Automatic virtual reconstruction of maxillofacial bone defects assisted by ICP (iterative closest point) algorithm and normal people database

Developing a three-dimensional statistical shape model of normal dentition using an automated algorithm and normal samples

OBJECTIVES

The statistical shape model (SSM) is a model of geometric properties of a set of shapes based on statistical shape analysis. The SSM develops an average model of several objects using an automated algorithm that excludes the operator's subjectivity. The aim of this study was to develop a three-dimensional (3D) SSM of normal dentition to provide virtual templates for efficient treatment.

MATERIALS AND METHODS

Dental casts were obtained from participants with normal dentition. After acquiring the 3D models, the SSMs of the individual teeth and whole dental arch were generated by an iterative closest point (ICP)-based rigid registration and point correspondences, respectively. Then, the individual tooth SSM was aligned to the whole dental arch SSM using ICP-based registration to generate an average model of normal dentition.

RESULTS

The generated 3D SSM showed specific morphological features of normal dentition similar to those previously reported. Moreover, on measuring the arch dimensions, all values in this study were similar to those previously reported using normal dentition.

CONCLUSIONS

The 3D SSM of normal dentition may increase the diagnostic efficiency of orthodontic treatments by providing a visual objective. It can be also used as a 3D template in various fields of dentistry.

CLINICAL RELEVANCE

Our SSM of normal dentition provides both quantitative and qualitative information on the 3D morphology of teeth and dental arches, which may provide valuable information on 3D virtual-setup, bracket fabrication, and aligner treatment.

Statistical and individual characteristics-based reconstruction for craniomaxillofacial surgery

PURPOSE

In craniomaxillofacial (CMF) surgery planning, a preoperative reconstruction of the CMF reference model is crucial for surgical restoration, especially the reconstruction of bilateral defects. Current reconstruction algorithms mainly generate reference models from the image analysis aspect, however, clinical indicators of the CMF reference model mostly consider the distribution of anatomical landmarks. Generating a reference model with optimal clinical evaluation helps promote the feasibility of an algorithm.

METHODS

We first build a dataset with 100 normal skull models and then calculate a statistical shape model (SSM) and the distribution of normal cephalometric values, which indicate the statistical features of a population. To further generate personalized reference models, we apply non-rigid registration to align the SSM with the defect skull model. An evaluation standard to select the optimal reference model considers both global performance and anatomical evaluation. Moreover, we develop a landmark detection network to improve the automatic level of the algorithm.

RESULTS

The proposed method performs better than methods including Iterative Closest Point and SSM. From a global evaluation aspect, the results show that the RMSE between the reference model and the ground truth is [Formula: see text] mm, the percentage of vertices with error below 2 mm is [Formula: see text]% and the average faces distance is [Formula: see text] mm (better than the state-of-the-art method). From the anatomical evaluation aspect, the target registration error between the landmark pairs is [Formula: see text] mm. In addition, the clinical application confirms that the reference model can meet clinical requirements.

CONCLUSION

To the best of our knowledge, we propose the first CMF reconstruction method considering the global performance of reconstruction and anatomically local evaluation from clinical experience. Simulated experiments and clinical cases prove the general applicability and strength of the method.

Application of machine learning in oral and maxillofacial surgery

Oral and maxillofacial anatomy is extremely complex, and medical imaging is critical in the diagnosis and treatment of soft and bone tissue lesions. Hence, there exists accumulating imaging data without being properly utilized over the last decades. As a result, problems are emerging regarding how to integrate and interpret a large amount of medical data and alleviate clinicians’ workload. Recently, artificial intelligence has been developing rapidly to analyze complex medical data, and machine learning is one of the specific methods of achieving this goal, which is based on a set of algorithms and previous results. Machine learning has been considered useful in assisting early diagnosis, treatment planning, and prognostic estimation through extracting key features and building mathematical models by computers. Over the past decade, machine learning techniques have been applied to the field of oral and maxillofacial surgery and increasingly achieved expert-level performance. Thus, we hold a positive attitude towards developing machine learning for reducing the number of medical errors, improving the quality of patient care, and optimizing clinical decision-making in oral and maxillofacial surgery. In this review, we explore the clinical application of machine learning in maxillofacial cysts and tumors, maxillofacial defect reconstruction, orthognathic surgery, and dental implant and discuss its current problems and solutions.