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Ravelo V, Acero J, Fuentes-Zambrano J, García Guevara H, Olate S. Artificial Intelligence Used for Diagnosis in Facial Deformities: A Systematic Review. J Pers Med 2024; 14:647. [PMID: 38929868 PMCID: PMC11204491 DOI: 10.3390/jpm14060647] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2024] [Revised: 05/26/2024] [Accepted: 06/02/2024] [Indexed: 06/28/2024] Open
Abstract
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.
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Affiliation(s)
- Victor Ravelo
- Grupo de Investigación de Pregrado en Odontología (GIPO), Universidad Autónoma de Chile, Temuco 4780000, Chile;
- PhD Program in Morphological Science, Universidad de La Frontera, Temuco 4780000, Chile
| | - Julio Acero
- Department of Oral and Maxillofacial Surgery, Ramon y Cajal University Hospital, Ramon y Cajal Research Institute (IRYCIS), University of Alcala, 28034 Madrid, Spain;
| | | | - Henry García Guevara
- Department of Oral Surgery, La Floresta Medical Institute, Caracas 1060, Venezuela;
- Division for Oral and Maxillofacial Surgery, Hospital Ortopedico Infantil, Caracas 1060, Venezuela
| | - Sergio Olate
- Center for Research in Morphology and Surgery (CEMyQ), Universidad de La Frontera, Temuco 4780000, Chile
- Division of Oral, Facial and Maxillofacial Surgery, Universidad de La Frontera, Temuco 4780000, Chile
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Li Z, Hung KF, Ai QYH, Gu M, Su YX, Shan Z. Radiographic Imaging for the Diagnosis and Treatment of Patients with Skeletal Class III Malocclusion. Diagnostics (Basel) 2024; 14:544. [PMID: 38473016 DOI: 10.3390/diagnostics14050544] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2024] [Revised: 02/28/2024] [Accepted: 03/01/2024] [Indexed: 03/14/2024] Open
Abstract
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).
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Affiliation(s)
- Zhuoying Li
- Division of Paediatric Dentistry and Orthodontics, Faculty of Dentistry, The University of Hong Kong, Hong Kong SAR, China
| | - Kuo Feng Hung
- Applied Oral Sciences & Community Dental Care, Faculty of Dentistry, The University of Hong Kong, Hong Kong SAR, China
| | - Qi Yong H Ai
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong SAR, China
| | - Min Gu
- Division of Paediatric Dentistry and Orthodontics, Faculty of Dentistry, The University of Hong Kong, Hong Kong SAR, China
| | - Yu-Xiong Su
- Division of Oral and Maxillofacial Surgery, Faculty of Dentistry, The University of Hong Kong, Hong Kong SAR, China
| | - Zhiyi Shan
- Division of Paediatric Dentistry and Orthodontics, Faculty of Dentistry, The University of Hong Kong, Hong Kong SAR, China
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Kılıç B, İbrahim AH, Aksoy S, Sakman MC, Demircan GS, Önal-Süzek T. A family-centered orthodontic screening approach using a machine learning-based mobile application. J Dent Sci 2024; 19:186-195. [PMID: 38303845 PMCID: PMC10829551 DOI: 10.1016/j.jds.2023.05.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2023] [Revised: 05/01/2023] [Indexed: 02/03/2024] Open
Abstract
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.
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Affiliation(s)
- Banu Kılıç
- Bezmialem Vakif University, Istanbul, Turkey
| | | | | | - Mehmet Cihan Sakman
- Muğla Sıtkı Koçman University, Muğla, Turkey
- Zurich University of Applied Sciences, Zurich, Switzerland
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Arsenina OI, Abakarov SI, Popova NV, Makhortova PI, Popova AV, Khachieva AV. [The use of fixed expanders with orthodontic miniscrews support in children with mesial occlusion]. STOMATOLOGIIA 2024; 103:61-70. [PMID: 38741537 DOI: 10.17116/stomat202410302161] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/16/2024]
Abstract
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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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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Zhou Y, Mao B, Zhang J, Zhou Y, Li J, Rong Q. Orthodontic craniofacial pattern diagnosis: cephalometric geometry and machine learning. Med Biol Eng Comput 2023; 61:3345-3361. [PMID: 37672141 DOI: 10.1007/s11517-023-02919-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2022] [Accepted: 08/21/2023] [Indexed: 09/07/2023]
Abstract
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.
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Affiliation(s)
- Yuqing Zhou
- Department of Mechanics and Engineering Science, College of Engineering, Peking University, Beijing, 100081, China
| | - Bochun Mao
- Department of Orthodontics, Peking University School of Stomatology & National Center of Stomatology & National Clinical Research Center for Oral Diseases & National Engineering Research Center of Oral Biomaterials and Digital Medical Devices & Beijing Key Laboratory of Digital Stomatology, Beijing, 100081, China
| | - Jiwu Zhang
- Department of Mechanics and Engineering Science, College of Engineering, Peking University, Beijing, 100081, China
| | - Yanheng Zhou
- Department of Orthodontics, Peking University School of Stomatology & National Center of Stomatology & National Clinical Research Center for Oral Diseases & National Engineering Research Center of Oral Biomaterials and Digital Medical Devices & Beijing Key Laboratory of Digital Stomatology, Beijing, 100081, China
| | - Jing Li
- Department of Orthodontics, Peking University School of Stomatology & National Center of Stomatology & National Clinical Research Center for Oral Diseases & National Engineering Research Center of Oral Biomaterials and Digital Medical Devices & Beijing Key Laboratory of Digital Stomatology, Beijing, 100081, China.
| | - Qiguo Rong
- Department of Mechanics and Engineering Science, College of Engineering, Peking University, Beijing, 100081, China.
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