Automatic landmark identification in cone-beam computed tomography

Deep Learning-Based Three-Dimensional Analysis Reveals Distinct Patterns of Condylar Remodelling After Orthognathic Surgery in Skeletal Class III Patients

OBJECTIVE

This retrospective study aimed to evaluate morphometric changes in mandibular condyles of patients with skeletal Class III malocclusion following two-jaw orthognathic surgery planned using virtual surgical planning (VSP) and analysed with automated three-dimensional (3D) image analysis based on deep-learning techniques.

MATERIALS AND METHODS

Pre-operative (T1) and 12-18 months post-operative (T2) Cone-Beam Computed Tomography (CBCT) scans of 17 patients (mean age: 24.8 ± 3.5 years) were analysed using 3DSlicer software. Deep-learning algorithms automated CBCT orientation, registration, bone segmentation, and landmark identification. By utilising voxel-based superimposition of pre- and post-operative CBCT scans and shape correspondence, the overall changes in condylar morphology were assessed, with a focus on bone resorption and apposition at specific regions (superior, lateral and medial poles). The correlation between these modifications and the extent of actual condylar movements post-surgery was investigated. Statistical analysis was conducted with a significance level of α = 0.05.

RESULTS

Overall condylar remodelling was minimal, with mean changes of < 1 mm. Small but statistically significant bone resorption occurred at the condylar superior articular surface, while bone apposition was primarily observed at the lateral pole. The bone apposition at the lateral pole and resorption at the superior articular surface were significantly correlated with medial condylar displacement (p < 0.05).

CONCLUSION

The automated 3D analysis revealed distinct patterns of condylar remodelling following orthognathic surgery in skeletal Class III patients, with minimal overall changes but significant regional variations. The correlation between condylar displacements and remodelling patterns highlights the need for precise pre-operative planning to optimise condylar positioning, potentially minimising harmful remodelling and enhancing stability.

Automatic identification of hard and soft tissue landmarks in cone-beam computed tomography via deep learning with diversity datasets: a methodological study

BACKGROUND

Manual landmark detection in cone beam computed tomography (CBCT) for evaluating craniofacial structures relies on medical expertise and is time-consuming. This study aimed to apply a new deep learning method to predict and locate soft and hard tissue craniofacial landmarks on CBCT in patients with various types of malocclusion.

METHODS

A total of 498 CBCT images were collected. Following the calibration procedure, two experienced clinicians identified 43 landmarks in the x-, y-, and z-coordinate planes on the CBCT images using Checkpoint Software, creating the ground truth by averaging the landmark coordinates. To evaluate the accuracy of our algorithm, we determined the mean absolute error along the x-, y-, and z-axes and calculated the mean radial error (MRE) between the reference landmark and predicted landmark, as well as the successful detection rate (SDR).

RESULTS

Each landmark prediction took approximately 4.2 s on a conventional graphics processing unit. The mean absolute error across all coordinates was 0.74 mm. The overall MRE for the 43 landmarks was 1.76 ± 1.13 mm, and the SDR was 60.16%, 91.05%, and 97.58% within 2-, 3-, and 4-mm error ranges of manual marking, respectively. The average MRE of the hard tissue landmarks (32/43) was 1.73 mm, while that for soft tissue landmarks (11/43) was 1.84 mm.

CONCLUSIONS

Our proposed algorithm demonstrates a clinically acceptable level of accuracy and robustness for automatic detection of CBCT soft- and hard-tissue landmarks across all types of malformations. The potential for artificial intelligence to assist in identifying three dimensional-CT landmarks in routine clinical practice and analysing large datasets for future research is promising.

Automatic segmentation and landmark detection of 3D CBCT images using semi supervised learning for assisting orthognathic surgery planning

Patients with abnormal relative position of the upper and lower jaws (the main part of the facial bones) require orthognathic surgery to improve the occlusal relationship and facial appearance. However, in addition to the retraction and protrusion of the maxillomandibular advancement, these patients may also develop asymmetry. This study aims to use a semi-supervised learning method to demonstrate the maxillary and mandible retraction, protrudation and asymmetry of patients before orthognathic surgery through automatic segmentation of 3D cone beam computed tomography (CBCT) images and landmark detection, so as to provide help for the preoperative planning of orthognathic surgery. Among them, the dice of the semi-supervised algorithm adopted in this study reached 93.41 and 96.89% in maxillary and mandibular segmentation tasks, and the average error of landmark detection tasks reached 1.908 ± 1.166 mm, both of which were superior to the full-supervised algorithm with the same data volume annotation. Therefore, we propose that the method can be applied in a clinical setting to assist surgeons in preoperative planning for orthognathic surgery.

Selection of density standard and X-ray tube settings for computed digital absorptiometry in horses using the k-means clustering algorithm

BACKGROUND

In veterinary medicine, conventional radiography is the first-choice method for most diagnostic imaging applications in both small animal and equine practice. One direction in its development is the integration of bone density evaluation and artificial intelligence-assisted clinical decision-making, which is expected to enhance and streamline veterinarians' daily practices. One such decision-support method is k-means clustering, a machine learning and data mining technique that can be used clinically to classify radiographic signs into healthy or affected clusters. The study aims to investigate whether the k-means clustering algorithm can differentiate cortical and trabecular bone in both healthy and affected horse limbs. Therefore, identifying the optimal computed digital absorptiometry parameters was necessary.

METHODS AND RESULTS

Five metal-made density standards, made of pure aluminum, aluminum alloy (duralumin), cuprum alloy, iron-nickel alloy, and iron-silicon alloy, and ten X-ray tube settings were evaluated for the radiographic imaging of equine distal limbs, including six healthy limbs and six with radiographic signs of osteoarthritis. Density standards were imaged using ten combinations of X-ray tube settings, ranging from 50 to 90 kV and 1.2 to 4.0 mAs. The relative density in Hounsfield units was firstly returned for both bone types and density standards, then compared, and finally used for clustering. In both healthy and osteoarthritis-affected limbs, the relative density of the long pastern bone (the proximal phalanx) differed between bone types, allowing the k-means clustering algorithm to successful differentiate cortical and trabecular bone.

CONCLUSION

Density standard made of duralumin, along with the 60 kV, 4.0 mAs X-ray tube settings, yielded the highest clustering metric values and was therefore considered optimal for further research. We believe that the identified optimal computed digital absorptiometry parameters may be recommended for further researches on the relative quantification of conventional radiographs and for distal limb examination in equine veterinary practice.

Maxillary protraction anchored on miniplates versus miniscrews: three-dimensional dentoskeletal comparison

OBJECTIVE

This retrospective study aimed to compare the three-dimensional (3D) outcomes of the novel miniscrew-anchored maxillary protraction (MAMP) therapy and the bone-anchored maxillary protraction (BAMP) therapy.

METHODS

The sample comprised growing patients with skeletal Class III malocclusion treated with two skeletal anchored maxillary protraction protocols. The MAMP group comprised 22 patients (9 female, 13 male; 10.9 ± 0.9 years of age at baseline) treated with Class III elastics anchored on a hybrid hyrax expander in the maxilla and two mandibular miniscrews distally to the permanent canines. The BAMP group comprised 24 patients (14 female, 10 male; 11.6 ± 1.1 years of age at baseline) treated with Class III elastic anchored in two titanium miniplates in the infra-zygomatic crest and two miniplates in the mesial of the mandibular permanent canines. Three-dimensional displacements were measured in the pre- and post-treatment cone-beam computed tomography scans superimposed on the cranial base using the Slicer Automated Dental Tools module of 3D Slicer software (www.slicer.org). Mean differences (MD) between groups and 95% confidence interval (CI) were obtained for all variables. Intergroup comparison was performed using the Analysis of Covariance (P < .05).

RESULTS

Both groups showed improvements after treatment. The MAMP group showed a smaller anterior (MD: -1.09 mm; 95% CI, -2.07 to -0.56) and 3D (MD: -1.27 mm; 95% CI, -2.16 to -0.74) displacements of the maxilla after treatment when compared with BAMP. Both groups showed negligible and similar anteroposterior changes in the mandible (MD: 0.33 mm; 95% CI, -2.15 to 1.34). A greater increase in the nasal cavity width (MD of 2.36; 95% CI, 1.97-3.05) was observed in the MAMP group when compared with BAMP.

LIMITATIONS

The absence of an untreated control group to assess the possible growth impact in these findings is a limitation of this study.

CONCLUSION

Both BAMP and MAMP therapies showed adequate 3D outcomes after treatment. However, BAMP therapy produced a greater maxillary advancement with treatment, while MAMP therapy showed greater transversal increases in the nasal cavity.

Accuracy Assessment of EM3D App-Based 3D Facial Scanning Compared to Cone Beam Computed Tomography

Background: The use of 3D facial scans is becoming essential for dental practice. However, traditional scanners require labor-intensive procedures and are expensive, making them less accessible in routine clinical practice. In this context, high-performance smartphones and dedicated apps offer a more accessible alternative. This study aims to validate the accuracy of the EM3D app, which utilizes the iPhone's TrueDepth camera technology, by comparing it to Cone Beam Computed Tomography (CBCT). Methods: Thirty patients requiring CBCT scans were recruited for the study. Facial scans obtained with the TrueDepth camera of the iPhone 13 Pro in conjunction with EM3D app were automatically superimposed onto the 3D models derived from the CBCTs through the implementation of a deep learning methodology. The approach enabled the automatic identification of fifteen landmarks to perform linear and angular measurements for quantitative assessment. A color map was created to highlight discrepancies between the overlaid meshes, and the overall surface differences between the models were automatically quantified. Results: The overall surface difference between the CBCT and EM3D scans was highly accurate, with a mean discrepancy of 0.387 ± 0.361 mm. The mean discrepancies of most measurements were lower than 1 mm (five out of six; 83.33%) between the groups, with no significant differences (p > 0.05). Conclusions: The combination of the iPhone's TrueDepth camera and the EM3D app exhibited high accuracy for 3D facial modeling. This makes it a cost-effective alternative to professional scanning systems.

A Comparative Study of Deep Learning and Manual Methods for Identifying Anatomical Landmarks through Cephalometry and Cone-Beam Computed Tomography: A Systematic Review and Meta-Analysis

Background: Researchers have noted that the advent of artificial intelligence (AI) heralds a promising era, with potential to significantly enhance diagnostic and predictive abilities in clinical settings. The aim of this meta-analysis is to evaluate the discrepancies in identifying anatomical landmarks between AI and manual approaches. Methods: A comprehensive search strategy was employed, incorporating controlled vocabulary (MeSH) and free-text terms. This search was conducted by two reviewers to identify published systematic reviews. Three major electronic databases, namely, Medline via PubMed, the Cochrane database, and Embase, were searched up to May 2024. Results: Initially, 369 articles were identified. After conducting a comprehensive search and applying strict inclusion criteria, a total of ten studies were deemed eligible for inclusion in the meta-analysis. The results showed that the average difference in detecting anatomical landmarks between artificial intelligence and manual approaches was 0.35, with a 95% confidence interval (CI) ranging from −0.09 to 0.78. Additionally, the overall effect between the two groups was found to be insignificant. Upon further analysis of the subgroup of cephalometric radiographs, it was determined that there were no significant differences between the two groups in terms of detecting anatomical landmarks. Similarly, the subgroup of cone-beam computed tomography (CBCT) revealed no significant differences between the groups. Conclusions: In summary, the study concluded that the use of artificial intelligence is just as effective as the manual approach when it comes to detecting anatomical landmarks, both in general and in specific contexts such as cephalometric radiographs and CBCT evaluations.

The Implications of Artificial Intelligence in Pedodontics: A Scoping Review of Evidence-Based Literature

BACKGROUND

Artificial intelligence (AI) has emerged as a revolutionary technology with several applications across different dental fields, including pedodontics. This systematic review has the objective to catalog and explore the various uses of artificial intelligence in pediatric dentistry.

METHODS

A thorough exploration of scientific databases was carried out to identify studies addressing the usage of AI in pediatric dentistry until December 2023 in the Embase, Scopus, PubMed, and Web of Science databases by two researchers, S.L.R. and A.L.G.

RESULTS

From a pool of 1301 articles, only 64 met the predefined criteria and were considered for inclusion in this review. From the data retrieved, it was possible to provide a narrative discussion of the potential implications of AI in the specialized area of pediatric dentistry. The use of AI algorithms and machine learning techniques has shown promising results in several applications of daily dental pediatric practice, including the following: (1) assisting the diagnostic and recognizing processes of early signs of dental pathologies, (2) enhancing orthodontic diagnosis by automating cephalometric tracing and estimating growth and development, (3) assisting and educating children to develop appropriate behavior for dental hygiene.

CONCLUSION

AI holds significant potential in transforming clinical practice, improving patient outcomes, and elevating the standards of care in pediatric patients. Future directions may involve developing cloud-based platforms for data integration and sharing, leveraging large datasets for improved predictive results, and expanding AI applications for the pediatric population.

Capabilities of Cephalometric Methods to Study X-rays in Three-Dimensional Space (Review)

The aim of the study was a systematic review of modern methods of three-dimensional cephalometric analysis, and the assessment of their efficiency. The scientific papers describing modern diagnostic methods of MFA in dental practice were searched in databases PubMed, Web of Science, eLIBRARY.RU, as well as in a searching system Google Scholar by the following key words: three-dimensional cephalometry, three-dimensional cephalometric analysis, orthodontics, asymmetric deformities, maxillofacial anomalies, 3D cephalometry, CBCT. The literature analysis showed many methods of cephalometric analysis described as three-dimensional to use two-dimensional reformates for measurements. True three-dimensional methods are not applicable for practical purposes due to the fragmentary nature of the studies. There is the disunity in choosing landmarks and supporting planes that makes the diagnosis difficult and costly. The major issue is the lack of uniform standards for tree-dimensional measurements of anatomical structures of the skull, and the data revealed can be compared to them. In this regard, the use of artificial neuron networks and in-depth study technologies to process three-dimensional images and determining standard indicators appear to be promising.

Automated artificial intelligence-based three-dimensional comparison of orthodontic treatment outcomes with and without piezocision surgery

OBJECTIVE(S)

This study aims to evaluate the influence of the piezocision surgery in the orthodontic biomechanics, as well as in the magnitude and direction of tooth movement in the mandibular arch using novel artificial intelligence (AI)-automated tools.

MATERIALS AND METHODS

Nineteen patients, who had piezocision performed in the lower arch at the beginning of treatment with the goal of accelerating tooth movement, were compared to 19 patients who did not receive piezocision. Cone beam computed tomography (CBCT) and intraoral scans (IOS) were acquired before and after orthodontic treatment. AI-automated dental tools were used to segment and locate landmarks in dental crowns from IOS and root canals from CBCT scans to quantify 3D tooth movement. Differences in mesial-distal, buccolingual, intrusion and extrusion linear movements, as well as tooth long axis angulation and rotation were compared.

RESULTS

The treatment time for the control and experimental groups were 13.2 ± 5.06 and 13 ± 5.52 months respectively (P = .176). Overall, anterior and posterior tooth movement presented similar 3D linear and angular changes in the groups. The piezocision group demonstrated greater (P = .01) mesial long axis angulation of lower right first premolar (4.4 ± 6°) compared with control group (0.02 ± 4.9°), while the mesial rotation was significantly smaller (P = .008) in the experimental group (0.5 ± 7.8°) than in the control (8.5 ± 9.8°) considering the same tooth.

CONCLUSION

The open source-automated dental tools facilitated the clinicians' assessment of piezocision treatment outcomes. The piezocision surgery prior to the orthodontic treatment did not decrease the treatment time and did not influence in the orthodontic biomechanics, leading to similar tooth movements compared to conventional treatment.

Enhancing skeletal stability and Class III correction through active orthodontist engagement in virtual surgical planning: A voxel-based 3-dimensional analysis

INTRODUCTION

Skeletal stability after bimaxillary surgical correction of Class III malocclusion was investigated through a qualitative and quantitative analysis of the maxilla and the distal and proximal mandibular segments using a 3-dimensional voxel-based superimposition among virtual surgical predictions performed by the orthodontist in close communication with the maxillofacial surgeon and 12-18 months postoperative outcomes.

METHODS

A comprehensive secondary data analysis was conducted on deidentified preoperative (1 month before surgery [T1]) and 12-18 months postoperative (midterm [T2]) cone-beam computed tomography scans, along with virtual surgical planning (VSP) data obtained by Dolphin Imaging software. The sample for the study consisted of 17 patients (mean age, 24.8 ± 3.5 years). Using 3D Slicer software, automated tools based on deep-learning approaches were used for cone-beam computed tomography orientation, registration, bone segmentation, and landmark identification. Colormaps were generated for qualitative analysis, whereas linear and angular differences between the planned (T1-VSP) and observed (T1-T2) outcomes were calculated for quantitative assessments. Statistical analysis was conducted with a significance level of α = 0.05.

RESULTS

The midterm surgical outcomes revealed a slight but significantly less maxillary advancement compared with the planned position (mean difference, 1.84 ± 1.50 mm; P = 0.004). The repositioning of the mandibular distal segment was stable, with insignificant differences in linear (T1-VSP, 1.01 ± 3.66 mm; T1-T2, 0.32 ± 4.17 mm) and angular (T1-VSP, 1.53° ± 1.60°; T1-T2, 1.54° ± 1.50°) displacements (P >0.05). The proximal segments exhibited lateral displacement within 1.5° for both the mandibular right and left ramus at T1-VSP and T1-T2 (P >0.05).

CONCLUSIONS

The analysis of fully digital planned and surgically repositioned maxilla and mandible revealed excellent precision. In the midterm surgical outcomes of maxillary advancement, a minor deviation from the planned anterior movement was observed.

3D analysis of condylar and mandibular remodeling one year after intra-oral ramus vertical lengthening osteotomy

OBJECTIVES

Among the existing techniques for the correction of mandibular posterior vertical insufficiency (PVI), the intra-oral ramus vertical lengthening osteotomy (IORVLO) can be proposed as it allows simultaneous correction of mandibular height and retrusion. This study assessed the 3D morpho-anatomical changes of the ramus-condyle unit and occlusal stability after IORVLO.

MATERIALS AND METHODS

This retrospective analysis compared immediate and 1-year post-operative 3D CBCT reconstructions. The analysis focused on the condylar height (primary endpoint) and on the changes in condylar (condylar diameter, condylar axis angle) and mandibular (ramus height, Frankfort-mandibular plane angle, gonion position, intergonial distance, angular remodeling) parameters. Additionally, this analysis investigated the maxillary markers and occlusal stability.

RESULTS

On the 38 condyles studied in 21 included patients (mean age 23.7 ± 3.9 years), a condylar height (CH) loss of 0.66 mm (p < 0,03) was observed, with no correlation with the degree of ramus lengthening (mean 13.3 ± 0.76 mm). Only one patient presented an occlusal relapse of Class II, but a 3.4 mm (28%) condylar diameter loss and a 33% condylar volume reduction with loss of 1 mm and 3.4 mm in CH and condyle diameter, respectively. A mean 3.56 mm (p < 0.001) decrease in ramus height was noted, mainly due to bone resorption in the mandibular angles.

CONCLUSION

This study confirms the overall stability obtained with IORVLO for the correction of PVI.

CLINICAL RELEVANCE

This study aims to precise indication of IORVLO, and to validate the clinical and anatomical stability of results.

Automated Orientation and Registration of Cone-Beam Computed Tomography Scans

Automated clinical decision support systems rely on accurate analysis of three-dimensional (3D) medical and dental images to assist clinicians in diagnosis, treatment planning, intervention, and assessment of growth and treatment effects. However, analyzing longitudinal 3D images requires standardized orientation and registration, which can be laborious and error-prone tasks dependent on structures of reference for registration. This paper proposes two novel tools to automatically perform the orientation and registration of 3D Cone-Beam Computed Tomography (CBCT) scans with high accuracy (<3° and <2mm of angular and linear errors when compared to expert clinicians). These tools have undergone rigorous testing, and are currently being evaluated by clinicians who utilize the 3D Slicer open-source platform. Our work aims to reduce the sources of error in the 3D medical image analysis workflow by automating these operations. These methods combine conventional image processing approaches and Artificial Intelligence (AI) based models trained and tested on de-identified CBCT volumetric images. Our results showed robust performance for standardized and reproducible image orientation and registration that provide a more complete understanding of individual patient facial growth and response to orthopedic treatment in less than 5 min.