TMJOAI: An Artificial Web-Based Intelligence Tool for Early Diagnosis of the Temporomandibular Joint Osteoarthritis

Le, Celia; Deleat‐Besson, Romain; Turkestani, Najla Al; Cevidanes, Lúcia Helena Soares; Bianchi, Jonas; Zhang, Winston; Gurgel, Marcela; Shah, Hina; Prieto, Juan Carlos; Li, Tengfei

doi:10.1007/978-3-030-90874-4_8

Cited by 9 publications

(4 citation statements)

References 18 publications

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“…Composed of clinical, biological, and radiomics features, this study is expected to boost future studies by integrating multidimensional information for the diagnosis of OA. 47 Another study by Celia Le et al 48 included 46 TMJ OA patients and 46 healthy controls. 52 features were obtained from ROI, which was the lower part of the TMJ.…”

Section: Application In Biochemical Biomarkersmentioning

confidence: 99%

The application of machine learning in early diagnosis of osteoarthritis: a narrative review

Xuan

Chen

et al. 2023

Therapeutic Advances in Musculoskeletal

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Osteoarthritis (OA) is the commonest musculoskeletal disease worldwide, with an increasing prevalence due to aging. It causes joint pain and disability, decreased quality of life, and a huge burden on healthcare services for society. However, the current main diagnostic methods are not suitable for early diagnosing patients of OA. The use of machine learning (ML) in OA diagnosis has increased dramatically in the past few years. Hence, in this review article, we describe the research progress in the application of ML in the early diagnosis of OA, discuss the current trends and limitations of ML approaches, and propose future research priorities to apply the tools in the field of OA. Accurate ML-based predictive models with imaging techniques that are sensitive to early changes in OA ahead of the emergence of clinical features are expected to address the current dilemma. The diagnostic ability of the fusion model that combines multidimensional information makes patient-specific early diagnosis and prognosis estimation of OA possible in the future.

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Section: Application In Biochemical Biomarkersmentioning

confidence: 99%

The application of machine learning in early diagnosis of osteoarthritis: a narrative review

Xuan

Chen

et al. 2023

Therapeutic Advances in Musculoskeletal

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“…In the literature, various studies on TMJ OA utilizing AI approaches can be found, as referenced in the following studies: [ 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 ].…”

Section: Literature On Tmjmentioning

confidence: 99%

A Comprehensive Review of Artificial Intelligence Based Algorithms Regarding Temporomandibular Joint Related Diseases

Ozsari,

Güzel,

Yılmaz

et al. 2023

Diagnostics

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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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“…These advances also optimize the concept of personalized care, where each patient receives a more customized treatment based on their biological profile, from modulating their microbiome or developing new targets of the inflammatory process such as specialized proresolving inflammatory mediators (Panigrahy et al 2021). Combining holistic approaches and precise data analysis with machine learning (ML) from multiple teams will lead to more accurate diagnoses, successful treatment outcomes, and fewer mistakes in dental practice (Le et al 2021).…”

Section: Introductionmentioning

confidence: 99%

The Forefront of Dentistry—Promising Tech-Innovations and New Treatments

DaSilva

Robinson

Shi³

et al. 2022

JDR Clinical & Translational Research

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Change in our world is happening quickly. For dentistry, this is no exception, and now is the time to foster new opportunities. Advances in the foundational science of dental, oral, and craniofacial health are expanding and enabling personalized care built on strong discoveries in the fields of microbiology, immunology, and neuroscience. Digital communication through electronic health records, patient portals, and teledentistry is emerging as vital new armamentarium for contemporary dental practice. Wearables linked with smartphone apps are trending to monitor pain levels, postoperative symptoms, wound healing, and home oral health habits. Oral fluid diagnostics are rapidly surfacing for use in diagnostics, drug testing, and hormone detection. Manipulation of the oral microbiome is under investigation for caries management and for potential impact on systemic conditions. Resolving mediators of inflammation are promising for addressing the chronic inflammation that plagues periodontal patients. New imaging modalities paired with digital approaches and 3-dimensional printing are formulating disruptions in the technical aspects of dentistry. Augmented reality and virtual reality are providing great potential as teaching tools for dental students and bear promise for lifelong learning for providers. Optimization of information technology and the incorporation of data science partnered with artificial intelligence and machine learning to improve patient care in a learning health care system approach will benefit large numbers of patients. These are exciting times for our profession, yet we need to navigate these paths carefully. Technology needs to significantly and positively change the experience of those involved (patients, dentists, staff, payors). New technologies need to be backed with solid research and implementation science. Automation should focus on improving care with attention to expanding care to the underserved and not lose the critical human connection that our patients need. There is great potential in the emerging innovations yet also great responsibility to ensure they are evidence based on rigorous science and deployable with equity and sensitivity. Knowledge Transfer Statement: This article discusses innovations in technology and treatments that have enormous potential to revolutionize our dental care, including novel concepts in electronic health records, communication between dentists and patients, biologics around diagnosis and treatment, digital dentistry, and, finally, the real-time optimization of information technology. The early implementation and validation of these innovations can drive down their costs and provide better dental and medical services to all members of our society.

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TMJOAI: An Artificial Web-Based Intelligence Tool for Early Diagnosis of the Temporomandibular Joint Osteoarthritis

Cited by 9 publications

References 18 publications

The application of machine learning in early diagnosis of osteoarthritis: a narrative review

The application of machine learning in early diagnosis of osteoarthritis: a narrative review

A Comprehensive Review of Artificial Intelligence Based Algorithms Regarding Temporomandibular Joint Related Diseases

The Forefront of Dentistry—Promising Tech-Innovations and New Treatments

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