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Lee J, Xu X, Kim D, Deng HH, Kuang T, Lampen N, Fang X, Gateno J, Yan P. Large Language Models Diagnose Facial Deformity. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.07.11.24310274. [PMID: 39040164 PMCID: PMC11261925 DOI: 10.1101/2024.07.11.24310274] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/24/2024]
Abstract
Purpose This study examines the application of Large Language Models (LLMs) in diagnosing jaw deformities, aiming to overcome the limitations of various diagnostic methods by harnessing the advanced capabilities of LLMs for enhanced data interpretation. The goal is to provide tools that simplify complex data analysis and make diagnostic processes more accessible and intuitive for clinical practitioners. Methods An experiment involving patients with jaw deformities was conducted, where cephalometric measurements (SNB Angle, Facial Angle, Mandibular Unit Length) were converted into text for LLM analysis. Multiple LLMs, including LLAMA-2 variants, GPT models, and the Gemini-Pro model, were evaluated against various methods (Threshold-based, Machine Learning Models) using balanced accuracy and F1-score. Results Our research demonstrates that larger LLMs efficiently adapt to diagnostic tasks, showing rapid performance saturation with minimal training examples and reducing ambiguous classification, which highlights their robust in-context learning abilities. The conversion of complex cephalometric measurements into intuitive text formats not only broadens the accessibility of the information but also enhances the interpretability, providing clinicians with clear and actionable insights. Conclusion Integrating LLMs into the diagnosis of jaw deformities marks a significant advancement in making diagnostic processes more accessible and reducing reliance on specialized training. These models serve as valuable auxiliary tools, offering clear, understandable outputs that facilitate easier decision-making for clinicians, particularly those with less experience or in settings with limited access to specialized expertise. Future refinements and adaptations to include more comprehensive and medically specific datasets are expected to enhance the precision and utility of LLMs, potentially transforming the landscape of medical diagnostics.
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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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Semerci ZM, Yardımcı S. Empowering Modern Dentistry: The Impact of Artificial Intelligence on Patient Care and Clinical Decision Making. Diagnostics (Basel) 2024; 14:1260. [PMID: 38928675 PMCID: PMC11202919 DOI: 10.3390/diagnostics14121260] [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: 05/05/2024] [Revised: 06/06/2024] [Accepted: 06/13/2024] [Indexed: 06/28/2024] Open
Abstract
Advancements in artificial intelligence (AI) are poised to catalyze a transformative shift across diverse dental disciplines including endodontics, oral radiology, orthodontics, pediatric dentistry, periodontology, prosthodontics, and restorative dentistry. This narrative review delineates the burgeoning role of AI in enhancing diagnostic precision, streamlining treatment planning, and potentially unveiling innovative therapeutic modalities, thereby elevating patient care standards. Recent analyses corroborate the superiority of AI-assisted methodologies over conventional techniques, affirming their capacity for personalization, accuracy, and efficiency in dental care. Central to these AI applications are convolutional neural networks and deep learning models, which have demonstrated efficacy in diagnosis, prognosis, and therapeutic decision making, in some instances surpassing traditional methods in complex cases. Despite these advancements, the integration of AI into clinical practice is accompanied by challenges, such as data security concerns, the demand for transparency in AI-generated outcomes, and the imperative for ongoing validation to establish the reliability and applicability of AI tools. This review underscores the prospective benefits of AI in dental practice, envisioning AI not as a replacement for dental professionals but as an adjunctive tool that fortifies the dental profession. While AI heralds improvements in diagnostics, treatment planning, and personalized care, ethical and practical considerations must be meticulously navigated to ensure responsible development of AI in dentistry.
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Affiliation(s)
- Zeliha Merve Semerci
- Department of Oral and Maxillofacial Radiology, Faculty of Dentistry, Akdeniz University, Antalya 07070, Turkey
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Surendran A, Daigavane P, Shrivastav S, Kamble R, Sanchla AD, Bharti L, Shinde M. The Future of Orthodontics: Deep Learning Technologies. Cureus 2024; 16:e62045. [PMID: 38989357 PMCID: PMC11234326 DOI: 10.7759/cureus.62045] [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: 03/03/2024] [Accepted: 06/09/2024] [Indexed: 07/12/2024] Open
Abstract
Deep learning has emerged as a revolutionary technical advancement in modern orthodontics, offering novel methods for diagnosis, treatment planning, and outcome prediction. Over the past 25 years, the field of dentistry has widely adopted information technology (IT), resulting in several benefits, including decreased expenses, increased efficiency, decreased need for human expertise, and reduced errors. The transition from preset rules to learning from real-world examples, particularly machine learning (ML) and artificial intelligence (AI), has greatly benefited the organization, analysis, and storage of medical data. Deep learning, a type of AI, enables robots to mimic human neural networks, allowing them to learn and make decisions independently without the need for explicit programming. Its ability to automate cephalometric analysis and enhance diagnosis through 3D imaging has revolutionized orthodontic operations. Deep learning models have the potential to significantly improve treatment outcomes and reduce human errors by accurately identifying anatomical characteristics on radiographs, thereby expediting analytical processes. Additionally, the use of 3D imaging technologies such as cone-beam computed tomography (CBCT) can facilitate precise treatment planning, allowing for comprehensive examinations of craniofacial architecture, tooth movements, and airway dimensions. In today's era of personalized medicine, deep learning's ability to customize treatments for individual patients has propelled the field of orthodontics forward tremendously. However, it is essential to address issues related to data privacy, model interpretability, and ethical considerations before orthodontic practices can use deep learning in an ethical and responsible manner. Modern orthodontics is evolving, thanks to the ability of deep learning to deliver more accurate, effective, and personalized orthodontic treatments, improving patient care as technology develops.
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Affiliation(s)
- Aathira Surendran
- Department of Orthodontics & Dentofacial Orthopaedics, Sharad Pawar Dental College & Hospital, Wardha, IND
| | - Pallavi Daigavane
- Department of Orthodontics & Dentofacial Orthopaedics, Sharad Pawar Dental College & Hospital, Wardha, IND
| | - Sunita Shrivastav
- Department of Orthodontics & Dentofacial Orthopaedics, Sharad Pawar Dental College & Hospital, Wardha, IND
| | - Ranjit Kamble
- Department of Orthodontics & Dentofacial Orthopaedics, Sharad Pawar Dental College & Hospital, Wardha, IND
| | - Abhishek D Sanchla
- Department of Orthodontics & Dentofacial Orthopaedics, Sharad Pawar Dental College & Hospital, Wardha, IND
| | - Lovely Bharti
- Department of Orthodontics & Dentofacial Orthopaedics, Sharad Pawar Dental College & Hospital, Wardha, IND
| | - Mrudula Shinde
- Department of Orthodontics & Dentofacial Orthopaedics, Sharad Pawar Dental College & Hospital, Wardha, IND
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Salazar D, Rossouw PE, Javed F, Michelogiannakis D. Artificial intelligence for treatment planning and soft tissue outcome prediction of orthognathic treatment: A systematic review. J Orthod 2024; 51:107-119. [PMID: 37772513 DOI: 10.1177/14653125231203743] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/30/2023]
Abstract
BACKGROUND The accuracy of artificial intelligence (AI) in treatment planning and outcome prediction in orthognathic treatment (OGT) has not been systematically reviewed. OBJECTIVES To determine the accuracy of AI in treatment planning and soft tissue outcome prediction in OGT. DESIGN Systematic review. DATA SOURCES Unrestricted search of indexed databases and reference lists of included studies. DATA SELECTION Clinical studies that addressed the focused question 'Is AI useful for treatment planning and soft tissue outcome prediction in OGT?' were included. DATA EXTRACTION Study screening, selection and data extraction were performed independently by two authors. The risk of bias (RoB) was assessed using the Cochrane Collaboration's RoB and ROBINS-I tools for randomised and non-randomised clinical studies, respectively. DATA SYNTHESIS Eight clinical studies (seven retrospective cohort studies and one randomised controlled study) were included. Four studies assessed the role of AI for treatment decision making; and four studies assessed the accuracy of AI in soft tissue outcome prediction after OGT. In four studies, the level of agreement between AI and non-AI decision making was found to be clinically acceptable (at least 90%). In four studies, it was shown that AI can be used for soft tissue outcome prediction after OGT; however, predictions were not clinically acceptable for the lip and chin areas. All studies had a low to moderate RoB. LIMITATIONS Due to high methodological inconsistencies among the included studies, it was not possible to conduct a meta-analysis and reporting biases assessment. CONCLUSION AI can be a useful aid to traditional treatment planning by facilitating clinical treatment decision making and providing a visualisation tool for soft tissue outcome prediction in OGT. REGISTRATION PROSPERO CRD42022366864.
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Affiliation(s)
- Daisy Salazar
- Department of Orthodontics and Dentofacial Orthopedics, Eastman Institute for Oral Health, University of Rochester, Rochester, NY, USA
| | - Paul Emile Rossouw
- Department of Orthodontics and Dentofacial Orthopedics, Eastman Institute for Oral Health, University of Rochester, Rochester, NY, USA
| | - Fawad Javed
- Department of Orthodontics and Dentofacial Orthopedics, Eastman Institute for Oral Health, University of Rochester, Rochester, NY, USA
| | - Dimitrios Michelogiannakis
- Department of Orthodontics and Dentofacial Orthopedics, Eastman Institute for Oral Health, University of Rochester, Rochester, NY, USA
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Chang JS, Ma CY, Ko EWC. Prediction of surgery-first approach orthognathic surgery using deep learning models. Int J Oral Maxillofac Surg 2024:S0901-5027(24)00148-6. [PMID: 38821731 DOI: 10.1016/j.ijom.2024.05.003] [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: 11/26/2023] [Revised: 04/24/2024] [Accepted: 05/08/2024] [Indexed: 06/02/2024]
Abstract
The surgery-first approach (SFA) orthognathic surgery can be beneficial due to reduced overall treatment time and earlier profile improvement. The objective of this study was to utilize deep learning to predict the treatment modality of SFA or the orthodontics-first approach (OFA) in orthognathic surgery patients and assess its clinical accuracy. A supervised deep learning model using three convolutional neural networks (CNNs) was trained based on lateral cephalograms and occlusal views of 3D dental model scans from 228 skeletal Class III malocclusion patients (114 treated by SFA and 114 by OFA). An ablation study of five groups (lateral cephalogram only, mandible image only, maxilla image only, maxilla and mandible images, and all data combined) was conducted to assess the influence of each input type. The results showed the average validation accuracy, precision, recall, F1 score, and AUROC for the five folds were 0.978, 0.980, 0.980, 0.980, and 0.998 ; the average testing results for the five folds were 0.906, 0.986, 0.828, 0.892, and 0.952. The lateral cephalogram only group had the least accuracy, while the maxilla image only group had the best accuracy. Deep learning provides a novel method for an accelerated workflow, automated assisted decision-making, and personalized treatment planning.
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Affiliation(s)
- J-S Chang
- Graduate Institute of Dental and Craniofacial Science, Chang Gung University, Taoyuan, Taiwan; Department of Craniofacial Orthodontics, Chang Gung Memorial Hospital, Taipei, Taiwan
| | - C-Y Ma
- Department of Artificial Intelligence, Chang Gung University, Taoyuan, Taiwan; Artificial Intelligence Research Center, Chang Gung University, Taoyuan, Taiwan; Division of Rheumatology, Allergy and Immunology, Chang Gung Memorial Hospital, Taoyuan, Taiwan
| | - E W-C Ko
- Graduate Institute of Dental and Craniofacial Science, Chang Gung University, Taoyuan, Taiwan; Department of Craniofacial Orthodontics, Chang Gung Memorial Hospital, Taipei, Taiwan; Craniofacial Research Center, Chang Gung Memorial Hospital, Linkou, Taiwan.
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Zhou G, Zhang Y, Zhao J, Tian L, Jia G, Ma Q. A rapid identification method for soft tissue markers of dentofacial deformities based on heatmap regression. BDJ Open 2024; 10:14. [PMID: 38429260 PMCID: PMC10907697 DOI: 10.1038/s41405-024-00189-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2023] [Revised: 12/19/2023] [Accepted: 01/08/2024] [Indexed: 03/03/2024] Open
Abstract
OBJECTIVE The purpose of this study was to construct a facial deformity dataset and a network model based on heatmap regression for the recognition of facial soft tissue landmarks to provide a basis for clinicians to perform cephalometric analysis of soft tissue. MATERIALS AND METHODS A 34-point face marker detection model, the Back High-Resolution Network (BHR-Net), was constructed based on the heatmap regression algorithm, and a custom dataset of 1780 facial detection images for orthognathic surgery was collected. The mean normalized error (MNE) and 10% failure rate (FR10%) were used to evaluate the performance of BHR-Net, and a test set of 50 patients was used to verify the accuracy of the landmarks and their measurement indicators. The test results were subsequently validated in 30 patients. RESULTS Both the MNE and FR10% of BHR-Net were optimal compared with other models. In the test set (50 patients), the accuracy of the markers excluding the nose root was 86%, and the accuracy of the remaining markers reached 94%. In the model validation (30 patients), using the markers detected by BHR-Net, the diagnostic accuracy of doctors was 100% for Class II and III deformities, 100% for the oral angle plane, and 70% for maxillofacial asymmetric deformities. CONCLUSIONS BHR-Net, a network model based on heatmap regression, can be used to effectively identify landmarks in maxillofacial multipose images, providing a reliable way for clinicians to perform cephalometric measurements of soft tissue objectively and quickly.
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Affiliation(s)
- Guilong Zhou
- State Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, National Clinical Research Centre for Oral Diseases, Shaanxi Clinical Research Centre for Oral Diseases, Department of Orthognathic Trauma Surgery, The Third Affiliated Hospital of Air Force Medical University, 710032, Xi'an, China
- Hospital 987, Joint Logistics Support Force, 721000, Baoji, China
| | - Yu Zhang
- School of Computer Science and Technology, Xidian University, 710071, Xi'an, China
| | - Jinlong Zhao
- State Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, National Clinical Research Centre for Oral Diseases, Shaanxi Clinical Research Centre for Oral Diseases, Department of Orthognathic Trauma Surgery, The Third Affiliated Hospital of Air Force Medical University, 710032, Xi'an, China
| | - Lei Tian
- State Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, National Clinical Research Centre for Oral Diseases, Shaanxi Clinical Research Centre for Oral Diseases, Department of Orthognathic Trauma Surgery, The Third Affiliated Hospital of Air Force Medical University, 710032, Xi'an, China.
- Oral Biomechanics Basic and Clinical Research Innovation Team, 710032, Xi'an, China.
| | - Guang Jia
- School of Computer Science and Technology, Xidian University, 710071, Xi'an, China.
| | - Qin Ma
- State Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, National Clinical Research Centre for Oral Diseases, Shaanxi Clinical Research Centre for Oral Diseases, Department of Orthognathic Trauma Surgery, The Third Affiliated Hospital of Air Force Medical University, 710032, Xi'an, China.
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Du W, Bi W, Liu Y, Zhu Z, Tai Y, Luo E. Machine learning-based decision support system for orthognathic diagnosis and treatment planning. BMC Oral Health 2024; 24:286. [PMID: 38419015 PMCID: PMC10902963 DOI: 10.1186/s12903-024-04063-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2023] [Accepted: 02/22/2024] [Indexed: 03/02/2024] Open
Abstract
BACKGROUND Dento-maxillofacial deformities are common problems. Orthodontic-orthognathic surgery is the primary treatment but accurate diagnosis and careful surgical planning are essential for optimum outcomes. This study aimed to establish and verify a machine learning-based decision support system for treatment of dento-maxillofacial malformations. METHODS Patients (n = 574) with dento-maxillofacial deformities undergoing spiral CT during January 2015 to August 2020 were enrolled to train diagnostic models based on five different machine learning algorithms; the diagnostic performances were compared with expert diagnoses. Accuracy, sensitivity, specificity, and area under the curve (AUC) were calculated. The adaptive artificial bee colony algorithm was employed to formulate the orthognathic surgical plan, and subsequently evaluated by maxillofacial surgeons in a cohort of 50 patients. The objective evaluation included the difference in bone position between the artificial intelligence (AI) generated and actual surgical plans for the patient, along with discrepancies in postoperative cephalometric analysis outcomes. RESULTS The binary relevance extreme gradient boosting model performed best, with diagnostic success rates > 90% for six different kinds of dento-maxillofacial deformities; the exception was maxillary overdevelopment (89.27%). AUC was > 0.88 for all diagnostic types. Median score for the surgical plans was 9, and was improved after human-computer interaction. There was no statistically significant difference between the actual and AI- groups. CONCLUSIONS Machine learning algorithms are effective for diagnosis and surgical planning of dento-maxillofacial deformities and help improve diagnostic efficiency, especially in lower medical centers.
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Affiliation(s)
- Wen Du
- State Key Laboratory of Oral Diseases & National Center for Stomatology & National Clinical Research Center for Oral Diseases, West China Hospital of Stomatology, Sichuan University, Chengdu, 610041, Sichuan, China
- Department of Oral and Maxillofacial Surgery, Peking University School and Hospital of Stomatology & National Clinical Research Center for Oral Diseases & National Engineering Laboratory for Digital and Material Technology of Stomatology & Beijing Key Laboratory of Digital Stomatology, Beijing, China
| | - Wenjun Bi
- School of Electric Power Engineering, Nanjing Institute of Technology, Nanjing, China
| | - Yao Liu
- State Key Laboratory of Oral Diseases & National Center for Stomatology & National Clinical Research Center for Oral Diseases, West China Hospital of Stomatology, Sichuan University, Chengdu, 610041, Sichuan, China
| | - Zhaokun Zhu
- State Key Laboratory of Oral Diseases & National Center for Stomatology & National Clinical Research Center for Oral Diseases, West China Hospital of Stomatology, Sichuan University, Chengdu, 610041, Sichuan, China
| | - Yue Tai
- State Key Laboratory of Oral Diseases & National Center for Stomatology & National Clinical Research Center for Oral Diseases, West China Hospital of Stomatology, Sichuan University, Chengdu, 610041, Sichuan, China
| | - En Luo
- State Key Laboratory of Oral Diseases & National Center for Stomatology & National Clinical Research Center for Oral Diseases, West China Hospital of Stomatology, Sichuan University, Chengdu, 610041, Sichuan, China.
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Takeshita WM, Silva TP, de Souza LLT, Tenorio JM. State of the art and prospects for artificial intelligence in orthognathic surgery: A systematic review with meta-analysis. JOURNAL OF STOMATOLOGY, ORAL AND MAXILLOFACIAL SURGERY 2024; 125:101787. [PMID: 38302057 DOI: 10.1016/j.jormas.2024.101787] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/28/2023] [Revised: 01/19/2024] [Accepted: 01/25/2024] [Indexed: 02/03/2024]
Abstract
OBJECTIVE To present a systematic review of the state of the art regarding clinical applications, main features, and outcomes of artificial intelligence (AI) in orthognathic surgery. METHODS The PICOS strategy was performed on a systematic review (SR) to answer the following question: "What are the state of the art, characteristics and outcomes of applications with artificial intelligence for orthognathic surgery?" After registering in PROSPERO (CRD42021270789) a systematic search was performed in the databases: PubMed (including MedLine), Scopus, Embase, LILACS, MEDLINE EBSCOHOST and Cochrane Library. 195 studies were selected, after screening titles and abstracts, of which thirteen manuscripts were included in the qualitative analysis and six in the quantitative analysis. The treatment effects were plotted in a Forest-plot. JBI questionnaire for observational studies was used to asses the risk of bias. The quality of the SR evidence was assessed using the GRADE tool. RESULTS AI studies on 2D cephalometry for orthognathic surgery, the Tau2 = 0.00, Chi2 = 3.78, p = 1.00 and I² of 0 %, indicating low heterogeneity, AI did not differ statistically from control (p = 0.79). AI studies in the diagnosis of the decision of whether or not to perform orthognathic surgery showed heterogeneity, and therefore meta-analysis was not peformed. CONCLUSION The outcome of AI is similar to the control group, with a low degree of bias, highlighting its potential for use in various applications.
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Affiliation(s)
- Wilton Mitsunari Takeshita
- Department of Diagnosis and Surgery, São Paulo State University (Unesp), School of Dentistry, Araçatuba, 16015-050 Araçatuba, São Paulo, Brazil
| | - Thaísa Pinheiro Silva
- Department of Oral Diagnosis, Division of Oral Radiology, Piracicaba Dental School, University of Campinas (UNICAMP), 13414-903 Piracicaba, Sao Paulo, Brazil.
| | | | - Josceli Maria Tenorio
- Department of Information technology and health, Federal Institute of São Paulo, 01109-010 São Paulo, São Paulo, Brazil
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Kazimierczak N, Kazimierczak W, Serafin Z, Nowicki P, Nożewski J, Janiszewska-Olszowska J. AI in Orthodontics: Revolutionizing Diagnostics and Treatment Planning-A Comprehensive Review. J Clin Med 2024; 13:344. [PMID: 38256478 PMCID: PMC10816993 DOI: 10.3390/jcm13020344] [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: 11/19/2023] [Revised: 12/29/2023] [Accepted: 01/05/2024] [Indexed: 01/24/2024] Open
Abstract
The advent of artificial intelligence (AI) in medicine has transformed various medical specialties, including orthodontics. AI has shown promising results in enhancing the accuracy of diagnoses, treatment planning, and predicting treatment outcomes. Its usage in orthodontic practices worldwide has increased with the availability of various AI applications and tools. This review explores the principles of AI, its applications in orthodontics, and its implementation in clinical practice. A comprehensive literature review was conducted, focusing on AI applications in dental diagnostics, cephalometric evaluation, skeletal age determination, temporomandibular joint (TMJ) evaluation, decision making, and patient telemonitoring. Due to study heterogeneity, no meta-analysis was possible. AI has demonstrated high efficacy in all these areas, but variations in performance and the need for manual supervision suggest caution in clinical settings. The complexity and unpredictability of AI algorithms call for cautious implementation and regular manual validation. Continuous AI learning, proper governance, and addressing privacy and ethical concerns are crucial for successful integration into orthodontic practice.
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Affiliation(s)
- Natalia Kazimierczak
- Kazimierczak Private Medical Practice, Dworcowa 13/u6a, 85-009 Bydgoszcz, Poland
| | - Wojciech Kazimierczak
- Kazimierczak Private Medical Practice, Dworcowa 13/u6a, 85-009 Bydgoszcz, Poland
- Department of Radiology and Diagnostic Imaging, Collegium Medicum, Nicolaus Copernicus University in Torun, Jagiellońska 13-15, 85-067 Bydgoszcz, Poland
| | - Zbigniew Serafin
- Department of Radiology and Diagnostic Imaging, Collegium Medicum, Nicolaus Copernicus University in Torun, Jagiellońska 13-15, 85-067 Bydgoszcz, Poland
| | - Paweł Nowicki
- Kazimierczak Private Medical Practice, Dworcowa 13/u6a, 85-009 Bydgoszcz, Poland
| | - Jakub Nożewski
- Department of Emeregncy Medicine, University Hospital No 2 in Bydgoszcz, Ujejskiego 75, 85-168 Bydgoszcz, Poland
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Dipalma G, Inchingolo AD, Inchingolo AM, Piras F, Carpentiere V, Garofoli G, Azzollini D, Campanelli M, Paduanelli G, Palermo A, Inchingolo F. Artificial Intelligence and Its Clinical Applications in Orthodontics: A Systematic Review. Diagnostics (Basel) 2023; 13:3677. [PMID: 38132261 PMCID: PMC10743240 DOI: 10.3390/diagnostics13243677] [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: 11/15/2023] [Revised: 12/05/2023] [Accepted: 12/13/2023] [Indexed: 12/23/2023] Open
Abstract
This review aims to analyze different strategies that make use of artificial intelligence to enhance diagnosis, treatment planning, and monitoring in orthodontics. Orthodontics has seen significant technological advancements with the introduction of digital equipment, including cone beam computed tomography, intraoral scanners, and software coupled to these devices. The use of deep learning in software has sped up image processing processes. Deep learning is an artificial intelligence technology that trains computers to analyze data like the human brain does. Deep learning models are capable of recognizing complex patterns in photos, text, audio, and other data to generate accurate information and predictions. MATERIALS AND METHODS Pubmed, Scopus, and Web of Science were used to discover publications from 1 January 2013 to 18 October 2023 that matched our topic. A comparison of various artificial intelligence applications in orthodontics was generated. RESULTS A final number of 33 studies were included in the review for qualitative analysis. CONCLUSIONS These studies demonstrate the effectiveness of AI in enhancing orthodontic diagnosis, treatment planning, and assessment. A lot of articles emphasize the integration of artificial intelligence into orthodontics and its potential to revolutionize treatment monitoring, evaluation, and patient outcomes.
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Affiliation(s)
- Gianna Dipalma
- Department of Interdisciplinary Medicine, University of Bari “Aldo Moro”, 70124 Bari, Italy; (A.M.I.); (F.P.); (V.C.); (G.G.); (D.A.); (M.C.); (G.P.); (F.I.)
| | - Alessio Danilo Inchingolo
- Department of Interdisciplinary Medicine, University of Bari “Aldo Moro”, 70124 Bari, Italy; (A.M.I.); (F.P.); (V.C.); (G.G.); (D.A.); (M.C.); (G.P.); (F.I.)
| | - Angelo Michele Inchingolo
- Department of Interdisciplinary Medicine, University of Bari “Aldo Moro”, 70124 Bari, Italy; (A.M.I.); (F.P.); (V.C.); (G.G.); (D.A.); (M.C.); (G.P.); (F.I.)
| | - Fabio Piras
- Department of Interdisciplinary Medicine, University of Bari “Aldo Moro”, 70124 Bari, Italy; (A.M.I.); (F.P.); (V.C.); (G.G.); (D.A.); (M.C.); (G.P.); (F.I.)
| | - Vincenzo Carpentiere
- Department of Interdisciplinary Medicine, University of Bari “Aldo Moro”, 70124 Bari, Italy; (A.M.I.); (F.P.); (V.C.); (G.G.); (D.A.); (M.C.); (G.P.); (F.I.)
| | - Grazia Garofoli
- Department of Interdisciplinary Medicine, University of Bari “Aldo Moro”, 70124 Bari, Italy; (A.M.I.); (F.P.); (V.C.); (G.G.); (D.A.); (M.C.); (G.P.); (F.I.)
| | - Daniela Azzollini
- Department of Interdisciplinary Medicine, University of Bari “Aldo Moro”, 70124 Bari, Italy; (A.M.I.); (F.P.); (V.C.); (G.G.); (D.A.); (M.C.); (G.P.); (F.I.)
| | - Merigrazia Campanelli
- Department of Interdisciplinary Medicine, University of Bari “Aldo Moro”, 70124 Bari, Italy; (A.M.I.); (F.P.); (V.C.); (G.G.); (D.A.); (M.C.); (G.P.); (F.I.)
| | - Gregorio Paduanelli
- Department of Interdisciplinary Medicine, University of Bari “Aldo Moro”, 70124 Bari, Italy; (A.M.I.); (F.P.); (V.C.); (G.G.); (D.A.); (M.C.); (G.P.); (F.I.)
| | - Andrea Palermo
- Implant Dentistry College of Medicine and Dentistry Birmingham, University of Birmingham, Birmingham B46BN, UK;
| | - Francesco Inchingolo
- Department of Interdisciplinary Medicine, University of Bari “Aldo Moro”, 70124 Bari, Italy; (A.M.I.); (F.P.); (V.C.); (G.G.); (D.A.); (M.C.); (G.P.); (F.I.)
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Liu J, Zhang C, Shan Z. Application of Artificial Intelligence in Orthodontics: Current State and Future Perspectives. Healthcare (Basel) 2023; 11:2760. [PMID: 37893833 PMCID: PMC10606213 DOI: 10.3390/healthcare11202760] [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: 08/24/2023] [Revised: 10/11/2023] [Accepted: 10/16/2023] [Indexed: 10/29/2023] Open
Abstract
In recent years, there has been the notable emergency of artificial intelligence (AI) as a transformative force in multiple domains, including orthodontics. This review aims to provide a comprehensive overview of the present state of AI applications in orthodontics, which can be categorized into the following domains: (1) diagnosis, including cephalometric analysis, dental analysis, facial analysis, skeletal-maturation-stage determination and upper-airway obstruction assessment; (2) treatment planning, including decision making for extractions and orthognathic surgery, and treatment outcome prediction; and (3) clinical practice, including practice guidance, remote care, and clinical documentation. We have witnessed a broadening of the application of AI in orthodontics, accompanied by advancements in its performance. Additionally, this review outlines the existing limitations within the field and offers future perspectives.
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Affiliation(s)
- Junqi Liu
- Division of Paediatric Dentistry and Orthodontics, Faculty of Dentistry, The University of Hong Kong, Hong Kong SAR, China;
| | - Chengfei Zhang
- Division of Restorative Dental Sciences, 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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Nomura Y, Hoshiyama M, Akita S, Naganishi H, Zenbutsu S, Matsuoka A, Ohnishi T, Haneishi H, Mitsukawa N. Computer-aided diagnosis for screening of lower extremity lymphedema in pelvic computed tomography images using deep learning. Sci Rep 2023; 13:16214. [PMID: 37758908 PMCID: PMC10533488 DOI: 10.1038/s41598-023-43503-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2023] [Accepted: 09/25/2023] [Indexed: 09/29/2023] Open
Abstract
Lower extremity lymphedema (LEL) is a common complication after gynecological cancer treatment, which significantly reduces the quality of life. While early diagnosis and intervention can prevent severe complications, there is currently no consensus on the optimal screening strategy for postoperative LEL. In this study, we developed a computer-aided diagnosis (CAD) software for LEL screening in pelvic computed tomography (CT) images using deep learning. A total of 431 pelvic CT scans from 154 gynecological cancer patients were used for this study. We employed ResNet-18, ResNet-34, and ResNet-50 models as the convolutional neural network (CNN) architecture. The input image for the CNN model used a single CT image at the greater trochanter level. Fat-enhanced images were created and used as input to improve classification performance. Receiver operating characteristic analysis was used to evaluate our method. The ResNet-34 model with fat-enhanced images achieved the highest area under the curve of 0.967 and an accuracy of 92.9%. Our CAD software enables LEL diagnosis from a single CT image, demonstrating the feasibility of LEL screening only on CT images after gynecologic cancer treatment. To increase the usefulness of our CAD software, we plan to validate it using external datasets.
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Affiliation(s)
- Yukihiro Nomura
- Center for Frontier Medical Engineering, Chiba University, 1-33 Yayoi-Cho, Inage-ku, Chiba, 263-8522, Japan.
| | - Masato Hoshiyama
- Department of Medical Engineering, Faculty of Engineering, Chiba University, 1-33 Yayoi-Cho, Inage-ku, Chiba, 263-8522, Japan
| | - Shinsuke Akita
- Department of Plastic, Reconstructive and Aesthetic Surgery, Graduate School of Medicine, Chiba University, 1-8-1 Inohana, Chuo-ku, Chiba, 260-8670, Japan
| | - Hiroki Naganishi
- Department of Plastic Surgery, Saiseikai Yokohamashi Nanbu Hospital, 3-2-10 Konandai, Konan-ku, Yokohama City, Kanagawa, 234-0054, Japan
| | - Satoki Zenbutsu
- Center for Frontier Medical Engineering, Chiba University, 1-33 Yayoi-Cho, Inage-ku, Chiba, 263-8522, Japan
| | - Ayumu Matsuoka
- Department of Gynecology and Maternal-Fetal Medicine, Chiba University Hospital, 1-8-1 Inohana, Chuo-ku, Chiba, 260-8670, Japan
| | - Takashi Ohnishi
- Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, 1133 York Avenue, New York, NY, 10065, USA
| | - Hideaki Haneishi
- Center for Frontier Medical Engineering, Chiba University, 1-33 Yayoi-Cho, Inage-ku, Chiba, 263-8522, Japan
| | - Nobuyuki Mitsukawa
- Department of Plastic, Reconstructive and Aesthetic Surgery, Graduate School of Medicine, Chiba University, 1-8-1 Inohana, Chuo-ku, Chiba, 260-8670, Japan
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14
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Suyun W, Suying Z. Application of big data classification effects based on neural network in video English course and relevant optimization suggestions. Soft comput 2023; 27:7615-7625. [PMID: 37192890 PMCID: PMC10105353 DOI: 10.1007/s00500-023-08123-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/28/2023] [Indexed: 05/18/2023]
Abstract
Due to the improvement of Internet technology and information technology, more and more students hope to learn and consolidate knowledge through video in the classroom. Teachers are more accustomed to using video in the classroom to improve and improve their teaching quality. In the current English class, teachers and students are more accustomed to using video English for teaching. English teaching videos are informative, intuitive and efficient. Through video teaching, we can make the classroom atmosphere more interesting, thus simplifying complex problems. In this context, this paper analyzes how neural networks can improve the application effect of English video courses in the context of big data, optimizes the PDCNO algorithm by using the neural network principle, and then discusses the impact of the optimized PDCNO algorithm on classification and system performance. This improves the accuracy of English video, reduces the execution time of the algorithm and reduces the memory occupation. Compared with ordinary video, the training time required under the same training parameters is shorter, and the convergence speed of the model itself will be faster. From the students' attitude towards video teaching, we can see that students prefer video English teaching, which also reflects the effectiveness of neural network big data in English video teaching. This paper introduces the neural network and big data technology into the video English course to improve the teaching effectiveness.
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Affiliation(s)
- Wen Suyun
- Department of Public Foreign Languages, Shijiazhuang University of Applied Technology, Shijiazhuang, 050081 Hebei China
| | - Zheng Suying
- Department of International Communication and Culture and Art, Hebei Professional College of Political Science and Law, Shijiazhuang, 050061 Hebei China
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15
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Duangsuwan J, Raocharernporn S, Thiradilok S, Manopatanakul S. Computerized three-dimensional cephalometric template for Thai adults. Heliyon 2023; 9:e15077. [PMID: 37095961 PMCID: PMC10121791 DOI: 10.1016/j.heliyon.2023.e15077] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2022] [Revised: 03/23/2023] [Accepted: 03/27/2023] [Indexed: 04/04/2023] Open
Abstract
Cephalometry is essential in several fields of study. These include health science, anthropology, and forensic studies. Furthermore, cephalometric norms are essential for numerous disciplines in the health sciences, such as clinical anatomy, plastic surgery, pediatrics, pediatric dentistry, orthodontics, oral and maxillofacial surgery, and forensic medicine. Three-dimensional (3D) cephalometric templates offer an advanced but simple method for these specialties. This study aimed to establish cephalometric norms by developing 3D templates for Thai adults, based on cephalometric landmark coordinates obtained from skull cone-beam computed tomography (CBCT) scans of individuals with normal skeletal patterns. Full-head CBCT scans of 45 individuals (20 men and 25 women) were obtained from the archive. All had a Class I molar relationship with minor crowded teeth. The scans were captured in a normal head position, and the coordinates of 21 important cephalometric landmarks were identified using Slicer 4.10.2 software. Manual affine transformation of all landmarks was used to transfer medical image coordinates (Digital Imaging and Communications in Medicine [DICOM] or Right-Anterior-Superior [RAS] systems) to Cartesian universal coordinates. Intraclass correlation coefficients (ICC) and Bland-Altman (BA) plots were used to assess inter- and intra-examiner reliability (ICC = 0.961-1.000, BA mean errors = -0.1 mm). Important cephalometric measurements were compared to the most relevant and recent study with a sample size of 200. Most measurements showed no statistical difference (one-sample t-test, p > 0.05). Independent samples t-tests revealed that there was no statistically significant difference in the X and Y axes; however, most mean coordinates between men and women in the Z-axis coordinates were statistically significant. Consequently, 3D cephalometric templates were generated separately for adult Thai men and women using landmark coordinates. Although they are available for all disciplines at no cost through QR codes, these templates should be used with care, especially for the upper and lower incisor angulation. The application and future development of each specialty are also described here.
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16
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Ryu J, Kim YH, Kim TW, Jung SK. Evaluation of artificial intelligence model for crowding categorization and extraction diagnosis using intraoral photographs. Sci Rep 2023; 13:5177. [PMID: 36997621 PMCID: PMC10063582 DOI: 10.1038/s41598-023-32514-7] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2023] [Accepted: 03/28/2023] [Indexed: 04/01/2023] Open
Abstract
Determining the severity of dental crowding and the necessity of tooth extraction for orthodontic treatment planning are time-consuming processes and there are no firm criteria. Thus, automated assistance would be useful to clinicians. This study aimed to construct and evaluate artificial intelligence (AI) systems to assist with such treatment planning. A total of 3,136 orthodontic occlusal photographs with annotations by two orthodontists were obtained. Four convolutional neural network (CNN) models, namely ResNet50, ResNet101, VGG16, and VGG19, were adopted for the AI process. Using the intraoral photographs as input, the crowding group and the necessity of tooth extraction were obtained. Arch length discrepancy analysis with AI-detected landmarks was used for crowding categorization. Various statistical and visual analyses were conducted to evaluate the performance. The maxillary and mandibular VGG19 models showed minimum mean errors of 0.84 mm and 1.06 mm for teeth landmark detection, respectively. Analysis of Cohen's weighted kappa coefficient indicated that crowding categorization performance was best in VGG19 (0.73), decreasing in the order of VGG16, ResNet101, and ResNet50. For tooth extraction, the maxillary VGG19 model showed the highest accuracy (0.922) and AUC (0.961). By utilizing deep learning with orthodontic photographs, dental crowding categorization and diagnosis of orthodontic extraction were successfully determined. This suggests that AI can assist clinicians in the diagnosis and decision making of treatment plans.
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Affiliation(s)
- Jiho Ryu
- Department of Orthodontics, School of Dentistry, Dental Research Institute, Seoul National University, 101 Daehak-ro, Jongno-gu, Seoul, 03080, Republic of Korea
| | - Ye-Hyun Kim
- Department of Orthodontics, School of Dentistry, Dental Research Institute, Seoul National University, 101 Daehak-ro, Jongno-gu, Seoul, 03080, Republic of Korea
| | - Tae-Woo Kim
- Department of Orthodontics, School of Dentistry, Dental Research Institute, Seoul National University, 101 Daehak-ro, Jongno-gu, Seoul, 03080, Republic of Korea.
| | - Seok-Ki Jung
- Department of Orthodontics, Korea University Guro Hospital, 148 Gurodong-ro, Guro-gu, Seoul, 08308, Republic of Korea.
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Chung EJ, Yang BE, Park IY, Yi S, On SW, Kim YH, Kang SH, Byun SH. Effectiveness of cone-beam computed tomography-generated cephalograms using artificial intelligence cephalometric analysis. Sci Rep 2022; 12:20585. [PMID: 36446924 PMCID: PMC9708822 DOI: 10.1038/s41598-022-25215-0] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2022] [Accepted: 11/28/2022] [Indexed: 12/02/2022] Open
Abstract
Lateral cephalograms and related analysis constitute representative methods for orthodontic treatment. However, since conventional cephalometric radiographs display a three-dimensional structure on a two-dimensional plane, inaccuracies may be produced when quantitative evaluation is required. Cone-beam computed tomography (CBCT) has minimal image distortion, and important parts can be observed without overlapping. It provides a high-resolution three-dimensional image at a relatively low dose and cost, but still shows a higher dose than a lateral cephalogram. It is especially true for children who are more susceptible to radiation doses and often have difficult diagnoses. A conventional lateral cephalometric radiograph can be obtained by reconstructing the Digital Imaging and Communications in Medicine data obtained from CBCT. This study evaluated the applicability and consistency of lateral cephalograms generated by CBCT using an artificial intelligence analysis program. Group I comprised conventional lateral cephalometric radiographs, group II comprised lateral cephalometric radiographs generated from CBCT using OnDemand 3D, and group III comprised lateral cephalometric radiographs generated from CBCT using Invivo5. All measurements in the three groups showed non-significant results. Therefore, a CBCT scan and artificial intelligence programs are efficient means when performing orthodontic analysis on pediatric or orthodontic patients for orthodontic diagnosis and planning.
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Affiliation(s)
- Eun-Ji Chung
- grid.488421.30000000404154154Department of Conservative Dentistry, Hallym University Sacred Heart Hospital, Anyang, 14068 Korea ,grid.256753.00000 0004 0470 5964Graduate School of Clinical Dentistry, Hallym University, Chuncheon, 24252 Republic of Korea
| | - Byoung-Eun Yang
- grid.488421.30000000404154154Department of Oral and Maxillofacial Surgery, Hallym University Sacred Heart Hospital, Anyang, 14068 Korea ,grid.256753.00000 0004 0470 5964Graduate School of Clinical Dentistry, Hallym University, Chuncheon, 24252 Republic of Korea ,grid.256753.00000 0004 0470 5964Institute of Clinical Dentistry, Hallym University, Chuncheon, 24252 Republic of Korea ,grid.488421.30000000404154154Dental Implant Robotic Center, Hallym University Sacred Heart Hospital, Anyang, 14068 Korea
| | - In-Young Park
- grid.488421.30000000404154154Department of Orthodontics, Hallym University Sacred Heart Hospital, Anyang, 14068 Korea ,grid.256753.00000 0004 0470 5964Graduate School of Clinical Dentistry, Hallym University, Chuncheon, 24252 Republic of Korea ,grid.256753.00000 0004 0470 5964Institute of Clinical Dentistry, Hallym University, Chuncheon, 24252 Republic of Korea
| | - Sangmin Yi
- grid.488421.30000000404154154Department of Oral and Maxillofacial Surgery, Hallym University Sacred Heart Hospital, Anyang, 14068 Korea ,grid.256753.00000 0004 0470 5964Graduate School of Clinical Dentistry, Hallym University, Chuncheon, 24252 Republic of Korea ,grid.256753.00000 0004 0470 5964Institute of Clinical Dentistry, Hallym University, Chuncheon, 24252 Republic of Korea
| | - Sung-Woon On
- grid.256753.00000 0004 0470 5964Graduate School of Clinical Dentistry, Hallym University, Chuncheon, 24252 Republic of Korea ,grid.256753.00000 0004 0470 5964Institute of Clinical Dentistry, Hallym University, Chuncheon, 24252 Republic of Korea ,grid.488450.50000 0004 1790 2596Department of Oral and Maxillofacial Surgery, Hallym University Dongtan Sacred Heart Hospital, Hwaseong, 18450 Korea
| | - Young-Hee Kim
- grid.488421.30000000404154154Department of Oral and Maxillofacial Radiology, Hallym University Sacred Heart Hospital, Anyang, 14068 Korea ,grid.256753.00000 0004 0470 5964Graduate School of Clinical Dentistry, Hallym University, Chuncheon, 24252 Republic of Korea ,grid.256753.00000 0004 0470 5964Institute of Clinical Dentistry, Hallym University, Chuncheon, 24252 Republic of Korea
| | - Sam-Hee Kang
- grid.488421.30000000404154154Department of Conservative Dentistry, Hallym University Sacred Heart Hospital, Anyang, 14068 Korea ,grid.256753.00000 0004 0470 5964Graduate School of Clinical Dentistry, Hallym University, Chuncheon, 24252 Republic of Korea ,grid.256753.00000 0004 0470 5964Institute of Clinical Dentistry, Hallym University, Chuncheon, 24252 Republic of Korea
| | - Soo-Hwan Byun
- grid.488421.30000000404154154Department of Oral and Maxillofacial Surgery, Hallym University Sacred Heart Hospital, Anyang, 14068 Korea ,grid.256753.00000 0004 0470 5964Graduate School of Clinical Dentistry, Hallym University, Chuncheon, 24252 Republic of Korea ,grid.256753.00000 0004 0470 5964Institute of Clinical Dentistry, Hallym University, Chuncheon, 24252 Republic of Korea ,grid.488421.30000000404154154Dental Implant Robotic Center, Hallym University Sacred Heart Hospital, Anyang, 14068 Korea
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Mohaideen K, Negi A, Verma DK, Kumar N, Sennimalai K, Negi A. Applications of artificial intelligence and machine learning in orthognathic surgery: A scoping review. JOURNAL OF STOMATOLOGY, ORAL AND MAXILLOFACIAL SURGERY 2022; 123:e962-e972. [PMID: 35803558 DOI: 10.1016/j.jormas.2022.06.027] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/29/2022] [Revised: 06/24/2022] [Accepted: 06/30/2022] [Indexed: 11/28/2022]
Abstract
Over the recent years, Artificial Intelligence (AI) has been progressing rapidly with its ability to mimic human cognitive functions. The potential applications of AI technology in diagnosis, treatment planning, and prognosis prediction have been demonstrated in various studies. The present scoping review aimed to provide an overview of AI and Machine Learning (ML) algorithms and their applications in orthognathic surgery. A comprehensive search was conducted in databases including PubMed, Embase, Scopus, Web of Science and OVID Medline until November 2021. This scoping review was conducted following the PRISMA-ScR guidelines. After applying the inclusion and exclusion criteria, a total of 19 studies were included for final review. AI has profoundly impacted the diagnosis and prediction of orthognathic surgeries with a clinically acceptable accuracy range. Furthermore, AI reduces the work burden of the clinician by eliminating the tedious registration procedures, thereby helping in efficient and automated planning. However, focussing on the research gaps, there is a need to foster the AI models/algorithms to contemporize their efficiency in clinical decision making, diagnosis and surgical planning in future studies.
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Affiliation(s)
- Kaja Mohaideen
- Department of Dentistry, AIIMS Bilaspur, Himachal Pradesh, India
| | - Anurag Negi
- Department of Dentistry, AIIMS Bilaspur, Himachal Pradesh, India.
| | | | - Neeraj Kumar
- Department of Dentistry, AIIMS Bilaspur, Himachal Pradesh, India
| | | | - Amita Negi
- Medical Officer (Dental) Bilaspur, Himachal Pradesh, India
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Artificial intelligence-aided detection of ectopic eruption of maxillary first molars based on panoramic radiography. J Dent 2022; 125:104239. [PMID: 35863549 DOI: 10.1016/j.jdent.2022.104239] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2021] [Revised: 07/13/2022] [Accepted: 07/17/2022] [Indexed: 02/08/2023] Open
Abstract
OBJECTIVES Ectopic eruption (EE) of maxillary permanent first molars (PFMs) is among the most frequent ectopic eruption, which leads to premature loss of adjacent primary second molars, impaction of premolars and a decrease in dental arch length. Apart from oral manifestations such as delayed eruption and discoloration of PFMs, panoramic radiography can reveal EE of maxillary PFMs as well. Identifying eruption anomalies in radiographs can be strongly experience-dependent, leading us to develop here an automatic model that can aid dentists in this task and allow timelier interventions. METHODS Panoramic X-ray images from 1480 patients aged 4-9 years old were used to train an auto-screening model. Another 100 panoramic images were used to validate and test the model. RESULTS The positive and negative predictive values of this auto-screening system were 0.86 and 0.88, respectively, with a specificity of 0.90 and a sensitivity of 0.86. Using the model to aid dentists in detecting EE on the 100 panoramic images led to higher sensitivity and specificity than when three experienced pediatric dentists detected EE manually. CONCLUSIONS Deep learning-based automatic screening system is useful and promising in the detection EE of maxillary PFMs with relatively high specificity. However, deep learning is not completely accurate in the detection of EE. To minimize the effect of possible false negative diagnosis, regular follow-ups and re-evaluation are required if necessary. CLINICAL SIGNIFICANCE Identification of EE through a semi-automatic screening model can improve the efficacy and accuracy of clinical diagnosis compared to human experts alone. This method may allow earlier detection and timelier intervention and management.
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Kim HJ, Kim KD, Kim DH. Deep convolutional neural network-based skeletal classification of cephalometric image compared with automated-tracing software. Sci Rep 2022; 12:11659. [PMID: 35804075 PMCID: PMC9270345 DOI: 10.1038/s41598-022-15856-6] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2021] [Accepted: 06/30/2022] [Indexed: 11/30/2022] Open
Abstract
This study aimed to investigate deep convolutional neural network- (DCNN-) based artificial intelligence (AI) model using cephalometric images for the classification of sagittal skeletal relationships and compare the performance of the newly developed DCNN-based AI model with that of the automated-tracing AI software. A total of 1574 cephalometric images were included and classified based on the A-point-Nasion- (N-) point-B-point (ANB) angle (Class I being 0–4°, Class II > 4°, and Class III < 0°). The DCNN-based AI model was developed using training (1334 images) and validation (120 images) sets with a standard classification label for the individual images. A test set of 120 images was used to compare the AI models. The agreement of the DCNN-based AI model or the automated-tracing AI software with a standard classification label was measured using Cohen’s kappa coefficient (0.913 for the DCNN-based AI model; 0.775 for the automated-tracing AI software). In terms of their performances, the micro-average values of the DCNN-based AI model (sensitivity, 0.94; specificity, 0.97; precision, 0.94; accuracy, 0.96) were higher than those of the automated-tracing AI software (sensitivity, 0.85; specificity, 0.93; precision, 0.85; accuracy, 0.90). With regard to the sagittal skeletal classification using cephalometric images, the DCNN-based AI model outperformed the automated-tracing AI software.
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Affiliation(s)
- Ho-Jin Kim
- Department of Orthodontics, School of Dentistry, Kyungpook National University, 2175, Dalgubul-Daero, Jung-Gu, Daegu, 41940, Korea.
| | - Kyoung Dong Kim
- School of Electronic and Electrical Engineering College of IT Engineering, Kyungpook National University, Daegu, Korea
| | - Do-Hoon Kim
- Medical Big Data Research Center, Kyungpook National University, Daegu, Korea
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21
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Performance of Artificial Intelligence Models Designed for Diagnosis, Treatment Planning and Predicting Prognosis of Orthognathic Surgery (OGS)—A Scoping Review. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12115581] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
The technological advancements in the field of medical science have led to an escalation in the development of artificial intelligence (AI) applications, which are being extensively used in health sciences. This scoping review aims to outline the application and performance of artificial intelligence models used for diagnosing, treatment planning and predicting the prognosis of orthognathic surgery (OGS). Data for this paper was searched through renowned electronic databases such as PubMed, Google Scholar, Scopus, Web of science, Embase and Cochrane for articles related to the research topic that have been published between January 2000 and February 2022. Eighteen articles that met the eligibility criteria were critically analyzed based on QUADAS-2 guidelines and the certainty of evidence of the included studies was assessed using the GRADE approach. AI has been applied for predicting the post-operative facial profiles and facial symmetry, deciding on the need for OGS, predicting perioperative blood loss, planning OGS, segmentation of maxillofacial structures for OGS, and differential diagnosis of OGS. AI models have proven to be efficient and have outperformed the conventional methods. These models are reported to be reliable and reproducible, hence they can be very useful for less experienced practitioners in clinical decision making and in achieving better clinical outcomes.
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