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Bardideh E, Lal Alizadeh F, Amiri M, Ghorbani M. Designing an artificial intelligence system for dental occlusion classification using intraoral photographs: A comparative analysis between artificial intelligence-based and clinical diagnoses. Am J Orthod Dentofacial Orthop 2024; 166:125-137. [PMID: 38842962 DOI: 10.1016/j.ajodo.2024.03.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2023] [Revised: 03/01/2024] [Accepted: 03/01/2024] [Indexed: 08/02/2024]
Abstract
INTRODUCTION This study aimed to design an artificial intelligence (AI) system for dental occlusion classification using intraoral photographs. Moreover, the performance of this system was compared with that of an expert clinician. METHODS This study included 948 adult patients with permanent dentition who presented to the Department of Orthodontics, School of Dentistry, Mashhad University of Medical Sciences, during 2022-2023. The intraoral photographs taken from the patients in left, right, and frontal views (3 photographs for each patient) were collected and underwent augmentation, and about 7500 final photographs were obtained. Moreover, the patients were clinically examined by an expert orthodontist for malocclusion, overjet, and overbite and were classified into 6 groups: Class I, Class II, half-cusp Class II, Super Class I, Class III, and unclassifiable. In addition, a multistage neural network system was created and trained using the photographs of 700 patients. Then, it was used to classify the remaining 248 patients using their intraoral photographs. Finally, its performance was compared with that of the expert clinician. All statistical analyses were performed using the Stata software (version 17; Stata Corp, College Station, Tex). RESULTS The accuracy, precision, recall, and F1 score of the AI system in the malocclusion classification of molars were calculated to be 93.1%, 88.6%, 91.2%, and 89.7%, respectively, whereas the AI system had an accuracy, precision, recall, and F1 score of 89.1%, 88.8%, 91.42%, and 89.8% for malocclusion classification of canines, respectively. Moreover, the mean absolute error of the AI system accuracy was 1.98 ± 2.11 for overjet and 1.28 ± 1.60 for overbite classifications. CONCLUSIONS AI exhibited remarkable performance in detecting all classes of malocclusion, which was higher than that of orthodontists, especially in predicting angle classification. However, its performance was not acceptable in overjet and overbite measurement compared with expert orthodontists.
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Affiliation(s)
- Erfan Bardideh
- Orthodontics Department, Dental Research Center, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Farzaneh Lal Alizadeh
- Orthodontics Department, Dental Research Center, Mashhad University of Medical Sciences, Mashhad, Iran.
| | | | - Mahsa Ghorbani
- Orthodontics Department, Dental School, Mashhad University of Medical Sciences, Mashhad, Iran
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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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Huang J, Farpour N, Yang BJ, Mupparapu M, Lure F, Li J, Yan H, Setzer FC. Uncertainty-based Active Learning by Bayesian U-Net for Multi-label Cone-beam CT Segmentation. J Endod 2024; 50:220-228. [PMID: 37979653 PMCID: PMC10842728 DOI: 10.1016/j.joen.2023.11.002] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2023] [Revised: 10/31/2023] [Accepted: 11/03/2023] [Indexed: 11/20/2023]
Abstract
INTRODUCTION Training of Artificial Intelligence (AI) for biomedical image analysis depends on large annotated datasets. This study assessed the efficacy of Active Learning (AL) strategies training AI models for accurate multilabel segmentation and detection of periapical lesions in cone-beam CTs (CBCTs) using a limited dataset. METHODS Limited field-of-view CBCT volumes (n = 20) were segmented by clinicians (clinician segmentation [CS]) and Bayesian U-Net-based AL strategies. Two AL functions, Bayesian Active Learning by Disagreement [BALD] and Max_Entropy [ME], were used for multilabel segmentation ("Lesion"-"Tooth Structure"-"Bone"-"Restorative Materials"-"Background"), and compared to a non-AL benchmark Bayesian U-Net function. The training-to-testing set ratio was 4:1. Comparisons between the AL and Bayesian U-Net functions versus CS were made by evaluating the segmentation accuracy with the Dice indices and lesion detection accuracy. The Kruskal-Wallis test was used to assess statistically significant differences. RESULTS The final training set contained 26 images. After 8 AL iterations, lesion detection sensitivity was 84.0% for BALD, 76.0% for ME, and 32.0% for Bayesian U-Net, which was significantly different (P < .0001; H = 16.989). The mean Dice index for all labels was 0.680 ± 0.155 for Bayesian U-Net and 0.703 ± 0.166 for ME after eight AL iterations, compared to 0.601 ± 0.267 for Bayesian U-Net over the mean of all iterations. The Dice index for "Lesion" was 0.504 for BALD and 0.501 for ME after 8 AL iterations, and at a maximum 0.288 for Bayesian U-Net. CONCLUSIONS Both AL strategies based on uncertainty quantification from Bayesian U-Net BALD, and ME, provided improved segmentation and lesion detection accuracy for CBCTs. AL may contribute to reducing extensive labeling needs for training AI algorithms for biomedical image analysis in dentistry.
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Affiliation(s)
- Jiayu Huang
- School of Computing and Augmented Intelligence Arizona State University, Tempe, Arizona
| | - Nazbanoo Farpour
- Department of Endodontics, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Bingjian J Yang
- Department of Endodontics, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Muralidhar Mupparapu
- Department of Oral Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Fleming Lure
- MS Technologies Corporation, Rockville, Maryland
| | - Jing Li
- School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, Georgia
| | - Hao Yan
- School of Computing and Augmented Intelligence Arizona State University, Tempe, Arizona
| | - Frank C Setzer
- Department of Endodontics, University of Pennsylvania, Philadelphia, Pennsylvania.
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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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Kazimierczak N, Kazimierczak W, Serafin Z, Nowicki P, Lemanowicz A, Nadolska K, Janiszewska-Olszowska J. Correlation Analysis of Nasal Septum Deviation and Results of AI-Driven Automated 3D Cephalometric Analysis. J Clin Med 2023; 12:6621. [PMID: 37892759 PMCID: PMC10607148 DOI: 10.3390/jcm12206621] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2023] [Revised: 10/04/2023] [Accepted: 10/13/2023] [Indexed: 10/29/2023] Open
Abstract
The nasal septum is believed to play a crucial role in the development of the craniofacial skeleton. Nasal septum deviation (NSD) is a common condition, affecting 18-65% of individuals. This study aimed to assess the prevalence of NSD and its potential association with abnormalities detected through cephalometric analysis using artificial intelligence (AI) algorithms. The study included CT scans of 120 consecutive, post-traumatic patients aged 18-30. Cephalometric analysis was performed using an AI web-based software, CephX. The automatic analysis comprised all the available cephalometric analyses. NSD was assessed using two methods: maximum deviation from an ideal non-deviated septum and septal deviation angle (SDA). The concordance of repeated manual measurements and automatic analyses was assessed. Of the 120 cases, 90 met the inclusion criteria. The AI-based cephalometric analysis provided comprehensive reports with over 100 measurements. Only the hinge axis angle (HAA) and SDA showed significant (p = 0.039) negative correlations. The rest of the cephalometric analyses showed no correlation with the NSD indicators. The analysis of the agreement between repeated manual measurements and automatic analyses showed good-to-excellent concordance, except in the case of two angular measurements: LI-N-B and Pr-N-A. The CephX AI platform showed high repeatability in automatic cephalometric analyses, demonstrating the reliability of the AI model for most cephalometric analyses.
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Affiliation(s)
| | - Wojciech Kazimierczak
- Kazimierczak Private Dental Practice, Dworcowa 13/u6a, 85-009 Bydgoszcz, Poland
- Collegium Medicum, Nicolaus Copernicus University in Torun, Jagiellońska 13-15, 85-067 Bydgoszcz, Poland; (Z.S.)
| | - Zbigniew Serafin
- Collegium Medicum, Nicolaus Copernicus University in Torun, Jagiellońska 13-15, 85-067 Bydgoszcz, Poland; (Z.S.)
| | - Paweł Nowicki
- Kazimierczak Private Dental Practice, Dworcowa 13/u6a, 85-009 Bydgoszcz, Poland
| | - Adam Lemanowicz
- Collegium Medicum, Nicolaus Copernicus University in Torun, Jagiellońska 13-15, 85-067 Bydgoszcz, Poland; (Z.S.)
| | - Katarzyna Nadolska
- Collegium Medicum, Nicolaus Copernicus University in Torun, Jagiellońska 13-15, 85-067 Bydgoszcz, Poland; (Z.S.)
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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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