Accuracy of automated 3D cephalometric landmarks by deep learning algorithms: systematic review and meta-analysis

Objectives The aim of the present systematic review and meta-analysis is to assess the accuracy of automated landmarking using deep learning in comparison with manual tracing for cephalometric analysis of 3D medical images. Methods PubMed/Medline, IEEE Xplore, Scopus and ArXiv electronic databases were searched. Selection criteria were: ex vivo and in vivo volumetric data images suitable for 3D landmarking (Problem), a minimum of five automated landmarking performed by deep learning method (Intervention), manual landmarking (Comparison), and mean accuracy, in mm, between manual and automated landmarking (Outcome). QUADAS-2 was adapted for quality analysis. Meta-analysis was performed on studies that reported as outcome mean values and standard deviation of the difference (error) between manual and automated landmarking. Linear regression plots were used to analyze correlations between mean accuracy and year of publication. Results The initial electronic screening yielded 252 papers published between 2020 and 2022. A total of 15 studies were included for the qualitative synthesis, whereas 11 studies were used for the meta-analysis. Overall random effect model revealed a mean value of 2.44 mm, with a high heterogeneity (I-2 = 98.13%, tau(2) = 1.018, p-value < 0.001); risk of bias was high due to the presence of issues for several domains per study. Meta-regression indicated a significant relation between mean error and year of publication (p value = 0.012). Conclusion Deep learning algorithms showed an excellent accuracy for automated 3D cephalometric landmarking. In the last two years promising algorithms have been developed and improvements in landmarks annotation accuracy have been done

Artificial Intelligence in Orthodontics: Where Are We Now? A Scoping Review

Objective: This scoping review aims to determine the applications of Artificial Intelligence (AI) that are extensively employed in the field of Orthodontics, to evaluate its benefits, and to discuss its potential implications in this speciality. Recent decades have witnessed enormous changes in our profession. The arrival of new and more aesthetic options in orthodontic treatment, the transition to a fully digital workflow, the emergence of temporary anchorage devices and new imaging methods all provide both patients and professionals with a new focus in orthodontic care. Materials and methods: This review was performed following the Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) guidelines. The electronic literature search was performed through MEDLINE/PubMed, Scopus, Web of Science, Cochrane and IEEE Xplore databases with a 11-year time restriction: January 2010 till March 2021. No additional manual searches were performed. Results: The electronic literature search initially returned 311 records, and 115 after removing duplicate references. Finally, the application of the inclusion criteria resulted in 17 eligible publications in the qualitative synthesis review. Conclusion: The analysed studies demonstrated that Convolution Neural Networks can be used for the automatic detection of anatomical reference points on radiological images. In the growth and development research area, the Cervical Vertebral Maturation stage can be determined using an Artificial Neural Network model and obtain the same results as expert human observers. AI technology can also improve the diagnostic accuracy for orthodontic treatments, thereby helping the orthodontist work more accurately and efficiently

Automatic Three-Dimensional Cephalometric Annotation System Using Three-Dimensional Convolutional Neural Networks

Background: Three-dimensional (3D) cephalometric analysis using computerized tomography data has been rapidly adopted for dysmorphosis and anthropometry. Several different approaches to automatic 3D annotation have been proposed to overcome the limitations of traditional cephalometry. The purpose of this study was to evaluate the accuracy of our newly-developed system using a deep learning algorithm for automatic 3D cephalometric annotation. Methods: To overcome current technical limitations, some measures were developed to directly annotate 3D human skull data. Our deep learning-based model system mainly consisted of a 3D convolutional neural network and image data resampling. Results: The discrepancies between the referenced and predicted coordinate values in three axes and in 3D distance were calculated to evaluate system accuracy. Our new model system yielded prediction errors of 3.26, 3.18, and 4.81 mm (for three axes) and 7.61 mm (for 3D). Moreover, there was no difference among the landmarks of the three groups, including the midsagittal plane, horizontal plane, and mandible (p>0.05). Conclusion: A new 3D convolutional neural network-based automatic annotation system for 3D cephalometry was developed. The strategies used to implement the system were detailed and measurement results were evaluated for accuracy. Further development of this system is planned for full clinical application of automatic 3D cephalometric annotation

3D superimposition of craniofacial imaging—The utility of multicentre collaborations

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/149360/1/ocr12281.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/149360/2/ocr12281_am.pd

Relational Reasoning Network (RRN) for Anatomical Landmarking

Accurately identifying anatomical landmarks is a crucial step in deformation analysis and surgical planning for craniomaxillofacial (CMF) bones. Available methods require segmentation of the object of interest for precise landmarking. Unlike those, our purpose in this study is to perform anatomical landmarking using the inherent relation of CMF bones without explicitly segmenting them. We propose a new deep network architecture, called relational reasoning network (RRN), to accurately learn the local and the global relations of the landmarks. Specifically, we are interested in learning landmarks in CMF region: mandible, maxilla, and nasal bones. The proposed RRN works in an end-to-end manner, utilizing learned relations of the landmarks based on dense-block units and without the need for segmentation. For a given a few landmarks as input, the proposed system accurately and efficiently localizes the remaining landmarks on the aforementioned bones. For a comprehensive evaluation of RRN, we used cone-beam computed tomography (CBCT) scans of 250 patients. The proposed system identifies the landmark locations very accurately even when there are severe pathologies or deformations in the bones. The proposed RRN has also revealed unique relationships among the landmarks that help us infer several reasoning about informativeness of the landmark points. RRN is invariant to order of landmarks and it allowed us to discover the optimal configurations (number and location) for landmarks to be localized within the object of interest (mandible) or nearby objects (maxilla and nasal). To the best of our knowledge, this is the first of its kind algorithm finding anatomical relations of the objects using deep learning.Comment: 10 pages, 6 Figures, 3 Table

Face the Future-Artificial Intelligence in Oral and Maxillofacial Surgery.

Artificial intelligence (AI) has emerged as a versatile health-technology tool revolutionizing medical services through the implementation of predictive, preventative, individualized, and participatory approaches. AI encompasses different computational concepts such as machine learning, deep learning techniques, and neural networks. AI also presents a broad platform for improving preoperative planning, intraoperative workflow, and postoperative patient outcomes in the field of oral and maxillofacial surgery (OMFS). The purpose of this review is to present a comprehensive summary of the existing scientific knowledge. The authors thoroughly reviewed English-language PubMed/MEDLINE and Embase papers from their establishment to 1 December 2022. The search terms were (1) "OMFS" OR "oral and maxillofacial" OR "oral and maxillofacial surgery" OR "oral surgery" AND (2) "AI" OR "artificial intelligence". The search format was tailored to each database's syntax. To find pertinent material, each retrieved article and systematic review's reference list was thoroughly examined. According to the literature, AI is already being used in certain areas of OMFS, such as radiographic image quality improvement, diagnosis of cysts and tumors, and localization of cephalometric landmarks. Through additional research, it may be possible to provide practitioners in numerous disciplines with additional assistance to enhance preoperative planning, intraoperative screening, and postoperative monitoring. Overall, AI carries promising potential to advance the field of OMFS and generate novel solution possibilities for persisting clinical challenges. Herein, this review provides a comprehensive summary of AI in OMFS and sheds light on future research efforts. Further, the advanced analysis of complex medical imaging data can support surgeons in preoperative assessments, virtual surgical simulations, and individualized treatment strategies. AI also assists surgeons during intraoperative decision-making by offering immediate feedback and guidance to enhance surgical accuracy and reduce complication rates, for instance by predicting the risk of bleeding

Contributions to the three-dimensional virtual treatment planning of orthognathic surgery

Orientadores: José Mario De Martino, Luis Augusto PasseriTese (doutorado) - Universidade Estadual de Campinas, Faculdade de Engenharia Elétrica e de ComputaçãoResumo: A tecnologia mais recente à disposição da Cirurgia Ortognática possibilita que o diagnóstico e o planejamento do tratamento das deformidades dentofaciais sejam realizados sob uma representação virtual tridimensional (3D) da cabeça do paciente. Com o propósito de contribuir para o aperfeiçoamento desta tecnologia, o trabalho apresentado nesta tese identificou e tratou quatro problemas. A primeira contribuição consistiu na verificação da validade da hipótese de que a mudança de definição do plano horizontal de Frankfort não produz diferenças de medição clinicamente relevantes quando sob indivíduos cujos crânios são consideravelmente simétricos. Os resultados da análise realizada no contexto deste tese indicam que, ao contrário do que se presumia, a hipótese é falsa. A segunda contribuição consistiu na extensão do método de análise cefalométrica de McNamara para que ele pudesse produzir valores 3D. Ao contrário de outros métodos de análise cefalométrica 3D, a extensão criada produz valores verdadeiramente 3D, não perde as informações do método original e preserva as definições geométricas originais das linhas e planos cefalométricos. A terceira contribuição consistiu a) no estabelecimento de normas cefalométricas para brasileiros adultos de ascendência europeia, a partir de imagens de tomografia computadorizada de feixe cônico, que produz uma imagem craniofacial mais precisa e confiável do que a telerradiografia; e b) na avaliação de dimorfismo sexual, para a identificação de características anatômicas diferenciadas entre homens e mulheres desta população. A quarta e última contribuição consistiu na automatização da principal etapa da tecnologia em questão, na qual o cirurgião executa o reposicionamento dos segmentos ósseos maxilares no crânio. O método criado é capaz de corrigir automaticamente os problemas dentofaciais mais comuns tratados pela Cirurgia Ortognática, que envolvem maloclusão esquelética, assimetria facial e discrepância de maxilares. Todas as contribuições deste trabalho foram publicadas em periódicos internacionais do campo da Odontologia e afinsAbstract: The latest technology available for orthognathic surgery allows the diagnosis and treatment planning of dentofacial deformities based on a three-dimensional (3D) virtual representation of the patient's head. In order to contribute to the improvement of this technology, the work presented in this thesis identified and treated four problems. The first contribution consisted in testing the validity of the hypothesis that changing the definition of the Frankfort horizontal plane does not produce clinically relevant measurement differences for subjects whose skulls are considerably symmetrical. The results of the analysis performed in this thesis indicate that, contrary to what was presumed, the hypothesis is false. The second contribution is an extension of the McNamara's method of cephalometric analysis to produce 3D values. Unlike other methods of 3D cephalometric analysis, the extension produces true 3D values, does not lose information captured by the original method, and preserves the original geometric definitions of the cephalometric lines and planes. The third contribution consisted in a) establishing cephalometric norms for Brazilian adults of European descent, based on images from cone-beam computed tomography, which produce a more accurate and reliable craniofacial image than cephalometric radiography; and b) evaluating sexual dimorphism, for the identification of distinct anatomic features between males and females of this population. The fourth contribution consisted in automating the main stage of the technology in question, in which the surgeon performs the positioning of jaw bone segments in the skull. The created method is able to automatically correct the most common dentofacial problems treated by orthognathic surgery, which involves skeletal malocclusion, facial asymmetry, and jaw discrepancy. The contributions of this work were published in international journals of the field of Dentistry and relatedDoutoradoEngenharia de ComputaçãoDoutor em Engenharia ElétricaCAPE