronic search located 7410 studies, of which 40 were included. Only three studies presented a low risk of bias for all domains evaluated. The meta-analysis showed AI agreement rates of 79% (95% CI: 76-82%, I2 = 99%) and 90% (95% CI: 87-92%, I2 = 99%) for the thresholds of 2 and 3 mm, respectively, with a mean divergence of 2.05 (95% CI: 1.41-2.69, I2 = 10%) compared to manual landmarking. The menton cephalometric landmark showed the lowest divergence between both methods (SMD, 1.17; 95% CI, 0.82; 1.53; I2 = 0%). Based on very low certainty of evidence, the application of AI was promising for automatically detecting cephalometric landmarks, but further studies should focus on testing its strength and validity in different samples.


Assuntos
Algoritmos , Inteligência Artificial , Humanos , Reprodutibilidade dos Testes , Cefalometria/métodos , Processamento Eletrônico de Dados
10.
Int Orthod ; 20(4): 100691, 2022 12.
Artigo em Inglês | MEDLINE | ID: mdl-36114136

RESUMO

OBJECTIVE: To compare the reliability of cephalometric landmark identification by an automated tracing software based on convolutional neural networks to human tracers. MATERIALS AND METHODS: Sixty cephalograms were traced by two board-certified orthodontists and AudaxCeph®'s artificial intelligence software. The following thirteen landmarks were identified in each tracing: Sella, Nasion, A point, B point, Porion, Menton, Pogonion, Orbitale, Gonion, Upper Central Incisor Incisal Edge (U1 Tip), Upper Central Incisor Root Apex (U1 apex), Lower Central Incisor Incisal Edge (L1 Tip), Lower Central Incisor Root Apex (L1 apex). An x-y axis was positioned in the bottom left corner of each cephalogram, and the x- and y-coordinates for the landmarks were exported into Excel. Distributions of landmarks (X, Y, radial distance) were compared using t-tests of equivalence with a 2mm equivalence bound. These compared the AI position to the two orthodontists - and the orthodontists' reliability by comparing equivalence against each other. RESULTS: There was no statistical difference between the orthodontists and AudaxCeph®'s automatic tracing software except for the x- and y-dimension of Porion and the y-dimension of L1 apex. The two orthodontists had good intra-examiner reliability with no statistical difference found when comparing them. CONCLUSION: AudaxCeph®'s automated cephalometric tracing software is a good adjunctive tool to use when diagnosing and treatment planning orthodontic cases.


Assuntos
Inteligência Artificial , Tecnologia , Humanos , Reprodutibilidade dos Testes , Cefalometria/métodos , Radiografia
11.
Orthod Craniofac Res ; 24 Suppl 2: 53-58, 2021 Dec.
Artigo em Inglês | MEDLINE | ID: mdl-34145974

RESUMO

AIM: To estimate the number of cephalograms needed to re-learn for different quality images, when artificial intelligence (AI) systems are introduced in a clinic. SETTINGS AND SAMPLE POPULATION: A total of 2385 digital lateral cephalograms (University data [1785]; Clinic F [300]; Clinic N [300]) were used. Using data from the university and clinics F and N, and combined data from clinics F and N, 50 cephalograms were randomly selected to test the system's performance (Test-data O, F, N, FN). MATERIALS AND METHODS: To examine the recognition ability of landmark positions of the AI system developed in Part I (Original System) for other clinical data, test data F, N and FN were applied to the original system, and success rates were calculated. Then, to determine the approximate number of cephalograms needed to re-learn for different quality images, 85 and 170 cephalograms were randomly selected from each group and used for the re-learning (F85, F170, N85, N170, FN85 and FN170) of the original system. To estimate the number of cephalograms needed for re-learning, we examined the changes in the success rate of the re-trained systems and compared them with the original system. Re-trained systems F85 and F170 were evaluated with test data F, N85 and N170 from test data N, and FN85 and FN170 from test data FN. RESULTS: For systems using F, N and FN, it was determined that 85, 170 and 85 cephalograms, respectively, were required for re-learning. CONCLUSIONS: The number of cephalograms needed to re-learn for images of different quality was estimated.


Assuntos
Inteligência Artificial , Cefalometria , Humanos , Radiografia
12.
Orthod Craniofac Res ; 24 Suppl 2: 43-52, 2021 Dec.
Artigo em Inglês | MEDLINE | ID: mdl-34021976

RESUMO

OBJECTIVES: To determine whether AI systems that recognize cephalometric landmarks can apply to various patient groups and to examine the patient-related factors associated with identification errors. SETTING AND SAMPLE POPULATION: The present retrospective cohort study analysed digital lateral cephalograms obtained from 1785 Japanese orthodontic patients. Patients were categorized into eight subgroups according to dental age, cleft lip and/or palate, orthodontic appliance use and overjet. MATERIALS AND METHODS: An AI system that automatically recognizes anatomic landmarks on lateral cephalograms was used. Thirty cephalograms in each subgroup were randomly selected and used to test the system's performance. The remaining cephalograms were used for system learning. The success rates in landmark recognition were evaluated using confidence ellipses with α = 0.99 for each landmark. The selection of test samples, learning of the system and evaluation of the system were repeated five times for each subgroup. The mean success rate and identification error were calculated. Factors associated with identification errors were examined using a multiple linear regression model. RESULTS: The success rate and error varied among subgroups, ranging from 85% to 91% and 1.32 mm to 1.50 mm, respectively. Cleft lip and/or palate was found to be a factor associated with greater identification errors, whereas dental age, orthodontic appliances and overjet were not significant factors (all, P < .05). CONCLUSION: Artificial intelligence systems that recognize cephalometric landmarks could be applied to various patient groups. Patient-oriented errors were found in patients with cleft lip and/or palate.


Assuntos
Fenda Labial , Fissura Palatina , Inteligência Artificial , Cefalometria , Fenda Labial/diagnóstico por imagem , Fissura Palatina/diagnóstico por imagem , Humanos , Estudos Retrospectivos
13.
Angle Orthod ; 90(1): 69-76, 2020 01.
Artigo em Inglês | MEDLINE | ID: mdl-31335162

RESUMO

OBJECTIVES: To compare detection patterns of 80 cephalometric landmarks identified by an automated identification system (AI) based on a recently proposed deep-learning method, the You-Only-Look-Once version 3 (YOLOv3), with those identified by human examiners. MATERIALS AND METHODS: The YOLOv3 algorithm was implemented with custom modifications and trained on 1028 cephalograms. A total of 80 landmarks comprising two vertical reference points and 46 hard tissue and 32 soft tissue landmarks were identified. On the 283 test images, the same 80 landmarks were identified by AI and human examiners twice. Statistical analyses were conducted to detect whether any significant differences between AI and human examiners existed. Influence of image factors on those differences was also investigated. RESULTS: Upon repeated trials, AI always detected identical positions on each landmark, while the human intraexaminer variability of repeated manual detections demonstrated a detection error of 0.97 ± 1.03 mm. The mean detection error between AI and human was 1.46 ± 2.97 mm. The mean difference between human examiners was 1.50 ± 1.48 mm. In general, comparisons in the detection errors between AI and human examiners were less than 0.9 mm, which did not seem to be clinically significant. CONCLUSIONS: AI showed as accurate an identification of cephalometric landmarks as did human examiners. AI might be a viable option for repeatedly identifying multiple cephalometric landmarks.


Assuntos
Algoritmos , Pontos de Referência Anatômicos , Cefalometria , Automação , Humanos , Radiografia , Reprodutibilidade dos Testes
14.
PeerJ ; 7: e8200, 2019.
Artigo em Inglês | MEDLINE | ID: mdl-31844585

RESUMO

AIMS: To define midfacial position differentiating maxillary and zygomatic regions and to evaluate the corresponding cephalometric characteristics discerning midfacial flatness and fullness. MATERIAL AND METHODS: A total of 183 pretreatment lateral cephalometric radiographs of non-growing orthodontic patients (age 25.98 ± 8.43 years) screened at our university orthodontic clinic. The lateral cephalographs of the orthodontic patients were stratified in four groups: flat, normal toward flat, normal toward full, full,according to distances from nasion and sella to points J and G (NJ, SJ, NG and SG). J is the midpoint of the distance connecting orbitale to point A, and G the center of the triangle connecting orbit, key ridge and pterygomaxillary fissure. Statistics included the Kendall tau-b test for best associations among measurements. RESULTS: All measurements were statistically significantly different between flat and full groups. The highest associations were between NJ and SJ (τb = 0.71; p < 0.001) and NG and SG (τb = 0.70; p < 0.001). Flat midfaces were characterized by canting of the cranial base and palatal plane, hyperdivergent pattern and maxillary retrognathism. The opposite was true for fuller midfaces. CONCLUSION: Midface skeletal location was assessed differentially in the naso-maxillary and malo-zygomatic structures differentially. Craniofacial characteristics were identified according to this stratification, indicating the potential for application in facial diagnosis and need for testing on 3D cone-beam computed tomography images.

15.
Angle Orthod ; 89(6): 903-909, 2019 11.
Artigo em Inglês | MEDLINE | ID: mdl-31282738

RESUMO

OBJECTIVE: To compare the accuracy and computational efficiency of two of the latest deep-learning algorithms for automatic identification of cephalometric landmarks. MATERIALS AND METHODS: A total of 1028 cephalometric radiographic images were selected as learning data that trained You-Only-Look-Once version 3 (YOLOv3) and Single Shot Multibox Detector (SSD) methods. The number of target labeling was 80 landmarks. After the deep-learning process, the algorithms were tested using a new test data set composed of 283 images. Accuracy was determined by measuring the point-to-point error and success detection rate and was visualized by drawing scattergrams. The computational time of both algorithms was also recorded. RESULTS: The YOLOv3 algorithm outperformed SSD in accuracy for 38 of 80 landmarks. The other 42 of 80 landmarks did not show a statistically significant difference between YOLOv3 and SSD. Error plots of YOLOv3 showed not only a smaller error range but also a more isotropic tendency. The mean computational time spent per image was 0.05 seconds and 2.89 seconds for YOLOv3 and SSD, respectively. YOLOv3 showed approximately 5% higher accuracy compared with the top benchmarks in the literature. CONCLUSIONS: Between the two latest deep-learning methods applied, YOLOv3 seemed to be more promising as a fully automated cephalometric landmark identification system for use in clinical practice.


Assuntos
Algoritmos , Cefalometria , Sulfadiazina de Prata , Aprendizado Profundo , Reprodutibilidade dos Testes
16.
Angle Orthod ; 85(1): 3-10, 2015 Jan.
Artigo em Inglês | MEDLINE | ID: mdl-24866835

RESUMO

OBJECTIVES: To identify two novel three-dimensional (3D) cephalometric landmarks and create a novel three-dimensionally based anteroposterior skeletal measurement that can be compared with traditional two-dimensional (2D) cephalometric measurements in patients with Class I and Class II skeletal patterns. MATERIALS AND METHODS: Full head cone-beam computed tomography (CBCT) scans of 100 patients with all first molars in occlusion were obtained from a private practice. InvivoDental 3D (version 5.1.6, Anatomage, San Jose, Calif) was used to analyze the CBCT scans in the sagittal and axial planes to create new landmarks and a linear 3D analysis (M measurement) based on maxillary and mandibular centroids. Independent samples t-test was used to compare the mean M measurement to traditional 2D cephalometric measurements, ANB and APDI. Interexaminer and intraexaminer reliability were evaluated using 2D and 3D scatterplots. RESULTS: The M measurement, ANB, and APDI could statistically differentiate between patients with Class I and Class II skeletal patterns (P < .001). The M measurement exhibited a correlation coefficient (r) of -0.79 and 0.88 with APDI and ANB, respectively. CONCLUSIONS: The overall centroid landmarks and the M measurement combine 2D and 3D methods of imaging; the measurement itself can distinguish between patients with Class I and Class II skeletal patterns and can serve as a potential substitute for ANB and APDI. The new three-dimensionally based landmarks and measurements are reliable, and there is great potential for future use of 3D analyses for diagnosis and research.


Assuntos
Pontos de Referência Anatômicos/diagnóstico por imagem , Cefalometria/métodos , Tomografia Computadorizada de Feixe Cônico/métodos , Imageamento Tridimensional/métodos , Processo Alveolar/diagnóstico por imagem , Cefalometria/estatística & dados numéricos , Tomografia Computadorizada de Feixe Cônico/estatística & dados numéricos , Humanos , Processamento de Imagem Assistida por Computador/métodos , Incisivo/diagnóstico por imagem , Má Oclusão Classe I de Angle/diagnóstico por imagem , Má Oclusão Classe II de Angle/diagnóstico por imagem , Mandíbula/diagnóstico por imagem , Maxila/diagnóstico por imagem , Dente Molar/diagnóstico por imagem , Osso Nasal/diagnóstico por imagem , Variações Dependentes do Observador , Reprodutibilidade dos Testes , Estudos Retrospectivos
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