A machine learning approach to determine the prognosis of patients with Class III malocclusion

Artificial Intelligence Used for Diagnosis in Facial Deformities: A Systematic Review

AI is included in a lot of different systems. In facial surgery, there are some AI-based software programs oriented to diagnosis in facial surgery. This study aims to evaluate the capacity and training of models for diagnosis of dentofacial deformities in class II and class III patients using artificial intelligence and the potential use for indicating orthognathic surgery. The search strategy is from 1943 to April 2024 in PubMed, Embase, Scopus, Lilacs, and Web of Science. Studies that used imaging to assess anatomical structures, airway volume, and craniofacial positions using the AI algorithm in the human population were included. The methodological quality of the studies was assessed using the Effective Public Health Practice Project instrument. The systematic search identified 697 articles. Eight studies were obtained for descriptive analysis after exclusion according to our inclusion and exclusion criteria. All studies were retrospective in design. A total of 5552 subjects with an age range between 14.7 and 56 years were obtained; 2474 (44.56%) subjects were male, and 3078 (55.43%) were female. Six studies were analyzed using 2D imaging and obtained highly accurate results in diagnosing skeletal features and determining the need for orthognathic surgery, and two studies used 3D imaging for measurement and diagnosis. Limitations of the studies such as age, diagnosis in facial deformity, and the included variables were observed. Concerning the overall analysis bias, six studies were at moderate risk due to weak study designs, while two were at high risk of bias. We can conclude that, with the few articles included, using AI-based software allows for some craniometric recognition and measurements to determine the diagnosis of facial deformities using mainly 2D analysis. However, it is necessary to perform studies based on three-dimensional images, increase the sample size, and train models in different populations to ensure accuracy of AI applications in this field. After that, the models can be trained for dentofacial diagnosis.

Radiographic Imaging for the Diagnosis and Treatment of Patients with Skeletal Class III Malocclusion

Skeletal Class III malocclusion is one type of dentofacial deformity that significantly affects patients' facial aesthetics and oral health. The orthodontic treatment of skeletal Class III malocclusion presents challenges due to uncertainties surrounding mandibular growth patterns and treatment outcomes. In recent years, disease-specific radiographic features have garnered interest from researchers in various fields including orthodontics, for their exceptional performance in enhancing diagnostic precision and treatment effect predictability. The aim of this narrative review is to provide an overview of the valuable radiographic features in the diagnosis and management of skeletal Class III malocclusion. Based on the existing literature, a series of analyses on lateral cephalograms have been concluded to identify the significant variables related to facial type classification, growth prediction, and decision-making for tooth extractions and orthognathic surgery in patients with skeletal Class III malocclusion. Furthermore, we summarize the parameters regarding the inter-maxillary relationship, as well as different anatomical structures including the maxilla, mandible, craniofacial base, and soft tissues from conventional and machine learning statistical models. Several distinct radiographic features for Class III malocclusion have also been preliminarily observed using cone beam computed tomography (CBCT) and magnetic resonance imaging (MRI).

A family-centered orthodontic screening approach using a machine learning-based mobile application

Background/purpose

Skeletal orthodontic deformities can have functional and aesthetic consequences, making early detection critical. This study aimed to address the issue of parents bringing their children for routine orthodontic checkups after the ideal treatment age has passed. To address this, we developed a mobile application that uses machine-learning to make a preliminary diagnosis of skeletal malocclusion using just one photograph.

Materials and methods

A retrospective study was conducted on 524 pre-pubertal children, aged between 5 and 12 years, to evaluate the accuracy of the machine learning based mobile application. The application detects multiple points in photographs taken from the mobile camera and generates a signal indicating the diagnosis of skeletal malocclusion.

Results

The final accuracy of the Class III vs not Class III model deployed to the mobile application was above 81%, indicating its ability to accurately identify skeletal malocclusion. On a separate validation dataset of 145 patients diagnosed by 5 different clinicians, the accuracy of Class II vs Class I model was 69%; And pg 4, ln 61: as Class II vs Class I with 69% accuracy.

Conclusion

The application provides parents with important information about the orthodontic problem, age of treatment, and various treatment options. This enables parents to seek further advice from an orthodontist at an earlier stage and make informed decisions. However, the diagnosis should still be confirmed by an orthodontist. This approach has the potential to improve access to orthodontic care, especially in underserved communities.

[The use of fixed expanders with orthodontic miniscrews support in children with mesial occlusion]

OBJECTIVE

Prevention of the development of pronounced skeletal abnormalities in patients with mesial occlusion.

MATERIALS AND METHODS

Biometric analysis of control and diagnostic models of dentition was performed in 60 patients with dental anomalies before and after treatment in 3 mutually perpendicular planes to identify violations in the formation of dental arches by sagittal and transversal dimensions, and alveolar processes - by vertical dimensions (methods of A. Pont, G. Korkhaus). Measurements of 23 parameters of TRG and sections of CBCT were carried out using the modified Nad-Ars technique with analysis of skeletal parameters before and after treatment. Treatment was carried out using dilators for the upper jaw in combination with a facial mask and further dynamic observation using active retention devices.

RESULTS

The results of treatment showed an increase in the length of the anterior segment of the upper dental arch by 2.8±0.55 mm (p<0.05 mm); expansion in the area of temporary molars by 2.85±0.65 mm (p<0.05); in the area of permanent molars by 2.75±0.55 mm (p<0.05); in the area of the apical basis of HF by 3.82±0.45 mm (p<0.05). The length of the lower dental arch in the anterior segment has not changed. Analysis of TRG parameters showed a significant increase in the values of

CONCLUSION

Early orthodontic treatment made it possible to create favorable conditions and prepare the patient's occlusion for a change of teeth, to form dental alveolar arches, to reduce the degree of complexity of therapeutic measures that may be needed after the completion of skeletal growth.

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Key Words

• cone-beam computed tomography
• early orthodontic treatment
• fixed expander
• mesial occlusion

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MESH Headings

• Humans
• Child
• Male
• Female
• Dental Arch/diagnostic imaging
• Malocclusion/therapy
• Palatal Expansion Technique/instrumentation
• Bone Screws
• Dental Occlusion
• Orthodontic Anchorage Procedures/instrumentation
• Orthodontic Anchorage Procedures/methods
• Adolescent
• Orthodontic Appliances, Fixed

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Affiliation(s)

O I Arsenina Central Research Institute of Dentistry and Maxillofacial Surgery, Moscow, Russia Russian Medical Academy of Continuous Professional Education, Moscow, Russia
S I Abakarov Russian Medical Academy of Continuous Professional Education, Moscow, Russia
N V Popova Central Research Institute of Dentistry and Maxillofacial Surgery, Moscow, Russia Burnasyan Federal Medical Biophysical Center of Federal Medical Biological Agency, Moscow, Russia
P I Makhortova Central Research Institute of Dentistry and Maxillofacial Surgery, Moscow, Russia Burnasyan Federal Medical Biophysical Center of Federal Medical Biological Agency, Moscow, Russia
A V Popova Central Research Institute of Dentistry and Maxillofacial Surgery, Moscow, Russia
A V Khachieva Central Research Institute of Dentistry and Maxillofacial Surgery, Moscow, Russia

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Orthodontic craniofacial pattern diagnosis: cephalometric geometry and machine learning

Efficient and reliable diagnosis of craniofacial patterns is critical to orthodontic treatment. Although machine learning (ML) is time-saving and high-precision, prior knowledge should validate its reliability. This study proposed a craniofacial ML diagnostic workflow base on a cephalometric geometric model through clinical verification. A cephalometric geometric model was established to determine the landmark location by analyzing 408 X-ray lateral cephalograms. Through geometric information and feature engineering, nine supervised ML algorithms were conducted for sagittal and vertical skeleton patterns. After dimension reduction, plane decision boundary and landmark contribution contours were depicted to demonstrate the diagnostic consistency and the consistency with clinical norms. As a result, multi-layer perceptron achieved 97.56% accuracy for sagittal, while linear support vector machine reached 90.24% for the vertical. Sagittal diagnoses showed average superiority (91.60 ± 5.43)% over the vertical (82.25 ± 6.37)%, where discriminative algorithms exhibited more steady performance (93.20 ± 3.29)% than the generative (85.98 ± 9.48)%. Further, the Kruskal-Wallis H test was carried out to explore statistical differences in diagnoses. Though sagittal patterns had no statistical difference in diagnostic accuracy, the vertical showed significance. All aspects of the tests indicated that the proposed craniofacial ML workflow was highly consistent with clinical norms and could supplement practical diagnosis.