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Almășan O, Mureșanu S, Hedeșiu P, Cotor A, Băciuț M, Roman R, Team Project Group. An Examination of Temporomandibular Joint Disc Displacement through Magnetic Resonance Imaging by Integrating Artificial Intelligence: Preliminary Findings. MEDICINA (KAUNAS, LITHUANIA) 2024; 60:1396. [PMID: 39336437 PMCID: PMC11433800 DOI: 10.3390/medicina60091396] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/12/2024] [Revised: 08/11/2024] [Accepted: 08/23/2024] [Indexed: 09/30/2024]
Abstract
Background and Objectives: This research was aimed at constructing a complete automated temporomandibular joint disc position identification system that could assist with magnetic resonance imaging disc displacement diagnosis on oblique sagittal and oblique coronal images. Materials and Methods: The study included fifty subjects with magnetic resonance imaging scans of the temporomandibular joint. Oblique sagittal and coronal sections of the magnetic resonance imaging scans were analyzed. Investigations were performed on the right and left coronal images with a closed mouth, as well as right and left sagittal images with closed and open mouths. Three hundred sagittal and coronal images were employed to train the artificial intelligence algorithm. Results: The accuracy ratio of the completely computerized articular disc identification method was 81%. Conclusions: An automated and accurate evaluation of temporomandibular joint disc position was developed by using both oblique sagittal and oblique coronal magnetic resonance imaging images.
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Affiliation(s)
- Oana Almășan
- Department of Prosthetic Dentistry and Dental Materials, Iuliu Hațieganu University of Medicine and Pharmacy, 400006 Cluj-Napoca, Romania
| | - Sorana Mureșanu
- Department of Maxillofacial Surgery and Radiology, Iuliu Hațieganu University of Medicine and Pharmacy, 37 Iuliu Hossu Street, 400029 Cluj-Napoca, Romania
| | - Petra Hedeșiu
- Emil Racoviță College, 9-11 Mihail Kogălniceanu, 400084 Cluj-Napoca, Romania
| | - Andrei Cotor
- Computer Science Department, Babes Bolyai University, 1 Mihail Kogălniceanu, 400084 Cluj-Napoca, Romania
| | - Mihaela Băciuț
- Department of Maxillofacial Surgery and Radiology, Iuliu Hațieganu University of Medicine and Pharmacy, 37 Iuliu Hossu Street, 400029 Cluj-Napoca, Romania
| | - Raluca Roman
- Department of Maxillofacial Surgery and Radiology, Iuliu Hațieganu University of Medicine and Pharmacy, 37 Iuliu Hossu Street, 400029 Cluj-Napoca, Romania
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Lee YH, Jeon S, Won JH, Auh QS, Noh YK. Automatic detection and visualization of temporomandibular joint effusion with deep neural network. Sci Rep 2024; 14:18865. [PMID: 39143180 PMCID: PMC11324909 DOI: 10.1038/s41598-024-69848-9] [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: 04/24/2024] [Accepted: 08/09/2024] [Indexed: 08/16/2024] Open
Abstract
This study investigated the usefulness of deep learning-based automatic detection of temporomandibular joint (TMJ) effusion using magnetic resonance imaging (MRI) in patients with temporomandibular disorder and whether the diagnostic accuracy of the model improved when patients' clinical information was provided in addition to MRI images. The sagittal MR images of 2948 TMJs were collected from 1017 women and 457 men (mean age 37.19 ± 18.64 years). The TMJ effusion diagnostic performances of three convolutional neural networks (scratch, fine-tuning, and freeze schemes) were compared with those of human experts based on areas under the curve (AUCs) and diagnosis accuracies. The fine-tuning model with proton density (PD) images showed acceptable prediction performance (AUC = 0.7895), and the from-scratch (0.6193) and freeze (0.6149) models showed lower performances (p < 0.05). The fine-tuning model had excellent specificity compared to the human experts (87.25% vs. 58.17%). However, the human experts were superior in sensitivity (80.00% vs. 57.43%) (all p < 0.001). In gradient-weighted class activation mapping (Grad-CAM) visualizations, the fine-tuning scheme focused more on effusion than on other structures of the TMJ, and the sparsity was higher than that of the from-scratch scheme (82.40% vs. 49.83%, p < 0.05). The Grad-CAM visualizations agreed with the model learned through important features in the TMJ area, particularly around the articular disc. Two fine-tuning models on PD and T2-weighted images showed that the diagnostic performance did not improve compared with using PD alone (p < 0.05). Diverse AUCs were observed across each group when the patients were divided according to age (0.7083-0.8375) and sex (male:0.7576, female:0.7083). The prediction accuracy of the ensemble model was higher than that of the human experts when all the data were used (74.21% vs. 67.71%, p < 0.05). A deep neural network (DNN) was developed to process multimodal data, including MRI and patient clinical data. Analysis of four age groups with the DNN model showed that the 41-60 age group had the best performance (AUC = 0.8258). The fine-tuning model and DNN were optimal for judging TMJ effusion and may be used to prevent true negative cases and aid in human diagnostic performance. Assistive automated diagnostic methods have the potential to increase clinicians' diagnostic accuracy.
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Affiliation(s)
- Yeon-Hee Lee
- Department of Orofacial Pain and Oral Medicine, Kyung Hee University Dental Hospital, Kyung Hee University School of Dentistry, #613 Hoegi-Dong, Dongdaemun-gu, Seoul, 02447, Korea.
| | - Seonggwang Jeon
- Department of Computer Science, Hanyang University, Seoul, 04763, Korea
| | - Jong-Hyun Won
- Department of Computer Science, Hanyang University, Seoul, 04763, Korea
| | - Q-Schick Auh
- Department of Orofacial Pain and Oral Medicine, Kyung Hee University Dental Hospital, Kyung Hee University School of Dentistry, #613 Hoegi-Dong, Dongdaemun-gu, Seoul, 02447, Korea
| | - Yung-Kyun Noh
- Department of Computer Science, Hanyang University, Seoul, 04763, Korea.
- School of Computational Sciences, Korea Institute for Advanced Study (KIAS), Seoul, 02455, Korea.
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Rokhshad R, Mohammad-Rahimi H, Sohrabniya F, Jafari B, Shobeiri P, Tsolakis IA, Ourang SA, Sultan AS, Khawaja SN, Bavarian R, Palomo JM. Deep learning for temporomandibular joint arthropathies: A systematic review and meta-analysis. J Oral Rehabil 2024; 51:1632-1644. [PMID: 38757865 DOI: 10.1111/joor.13701] [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: 08/16/2023] [Revised: 02/20/2024] [Accepted: 04/09/2024] [Indexed: 05/18/2024]
Abstract
BACKGROUND AND OBJECTIVE The accurate diagnosis of temporomandibular disorders continues to be a challenge, despite the existence of internationally agreed-upon diagnostic criteria. The purpose of this study is to review applications of deep learning models in the diagnosis of temporomandibular joint arthropathies. MATERIALS AND METHODS An electronic search was conducted on PubMed, Scopus, Embase, Google Scholar, IEEE, arXiv, and medRxiv up to June 2023. Studies that reported the efficacy (outcome) of prediction, object detection or classification of TMJ arthropathies by deep learning models (intervention) of human joint-based or arthrogenous TMDs (population) in comparison to reference standard (comparison) were included. To evaluate the risk of bias, included studies were critically analysed using the quality assessment of diagnostic accuracy studies (QUADAS-2). Diagnostic odds ratios (DOR) were calculated. Forrest plot and funnel plot were created using STATA 17 and MetaDiSc. RESULTS Full text review was performed on 46 out of the 1056 identified studies and 21 studies met the eligibility criteria and were included in the systematic review. Four studies were graded as having a low risk of bias for all domains of QUADAS-2. The accuracy of all included studies ranged from 74% to 100%. Sensitivity ranged from 54% to 100%, specificity: 85%-100%, Dice coefficient: 85%-98%, and AUC: 77%-99%. The datasets were then pooled based on the sensitivity, specificity, and dataset size of seven studies that qualified for meta-analysis. The pooled sensitivity was 95% (85%-99%), specificity: 92% (86%-96%), and AUC: 97% (96%-98%). DORs were 232 (74-729). According to Deek's funnel plot and statistical evaluation (p =.49), publication bias was not present. CONCLUSION Deep learning models can detect TMJ arthropathies high sensitivity and specificity. Clinicians, and especially those not specialized in orofacial pain, may benefit from this methodology for assessing TMD as it facilitates a rigorous and evidence-based framework, objective measurements, and advanced analysis techniques, ultimately enhancing diagnostic accuracy.
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Affiliation(s)
- Rata Rokhshad
- Topic Group Dental Diagnostics and Digital Dentistry, ITU/WHO Focus Group AI on Health, Berlin, Germany
| | - Hossein Mohammad-Rahimi
- Topic Group Dental Diagnostics and Digital Dentistry, ITU/WHO Focus Group AI on Health, Berlin, Germany
- Division of Artificial Intelligence Research, University of Maryland School of Dentistry, Baltimore, Maryland, USA
| | - Fatemeh Sohrabniya
- Topic Group Dental Diagnostics and Digital Dentistry, ITU/WHO Focus Group AI on Health, Berlin, Germany
| | - Bahare Jafari
- Topic Group Dental Diagnostics and Digital Dentistry, ITU/WHO Focus Group AI on Health, Berlin, Germany
| | - Parnian Shobeiri
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, New York, United States
| | - Ioannis A Tsolakis
- Department of Orthodontics, School of Dentistry, Aristotle University of Thessaloniki, Thessaloniki, Greece
- Department of Orthodontics, School of Dental Medicine, Case Western Reserve University, Cleveland, Ohio, USA
| | - Seyed AmirHossein Ourang
- Dentofacial Deformities Research Center, Research Institute of Dental Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Ahmed S Sultan
- Division of Artificial Intelligence Research, University of Maryland School of Dentistry, Baltimore, Maryland, USA
- Department of Oncology and Diagnostic Sciences, University of Maryland School of Dentistry, Baltimore, Maryland, USA
- University of Maryland Marlene and Stewart Greenebaum Comprehensive Cancer Center, Baltimore, Maryland, USA
| | - Shehryar Nasir Khawaja
- Orofacial Pain Medicine, Shaukat Khanum Memorial Cancer Hospitals and Research Centres, Lahore and Peshawar, Pakistan
- School of Dental Medicine, Tufts University, Boston, Massachusetts, USA
| | - Roxanne Bavarian
- Department of Oral and Maxillofacial Surgery, Massachusetts General Hospital, Boston, Massachusetts, USA
- Department of Oral and Maxillofacial Surgery, Harvard School of Dental Medicine, Boston, Massachusetts, USA
| | - Juan Martin Palomo
- Department of Orthodontics, School of Dental Medicine, Case Western Reserve University, Cleveland, Ohio, USA
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Czako L, Sufliarsky B, Simko K, Sovis M, Vidova I, Farska J, Lifková M, Hamar T, Galis B. Exploring the Practical Applications of Artificial Intelligence, Deep Learning, and Machine Learning in Maxillofacial Surgery: A Comprehensive Analysis of Published Works. Bioengineering (Basel) 2024; 11:679. [PMID: 39061761 PMCID: PMC11274331 DOI: 10.3390/bioengineering11070679] [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: 04/21/2024] [Revised: 05/29/2024] [Accepted: 06/13/2024] [Indexed: 07/28/2024] Open
Abstract
Artificial intelligence (AI), deep learning (DL), and machine learning (ML) are computer, machine, and engineering systems that mimic human intelligence to devise procedures. These technologies also provide opportunities to advance diagnostics and planning in human medicine and dentistry. The purpose of this literature review was to ascertain the applicability and significance of AI and to highlight its uses in maxillofacial surgery. Our primary inclusion criterion was an original paper written in English focusing on the use of AI, DL, or ML in maxillofacial surgery. The sources were PubMed, Scopus, and Web of Science, and the queries were made on the 31 December 2023. The search strings used were "artificial intelligence maxillofacial surgery", "machine learning maxillofacial surgery", and "deep learning maxillofacial surgery". Following the removal of duplicates, the remaining search results were screened by three independent operators to minimize the risk of bias. A total of 324 publications from 1992 to 2023 were finally selected. These were calculated according to the year of publication with a continuous increase (excluding 2012 and 2013) and R2 = 0.9295. Generally, in orthognathic dentistry and maxillofacial surgery, AI and ML have gained popularity over the past few decades. When we included the keywords "planning in maxillofacial surgery" and "planning in orthognathic surgery", the number significantly increased to 7535 publications. The first publication appeared in 1965, with an increasing trend (excluding 2014-2018), with an R2 value of 0.8642. These technologies have been found to be useful in diagnosis and treatment planning in head and neck surgical oncology, cosmetic and aesthetic surgery, and oral pathology. In orthognathic surgery, they have been utilized for diagnosis, treatment planning, assessment of treatment needs, and cephalometric analyses, among other applications. This review confirms that the current use of AI and ML in maxillofacial surgery is focused mainly on evaluating digital diagnostic methods, especially radiology, treatment plans, and postoperative results. However, as these technologies become integrated into maxillofacial surgery and robotic surgery in the head and neck region, it is expected that they will be gradually utilized to plan and comprehensively evaluate the success of maxillofacial surgeries.
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Affiliation(s)
- Ladislav Czako
- Department of Oral and Maxillofacial Surgery, Faculty of Medicine, Comenius University in Bratislava and University Hospital, Ruzinovska 6, 826 06 Bratislava, Slovakia; (L.C.); (K.S.); (M.S.); (I.V.); (J.F.); (B.G.)
| | - Barbora Sufliarsky
- Department of Oral and Maxillofacial Surgery, Faculty of Medicine, Comenius University in Bratislava and University Hospital, Ruzinovska 6, 826 06 Bratislava, Slovakia; (L.C.); (K.S.); (M.S.); (I.V.); (J.F.); (B.G.)
| | - Kristian Simko
- Department of Oral and Maxillofacial Surgery, Faculty of Medicine, Comenius University in Bratislava and University Hospital, Ruzinovska 6, 826 06 Bratislava, Slovakia; (L.C.); (K.S.); (M.S.); (I.V.); (J.F.); (B.G.)
| | - Marek Sovis
- Department of Oral and Maxillofacial Surgery, Faculty of Medicine, Comenius University in Bratislava and University Hospital, Ruzinovska 6, 826 06 Bratislava, Slovakia; (L.C.); (K.S.); (M.S.); (I.V.); (J.F.); (B.G.)
| | - Ivana Vidova
- Department of Oral and Maxillofacial Surgery, Faculty of Medicine, Comenius University in Bratislava and University Hospital, Ruzinovska 6, 826 06 Bratislava, Slovakia; (L.C.); (K.S.); (M.S.); (I.V.); (J.F.); (B.G.)
| | - Julia Farska
- Department of Oral and Maxillofacial Surgery, Faculty of Medicine, Comenius University in Bratislava and University Hospital, Ruzinovska 6, 826 06 Bratislava, Slovakia; (L.C.); (K.S.); (M.S.); (I.V.); (J.F.); (B.G.)
| | - Michaela Lifková
- Department of Stomatology and Maxillofacial Surgery, Faculty of Medicine, Comenius University in Bratislava, St. Elisabeth Hospital Bratislava, Heydukova 10, 812 50 Bratislava, Slovakia;
| | - Tomas Hamar
- Institute of Medical Terminology and Foreign Languages, Faculty of Medicine, Comenius University in Bratislava, Moskovska 2, 811 08 Bratislava, Slovakia;
| | - Branislav Galis
- Department of Oral and Maxillofacial Surgery, Faculty of Medicine, Comenius University in Bratislava and University Hospital, Ruzinovska 6, 826 06 Bratislava, Slovakia; (L.C.); (K.S.); (M.S.); (I.V.); (J.F.); (B.G.)
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Buduru S, Almăşan O, Condor D, Tăut M, Mesaroş A, Manziuc M, Kui A. Therapeutic challenges in temporomandibular disorders. Med Pharm Rep 2024; 97:357-369. [PMID: 39234451 PMCID: PMC11370858 DOI: 10.15386/mpr-2687] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2023] [Accepted: 12/18/2023] [Indexed: 09/06/2024] Open
Abstract
Background and aims This study aimed at evaluating the etiology and treatment challenges of temporomandibular disorders (TMDs). Methods 160 subjects with TMDs, 38 males (23.8%) and 122 females (76.3%) were studied. A personalized coefficient was designated, which included the resolution of the main symptom, correction of secondary symptoms, patient collaboration (emotional parameter), treatment duration, and cost. Results The most frequent cause for consultation was muscle impairment (42.5%), or limitation of mouth opening, followed by joint impairment (23.1%). Muscle pain was noticed, particularly in the masseter (57.5%) and lateral pterygoid muscles (51.9%). Tooth pain or gingival retraction was frequently associated with tooth wear (48.1%) and dental abfraction (31.3%). Remote symptomatology was dominated by otologic symptomatology. Iatrogenic etiology was highest (69.4%), followed by untreated missing teeth (66.9%). Treatment options included muscle relaxation, occlusal balancing (equilibration), kinesitherapy, medication, and swallowing re-education. Most patients benefited from four to seven different types of therapy, which resulted in a higher cost and a longer and more uncomfortable treatment. The primary symptom was relieved in 82.3% of cases, with recurrence occurring in 15.7%. Conclusion The treatment of temporomandibular joint dysfunction is time-consuming, demanding, and intricate. Most patients required four to seven different types of therapy, which increased the expense, treatment duration, and suffering.
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Affiliation(s)
- Smaranda Buduru
- Prosthetic Dentistry and Dental Materials Department, Iuliu Hatieganu University of Medicine and Pharmacy, Cluj-Napoca, Romania
| | - Oana Almăşan
- Prosthetic Dentistry and Dental Materials Department, Iuliu Hatieganu University of Medicine and Pharmacy, Cluj-Napoca, Romania
| | - Daniela Condor
- Oral Rehabilitation Department, Iuliu Hatieganu University of Medicine and Pharmacy, Cluj-Napoca, Romania
| | - Manuela Tăut
- Prosthetic Dentistry and Dental Materials Department, Iuliu Hatieganu University of Medicine and Pharmacy, Cluj-Napoca, Romania
| | - Anca Mesaroş
- Propedeutics and Esthetics Department, Iuliu Hatieganu University of Medicine and Pharmacy, Cluj-Napoca, Romania
| | - Manuela Manziuc
- Prosthetic Dentistry and Dental Materials Department, Iuliu Hatieganu University of Medicine and Pharmacy, Cluj-Napoca, Romania
| | - Andreea Kui
- Prosthetic Dentistry and Dental Materials Department, Iuliu Hatieganu University of Medicine and Pharmacy, Cluj-Napoca, Romania
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Uppal S, Kumar Shrivastava P, Khan A, Sharma A, Kumar Shrivastav A. Machine learning methods in predicting the risk of malignant transformation of oral potentially malignant disorders: A systematic review. Int J Med Inform 2024; 186:105421. [PMID: 38552265 DOI: 10.1016/j.ijmedinf.2024.105421] [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: 11/28/2023] [Revised: 03/18/2024] [Accepted: 03/19/2024] [Indexed: 04/22/2024]
Abstract
BACKGROUND Oral Potentially Malignant Disorders (OPMDs) refer to a heterogenous group of clinical presentations with heightened rate of malignant transformation. Identification of risk levels in OPMDs is crucial to determine the need for active intervention in high-risk patients and routine follow-up in low-risk ones. Machine learning models has shown tremendous potential in several areas of dentistry that strongly suggest its application to estimate rate of malignant transformation of precancerous lesions. METHODS A comprehensive literature search was performed on Pubmed/MEDLINE, Web of Science, Scopus, Embase, Cochrane Library database to identify articles including machine learning models and algorithms to predict malignant transformation in OPMDs. Relevant bibliographic data, study characteristics, and outcomes were extracted for eligible studies. Quality of the included studies was assessed through the IJMEDI checklist. RESULTS Fifteen articles were found suitable for the review as per the PECOS criteria. Amongst all studies, highest sensitivity (100%) was recorded for U-net architecture, Peaks Random forest model, and Partial least squares discriminant analysis (PLSDA). Highest specificity (100%) was noted for PLSDA. Range of overall accuracy in risk prediction was between 95.4% and 74%. CONCLUSION Machine learning proved to be a viable tool in risk prediction, demonstrating heightened sensitivity, automation, and improved accuracy for predicting transformation of OPMDs. It presents an effective approach for incorporating multiple variables to monitor the progression of OPMDs and predict their malignant potential. However, its sensitivity to dataset characteristics necessitates the optimization of input parameters to maximize the efficiency of the classifiers.
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Affiliation(s)
- Simran Uppal
- Faculty of Dentistry, Jamia Millia Islamia, New Delhi, India.
| | | | - Atiya Khan
- Faculty of Dentistry, Jamia Millia Islamia, New Delhi, India.
| | - Aditi Sharma
- Faculty of Dentistry, Jamia Millia Islamia, New Delhi, India.
| | - Ayush Kumar Shrivastav
- Computer Science and Engineering, Centre for Development of Advanced Computing, Noida, Uttar Pradesh, India.
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Tyndall DA, Price JB, Gaalaas L, Spin-Neto R. Surveying the landscape of diagnostic imaging in dentistry's future: Four emerging technologies with promise. J Am Dent Assoc 2024; 155:364-378. [PMID: 38520421 DOI: 10.1016/j.adaj.2024.01.005] [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: 05/24/2023] [Revised: 01/04/2024] [Accepted: 01/07/2024] [Indexed: 03/25/2024]
Abstract
BACKGROUND Advances in digital radiography for both intraoral and panoramic imaging and cone-beam computed tomography have led the way to an increase in diagnostic capabilities for the dental care profession. In this article, the authors provide information on 4 emerging technologies with promise. TYPES OF STUDIES REVIEWED The authors feature the following: artificial intelligence in the form of deep learning using convolutional neural networks, dental magnetic resonance imaging, stationary intraoral tomosynthesis, and second-generation cone-beam computed tomography sources based on carbon nanotube technology and multispectral imaging. The authors review and summarize articles featuring these technologies. RESULTS The history and background of these emerging technologies are previewed along with their development and potential impact on the practice of dental diagnostic imaging. The authors conclude that these emerging technologies have the potential to have a substantial influence on the practice of dentistry as these systems mature. The degree of influence most likely will vary, with artificial intelligence being the most influential of the 4. CONCLUSIONS AND PRACTICAL IMPLICATIONS The readers are informed about these emerging technologies and the potential effects on their practice going forward, giving them information on which to base decisions on adopting 1 or more of these technologies. The 4 technologies reviewed in this article have the potential to improve imaging diagnostics in dentistry thereby leading to better patient care and heightened professional satisfaction.
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Al Turkestani N, Li T, Bianchi J, Gurgel M, Prieto J, Shah H, Benavides E, Soki F, Mishina Y, Fontana M, Rao A, Zhu H, Cevidanes L. A comprehensive patient-specific prediction model for temporomandibular joint osteoarthritis progression. Proc Natl Acad Sci U S A 2024; 121:e2306132121. [PMID: 38346188 PMCID: PMC10895339 DOI: 10.1073/pnas.2306132121] [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: 04/17/2023] [Accepted: 01/03/2024] [Indexed: 02/15/2024] Open
Abstract
Temporomandibular joint osteoarthritis (TMJ OA) is a prevalent degenerative disease characterized by chronic pain and impaired jaw function. The complexity of TMJ OA has hindered the development of prognostic tools, posing a significant challenge in timely, patient-specific management. Addressing this gap, our research employs a comprehensive, multidimensional approach to advance TMJ OA prognostication. We conducted a prospective study with 106 subjects, 74 of whom were followed up after 2 to 3 y of conservative treatment. Central to our methodology is the development of an innovative, open-source predictive modeling framework, the Ensemble via Hierarchical Predictions through Nested cross-validation tool (EHPN). This framework synergistically integrates 18 feature selection, statistical, and machine learning methods to yield an accuracy of 0.87, with an area under the ROC curve of 0.72 and an F1 score of 0.82. Our study, beyond technical advancements, emphasizes the global impact of TMJ OA, recognizing its unique demographic occurrence. We highlight key factors influencing TMJ OA progression. Using SHAP analysis, we identified personalized prognostic predictors: lower values of headache, lower back pain, restless sleep, condyle high gray level-GL-run emphasis, articular fossa GL nonuniformity, and long-run low GL emphasis; and higher values of superior joint space, mouth opening, saliva Vascular-endothelium-growth-factor, Matrix-metalloproteinase-7, serum Epithelial-neutrophil-activating-peptide, and age indicate recovery likelihood. Our multidimensional and multimodal EHPN tool enhances clinicians' decision-making, offering a transformative translational infrastructure. The EHPN model stands as a significant contribution to precision medicine, offering a paradigm shift in the management of temporomandibular disorders and potentially influencing broader applications in personalized healthcare.
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Affiliation(s)
- Najla Al Turkestani
- Department of Restorative Dentistry, Faculty of Dentistry, King Abdulaziz University, Jeddah21589, Saudi Arabia
- Department of Orthodontics and Pediatric Dentistry, School of Dentistry, University of Michigan, Ann Arbor, MI48109
| | - Tengfei Li
- Department of Psychiatry, The University of North Carolina at Chapel Hill, Chapel Hill, NC27599
| | - Jonas Bianchi
- Department of Orthodontics, University of the Pacific, Arthur A. Dugoni School of Dentistry, San Francisco, CA94103
| | - Marcela Gurgel
- Department of Orthodontics and Pediatric Dentistry, School of Dentistry, University of Michigan, Ann Arbor, MI48109
| | - Juan Prieto
- Department of Psychiatry, The University of North Carolina at Chapel Hill, Chapel Hill, NC27599
| | - Hina Shah
- Department of Psychiatry, The University of North Carolina at Chapel Hill, Chapel Hill, NC27599
| | - Erika Benavides
- Department of Periodontics & Oral Medicine, School of Dentistry, University of Michigan, Ann Arbor, MI48109
| | - Fabiana Soki
- Department of Periodontics & Oral Medicine, School of Dentistry, University of Michigan, Ann Arbor, MI48109
| | - Yuji Mishina
- Department of Biologic and Materials Sciences & Prosthodontics, School of Dentistry, University of Michigan, Ann Arbor, MI48109
| | - Margherita Fontana
- Department of Cariology, Restorative Sciences and Endodontics, School of Dentistry, University of Michigan, Ann Arbor, MI48109
| | - Arvind Rao
- Department of Radiation Oncology, University of Michigan, Ann Arbor, MI48109
- Department of Computational Medicine & Bioinformatics, School of Dentistry, University of Michigan, Ann Arbor, MI48109
| | - Hongtu Zhu
- Department of Radiology and Biomedical Research Imaging Center, The University of North Carolina at Chapel Hill, Chapel Hill, NC27599
| | - Lucia Cevidanes
- Department of Orthodontics and Pediatric Dentistry, School of Dentistry, University of Michigan, Ann Arbor, MI48109
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Chweidan H, Rudyuk N, Tzur D, Goldstein C, Almoznino G. Statistical Methods and Machine Learning Algorithms for Investigating Metabolic Syndrome in Temporomandibular Disorders: A Nationwide Study. Bioengineering (Basel) 2024; 11:134. [PMID: 38391620 PMCID: PMC10886027 DOI: 10.3390/bioengineering11020134] [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: 12/02/2023] [Revised: 01/23/2024] [Accepted: 01/25/2024] [Indexed: 02/24/2024] Open
Abstract
The objective of this study was to analyze the associations between temporomandibular disorders (TMDs) and metabolic syndrome (MetS) components, consequences, and related conditions. This research analyzed data from the Dental, Oral, Medical Epidemiological (DOME) records-based study which integrated comprehensive socio-demographic, medical, and dental databases from a nationwide sample of dental attendees aged 18-50 years at military dental clinics for 1 year. Statistical and machine learning models were performed with TMDs as the dependent variable. The independent variables included age, sex, smoking, each of the MetS components, and consequences and related conditions, including hypertension, hyperlipidemia, diabetes, impaired glucose tolerance (IGT), obesity, cardiac disease, obstructive sleep apnea (OSA), nonalcoholic fatty liver disease (NAFLD), transient ischemic attack (TIA), stroke, deep venous thrombosis (DVT), and anemia. The study included 132,529 subjects, of which 1899 (1.43%) had been diagnosed with TMDs. The following parameters retained a statistically significant positive association with TMDs in the multivariable binary logistic regression analysis: female sex [OR = 2.65 (2.41-2.93)], anemia [OR = 1.69 (1.48-1.93)], and age [OR = 1.07 (1.06-1.08)]. Features importance generated by the XGBoost machine learning algorithm ranked the significance of the features with TMDs (the target variable) as follows: sex was ranked first followed by age (second), anemia (third), hypertension (fourth), and smoking (fifth). Metabolic morbidity and anemia should be included in the systemic evaluation of TMD patients.
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Affiliation(s)
- Harry Chweidan
- Department of Prosthodontics, Oral and Maxillofacial Center, Israel Defense Forces, Medical Corps, Tel-Hashomer, Ramat Gan 02149, Israel
| | - Nikolay Rudyuk
- Department of Prosthodontics, Oral and Maxillofacial Center, Israel Defense Forces, Medical Corps, Tel-Hashomer, Ramat Gan 02149, Israel
| | - Dorit Tzur
- Medical Information Department, General Surgeon Headquarters, Israel Defense Forces, Medical Corps, Tel-Hashomer, Ramat Gan 02149, Israel
| | - Chen Goldstein
- Big Biomedical Data Research Laboratory, Dean's Office, Hadassah Medical Center, Faculty of Dental Medicine, Hebrew University of Jerusalem, Jerusalem 91120, Israel
| | - Galit Almoznino
- Big Biomedical Data Research Laboratory, Dean's Office, Hadassah Medical Center, Faculty of Dental Medicine, Hebrew University of Jerusalem, Jerusalem 91120, Israel
- Department of Oral Medicine, Sedation & Maxillofacial Imaging, Hadassah Medical Center, Faculty of Dental Medicine, Hebrew University of Jerusalem, Jerusalem 91120, Israel
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10
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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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11
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Shrivastava M, Ye L. Neuroimaging and artificial intelligence for assessment of chronic painful temporomandibular disorders-a comprehensive review. Int J Oral Sci 2023; 15:58. [PMID: 38155153 PMCID: PMC10754947 DOI: 10.1038/s41368-023-00254-z] [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: 08/01/2023] [Revised: 10/19/2023] [Accepted: 10/20/2023] [Indexed: 12/30/2023] Open
Abstract
Chronic Painful Temporomandibular Disorders (TMD) are challenging to diagnose and manage due to their complexity and lack of understanding of brain mechanism. In the past few decades' neural mechanisms of pain regulation and perception have been clarified by neuroimaging research. Advances in the neuroimaging have bridged the gap between brain activity and the subjective experience of pain. Neuroimaging has also made strides toward separating the neural mechanisms underlying the chronic painful TMD. Recently, Artificial Intelligence (AI) is transforming various sectors by automating tasks that previously required humans' intelligence to complete. AI has started to contribute to the recognition, assessment, and understanding of painful TMD. The application of AI and neuroimaging in understanding the pathophysiology and diagnosis of chronic painful TMD are still in its early stages. The objective of the present review is to identify the contemporary neuroimaging approaches such as structural, functional, and molecular techniques that have been used to investigate the brain of chronic painful TMD individuals. Furthermore, this review guides practitioners on relevant aspects of AI and how AI and neuroimaging methods can revolutionize our understanding on the mechanisms of painful TMD and aid in both diagnosis and management to enhance patient outcomes.
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Affiliation(s)
- Mayank Shrivastava
- Adams School of Dentistry, University of North Carolina, Chapel Hill, NC, USA
| | - Liang Ye
- Department of Rehabilitation Medicine, University of Minnesota Medical School, Minneapolis, MN, USA.
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12
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Moxley B, Stevens W, Sneed J, Pearl C. Novel Diagnostic and Therapeutic Approaches to Temporomandibular Dysfunction: A Narrative Review. Life (Basel) 2023; 13:1808. [PMID: 37763212 PMCID: PMC10533154 DOI: 10.3390/life13091808] [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: 07/20/2023] [Revised: 08/19/2023] [Accepted: 08/23/2023] [Indexed: 09/29/2023] Open
Abstract
Temporomandibular dysfunction (TMD) is a burgeoning area of study within the dental field. TMD is caused by abnormalities in the temporomandibular joint or muscles of mastication and can lead to pain, loss of function, and other complications. As this area of patient care receives increased focus, the ability to accurately diagnose TMD becomes paramount. The aim of this review is to summarize novel diagnostic and therapeutic techniques that have been proposed within the last approximately 3 years in order to inform readers of the cutting-edge advances in the field of TMD diagnosis and management, while also analyzing the clinical relevance of each study. A PubMed search was completed on 1 March 2023, using MeSH terms related to TMD diagnosis and treatment. The search yielded seven articles that pertained to the aim of this review article. The main findings from each study are summarized in this review article. These novel methods of diagnosing and treating TMD may improve our ability to assess and treat patients suffering from TMD.
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Affiliation(s)
- Brendan Moxley
- School of Dentistry, The University of Texas Health Science Center at Houston, Houston, TX 77054, USA; (W.S.); (J.S.)
| | - William Stevens
- School of Dentistry, The University of Texas Health Science Center at Houston, Houston, TX 77054, USA; (W.S.); (J.S.)
| | - Joel Sneed
- School of Dentistry, The University of Texas Health Science Center at Houston, Houston, TX 77054, USA; (W.S.); (J.S.)
| | - Craig Pearl
- Department of Oral and Maxillofacial Surgery, The University of Texas Health Science Center at Houston, Houston, TX 77054, USA;
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13
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Ozsari S, Güzel MS, Yılmaz D, Kamburoğlu K. A Comprehensive Review of Artificial Intelligence Based Algorithms Regarding Temporomandibular Joint Related Diseases. Diagnostics (Basel) 2023; 13:2700. [PMID: 37627959 PMCID: PMC10453523 DOI: 10.3390/diagnostics13162700] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2023] [Revised: 08/13/2023] [Accepted: 08/16/2023] [Indexed: 08/27/2023] Open
Abstract
Today, with rapid advances in technology, computer-based studies and Artificial Intelligence (AI) approaches are finding their place in every field, especially in the medical sector, where they attract great attention. The Temporomandibular Joint (TMJ) stands as the most intricate joint within the human body, and diseases related to this joint are quite common. In this paper, we reviewed studies that utilize AI-based algorithms and computer-aided programs for investigating TMJ and TMJ-related diseases. We conducted a literature search on Google Scholar, Web of Science, and PubMed without any time constraints and exclusively selected English articles. Moreover, we examined the references to papers directly related to the topic matter. As a consequence of the survey, a total of 66 articles within the defined scope were assessed. These selected papers were distributed across various areas, with 11 focusing on segmentation, 3 on Juvenile Idiopathic Arthritis (JIA), 10 on TMJ Osteoarthritis (OA), 21 on Temporomandibular Joint Disorders (TMD), 6 on decision support systems, 10 reviews, and 5 on sound studies. The observed trend indicates a growing interest in artificial intelligence algorithms, suggesting that the number of studies in this field will likely continue to expand in the future.
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Affiliation(s)
- Sifa Ozsari
- Department of Computer Engineering, Ankara University, 06830 Ankara, Turkey;
| | - Mehmet Serdar Güzel
- Department of Computer Engineering, Ankara University, 06830 Ankara, Turkey;
| | - Dilek Yılmaz
- Faculty of Dentistry, Baskent University, 06490 Ankara, Turkey;
| | - Kıvanç Kamburoğlu
- Department of Dentomaxillofacial Radiology, Ankara University, 06560 Ankara, Turkey;
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14
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Huang H, Zheng O, Wang D, Yin J, Wang Z, Ding S, Yin H, Xu C, Yang R, Zheng Q, Shi B. ChatGPT for shaping the future of dentistry: the potential of multi-modal large language model. Int J Oral Sci 2023; 15:29. [PMID: 37507396 PMCID: PMC10382494 DOI: 10.1038/s41368-023-00239-y] [Citation(s) in RCA: 30] [Impact Index Per Article: 30.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2023] [Revised: 07/06/2023] [Accepted: 07/13/2023] [Indexed: 07/30/2023] Open
Abstract
The ChatGPT, a lite and conversational variant of Generative Pretrained Transformer 4 (GPT-4) developed by OpenAI, is one of the milestone Large Language Models (LLMs) with billions of parameters. LLMs have stirred up much interest among researchers and practitioners in their impressive skills in natural language processing tasks, which profoundly impact various fields. This paper mainly discusses the future applications of LLMs in dentistry. We introduce two primary LLM deployment methods in dentistry, including automated dental diagnosis and cross-modal dental diagnosis, and examine their potential applications. Especially, equipped with a cross-modal encoder, a single LLM can manage multi-source data and conduct advanced natural language reasoning to perform complex clinical operations. We also present cases to demonstrate the potential of a fully automatic Multi-Modal LLM AI system for dentistry clinical application. While LLMs offer significant potential benefits, the challenges, such as data privacy, data quality, and model bias, need further study. Overall, LLMs have the potential to revolutionize dental diagnosis and treatment, which indicates a promising avenue for clinical application and research in dentistry.
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Affiliation(s)
- Hanyao Huang
- State Key Laboratory of Oral Diseases & National Clinical Research Center for Oral Diseases & Department of Oral and Maxillofacial Surgery, West China Hospital of Stomatology, Sichuan University, Chengdu, China.
| | - Ou Zheng
- Department of Civil, Environmental & Construction Engineering, University of Central Florida, Orlando, USA.
| | - Dongdong Wang
- Department of Civil, Environmental & Construction Engineering, University of Central Florida, Orlando, USA
| | - Jiayi Yin
- State Key Laboratory of Oral Diseases & National Clinical Research Center for Oral Diseases & Department of Oral and Maxillofacial Surgery, West China Hospital of Stomatology, Sichuan University, Chengdu, China
| | - Zijin Wang
- Department of Civil, Environmental & Construction Engineering, University of Central Florida, Orlando, USA
| | - Shengxuan Ding
- College of Transportation Engineering, University of Central Florida, Orlando, USA
| | - Heng Yin
- State Key Laboratory of Oral Diseases & National Clinical Research Center for Oral Diseases & Department of Oral and Maxillofacial Surgery, West China Hospital of Stomatology, Sichuan University, Chengdu, China
| | - Chuan Xu
- School of Transportation and Logistics, Southwest Jiaotong University, Chengdu, China
- C2SMART Center, Tandon School of Engineering, New York University, Brooklyn, USA
| | - Renjie Yang
- State Key Laboratory of Oral Diseases & National Clinical Research Center for Oral Diseases & Eastern Clinic, West China Hospital of Stomatology, Sichuan University, Chengdu, China
| | - Qian Zheng
- State Key Laboratory of Oral Diseases & National Clinical Research Center for Oral Diseases & Department of Oral and Maxillofacial Surgery, West China Hospital of Stomatology, Sichuan University, Chengdu, China
| | - Bing Shi
- State Key Laboratory of Oral Diseases & National Clinical Research Center for Oral Diseases & Department of Oral and Maxillofacial Surgery, West China Hospital of Stomatology, Sichuan University, Chengdu, China
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15
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Xu L, Chen J, Qiu K, Yang F, Wu W. Artificial intelligence for detecting temporomandibular joint osteoarthritis using radiographic image data: A systematic review and meta-analysis of diagnostic test accuracy. PLoS One 2023; 18:e0288631. [PMID: 37450501 PMCID: PMC10348514 DOI: 10.1371/journal.pone.0288631] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2023] [Accepted: 07/02/2023] [Indexed: 07/18/2023] Open
Abstract
In this review, we assessed the diagnostic efficiency of artificial intelligence (AI) models in detecting temporomandibular joint osteoarthritis (TMJOA) using radiographic imaging data. Based upon the PRISMA guidelines, a systematic review of studies published between January 2010 and January 2023 was conducted using PubMed, Web of Science, Scopus, and Embase. Articles on the accuracy of AI to detect TMJOA or degenerative changes by radiographic imaging were selected. The characteristics and diagnostic information of each article were extracted. The quality of studies was assessed by the QUADAS-2 tool. Pooled data for sensitivity, specificity, and summary receiver operating characteristic curve (SROC) were calculated. Of 513 records identified through a database search, six met the inclusion criteria and were collected. The pooled sensitivity, specificity, and area under the curve (AUC) were 80%, 90%, and 92%, respectively. Substantial heterogeneity between AI models mainly arose from imaging modality, ethnicity, sex, techniques of AI, and sample size. This article confirmed AI models have enormous potential for diagnosing TMJOA automatically through radiographic imaging. Therefore, AI models appear to have enormous potential to diagnose TMJOA automatically using radiographic images. However, further studies are needed to evaluate AI more thoroughly.
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Affiliation(s)
- Liang Xu
- The School of Stomatology, Fujian Medical University, Fuzhou, Fujian, China
- Department of Stomatology, The First Affiliated Hospital of Fujian Medical University, Fuzhou, Fujian, China
| | - Jiang Chen
- The School of Stomatology, Fujian Medical University, Fuzhou, Fujian, China
- School and Hospital of Stomatology, Fujian Medical University, Fuzhou, Fujian, China
| | - Kaixi Qiu
- Fuzhou No. 1 Hospital Affiliated with Fujian Medical University, Fuzhou, Fujian, China
| | - Feng Yang
- School and Hospital of Stomatology, Fujian Medical University, Fuzhou, Fujian, China
| | - Weiliang Wu
- The School of Stomatology, Fujian Medical University, Fuzhou, Fujian, China
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16
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Abesi F, Maleki M, Zamani M. Diagnostic performance of artificial intelligence using cone-beam computed tomography imaging of the oral and maxillofacial region: A scoping review and meta-analysis. Imaging Sci Dent 2023; 53:101-108. [PMID: 37405196 PMCID: PMC10315225 DOI: 10.5624/isd.20220224] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2022] [Revised: 02/13/2023] [Accepted: 02/22/2023] [Indexed: 04/12/2024] Open
Abstract
Purpose The aim of this study was to conduct a scoping review and meta-analysis to provide overall estimates of the recall and precision of artificial intelligence for detection and segmentation using oral and maxillofacial cone-beam computed tomography (CBCT) scans. Materials and Methods A literature search was done in Embase, PubMed, and Scopus through October 31, 2022 to identify studies that reported the recall and precision values of artificial intelligence systems using oral and maxillofacial CBCT images for the automatic detection or segmentation of anatomical landmarks or pathological lesions. Recall (sensitivity) indicates the percentage of certain structures that are correctly detected. Precision (positive predictive value) indicates the percentage of accurately identified structures out of all detected structures. The performance values were extracted and pooled, and the estimates were presented with 95% confidence intervals (CIs). Results In total, 12 eligible studies were finally included. The overall pooled recall for artificial intelligence was 0.91 (95% CI: 0.87-0.94). In a subgroup analysis, the pooled recall was 0.88 (95% CI: 0.77-0.94) for detection and 0.92 (95% CI: 0.87-0.96) for segmentation. The overall pooled precision for artificial intelligence was 0.93 (95% CI: 0.88-0.95). A subgroup analysis showed that the pooled precision value was 0.90 (95% CI: 0.77-0.96) for detection and 0.94 (95% CI: 0.89-0.97) for segmentation. Conclusion Excellent performance was found for artificial intelligence using oral and maxillofacial CBCT images.
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Affiliation(s)
- Farida Abesi
- Department of Oral and Maxillofacial Radiology, Dental Faculty, Babol University of Medical Sciences, Babol, Iran
| | - Mahla Maleki
- Student Research Committee, Babol University of Medical Sciences, Babol, Iran
| | - Mohammad Zamani
- Student Research Committee, Babol University of Medical Sciences, Babol, Iran
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17
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Dhopte A, Bagde H. Smart Smile: Revolutionizing Dentistry With Artificial Intelligence. Cureus 2023; 15:e41227. [PMID: 37529520 PMCID: PMC10387377 DOI: 10.7759/cureus.41227] [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: 06/23/2023] [Accepted: 06/30/2023] [Indexed: 08/03/2023] Open
Abstract
Artificial intelligence (AI) has emerged as a transformative technology in various industries, and its potential in dentistry is gaining significant attention. This abstract explores the future prospects of AI in dentistry, highlighting its potential to revolutionize clinical practice, improve patient outcomes, and enhance the overall efficiency of dental care. The application of AI in dentistry encompasses several key areas, including diagnosis, treatment planning, image analysis, patient management, and personalized care. AI algorithms have shown promising results in the automated detection and diagnosis of dental conditions, such as caries, periodontal diseases, and oral cancers, aiding clinicians in early intervention and improving treatment outcomes. Furthermore, AI-powered treatment planning systems leverage machine learning techniques to analyze vast amounts of patient data, considering factors like medical history, anatomical variations, and treatment success rates. These systems provide dentists with valuable insights and support in making evidence-based treatment decisions, ultimately leading to more predictable and tailored treatment approaches. While the potential of AI in dentistry is immense, it is essential to address certain challenges, including data privacy, algorithm bias, and regulatory considerations. Collaborative efforts between dental professionals, AI experts, and policymakers are crucial to developing robust frameworks that ensure the responsible and ethical implementation of AI in dentistry. Moreover, AI-driven robotics has introduced innovative approaches to dental surgery, enabling precise and minimally invasive procedures, and ultimately reducing patient discomfort and recovery time. Virtual reality (VR) and augmented reality (AR) applications further enhance dental education and training, allowing dental professionals to refine their skills in a realistic and immersive environment. AI holds tremendous promise in shaping the future of dentistry. Through its ability to analyze vast amounts of data, provide accurate diagnoses, facilitate treatment planning, improve image analysis, streamline patient management, and enable personalized care, AI has the potential to enhance dental practice and significantly improve patient outcomes. Embracing this technology and its future development will undoubtedly revolutionize the field of dentistry, fostering a more efficient, precise, and patient-centric approach to oral healthcare. Overall, AI represents a powerful tool that has the potential to revolutionize various aspects of society, from improving healthcare outcomes to optimizing business operations. Continued research, development, and responsible implementation of AI technologies will shape our future, unlocking new possibilities and transforming the way we live and work.
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Affiliation(s)
- Ashwini Dhopte
- Department of Oral Medicine and Radiology, Rama Dental College and Research Centre, Kanpur, IND
| | - Hiroj Bagde
- Department of Periodontology, Rama Dental College and Research Centre, Kanpur, IND
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18
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Mohammad-Rahimi H, Rokhshad R, Bencharit S, Krois J, Schwendicke F. Deep learning: A primer for dentists and dental researchers. J Dent 2023; 130:104430. [PMID: 36682721 DOI: 10.1016/j.jdent.2023.104430] [Citation(s) in RCA: 19] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2022] [Revised: 01/04/2023] [Accepted: 01/16/2023] [Indexed: 01/21/2023] Open
Abstract
OBJECTIVES Despite deep learning's wide adoption in dental artificial intelligence (AI) research, researchers from other dental fields and, more so, dental professionals may find it challenging to understand and interpret deep learning studies, their employed methods, and outcomes. The objective of this primer is to explain the basic concept of deep learning. It will lay out the commonly used terms, and describe different deep learning approaches, their methods, and outcomes. METHODS Our research is based on the latest review studies, medical primers, as well as the state-of-the-art research on AI and deep learning, which have been gathered in the current study. RESULTS In this study, a basic understanding of deep learning models and various approaches to deep learning is presented. An overview of data management strategies for deep learning projects is presented, including data collection, data curation, data annotation, and data preprocessing. Additionally, we provided a step-by-step guide for completing a real-world project. CONCLUSION Researchers and clinicians can benefit from this study by gaining insight into deep learning. It can be used to critically appraise existing work or plan new deep learning projects. CLINICAL SIGNIFICANCE This study may be useful to dental researchers and professionals who are assessing and appraising deep learning studies within the field of dentistry.
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Affiliation(s)
- Hossein Mohammad-Rahimi
- Topic Group Dental Diagnostics and Digital Dentistry, ITU/WHO Focus Group AI on Health, Berlin, Federal Republic of Germany
| | - Rata Rokhshad
- Department of Medicine, Section of Endocrinology, Nutrition, and Diabetes, Vitamin D, Boston University Medical Center, Boston, MA, USA
| | - Sompop Bencharit
- Department of Oral and Craniofacial Molecular Biology, Philips Institute for Oral Health Research, School of Dentistry, and Department of Biomedical Engineering, College of Engineering, Virginia Commonwealth University, Richmond, VA 23298, USA
| | - Joachim Krois
- Topic Group Dental Diagnostics and Digital Dentistry, ITU/WHO Focus Group AI on Health, Berlin, Federal Republic of Germany
| | - Falk Schwendicke
- Topic Group Dental Diagnostics and Digital Dentistry, ITU/WHO Focus Group AI on Health, Berlin, Federal Republic of Germany; Department of Oral Diagnostics, Digital Health and Health Services Research, Charité - Universitätsmedizin Berlin, Aßmannshauser Str. 4-6, Berlin 14197, Federal Republic of Germany.
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19
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Cai L, Al Turkestani N, Cevidanes L, Bianchi J, Gurgel M, Najarian K, Soroushmehr R. Integrative Risk Predictors of Temporomandibular Joint Osteoarthritis Progression. PROCEEDINGS OF SPIE--THE INTERNATIONAL SOCIETY FOR OPTICAL ENGINEERING 2023; 12464:124641N. [PMID: 38533187 PMCID: PMC10964797 DOI: 10.1117/12.2651940] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/28/2024]
Abstract
In this paper we propose feature selection and machine learning approaches to identify a combination of features for risk prediction of Temporomandibular Joint (TMJ) disease progression. In a sample of 32 TMJ osteoarthritis and 38 controls, feature selection of 5 clinical comorbidities, 43 quantitative imaging, 28 biological features and was performed using Maximum Relevance Minimum Redundancy, Chi-Square and Least Absolute Shrinkage and Selection Operator (LASSO) and Recursive Feature Elimination. We compared the performance of learning using concave and convex kernels (LUCCK), Support Vector Machine (SVM) and Random Forest (RF) approaches to predict disease cure/improvement or persistence/worsening. We show that the SVM model using LASSO achieves area under the curve (AUC), sensitivity and precision of 0.92±0.08, 0.85±0.19 and 0.76 ±0.18, respectively. Baseline levels of headaches, lower back pain, restless sleep, muscle soreness, articular fossa bone surface/bone volume and trabecular separation, condylar High Gray Level Run Emphasis and Short Run High Gray Level Emphasis, saliva levels of 6Ckine, Osteoprotegerin (OPG) and Angiogenin, and serum levels of 6ckine and Brain Derived Neurotrophic Factor (BDNF) were the most frequently occurring features to predict more severe TMJ osteoarthritis prognosis.
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Affiliation(s)
- Lingrui Cai
- Department of Computational Medicine and Bioinformatics, University of Michigan, 100 Washtenaw Ave, Ann Arbor, 48109, MI,USA
| | - Najla Al Turkestani
- Department of Orthodontics and Pediatric Dentistry, University of Michigan, 1011 North University Ave, Ann Arbor, 48109, MI, USA
- Department of Restorative and Aesthetic Dentistry, Faculty of Dentistry, King Abdulaziz University, Jeddah, 22252, Saudi Arabia
| | - Lucia Cevidanes
- Department of Orthodontics and Pediatric Dentistry, University of Michigan, 1011 North University Ave, Ann Arbor, 48109, MI, USA
| | - Jonas Bianchi
- Department of Orthodontics, University of the Pacific, Arthur A. Dugoni School of Dentistry, 155 5th St, San Francisco, 94103, CA, United States
| | - Marcela Gurgel
- Department of Orthodontics and Pediatric Dentistry, University of Michigan, 1011 North University Ave, Ann Arbor, 48109, MI, USA
| | - Kayvan Najarian
- Department of Computational Medicine and Bioinformatics, University of Michigan, 100 Washtenaw Ave, Ann Arbor, 48109, MI,USA
| | - Reza Soroushmehr
- Department of Computational Medicine and Bioinformatics, University of Michigan, 100 Washtenaw Ave, Ann Arbor, 48109, MI,USA
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