Application of Artificial Intelligence in Orthodontics: Current State and Future Perspectives

Liu, Junqi; Zhang, Chengfei; Shan, Zhiyi

doi:10.3390/healthcare11202760

Cited by 11 publications

(3 citation statements)

References 187 publications

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“…To predict diabetes outcomes, two machine learning models Random Forest and Logistic Regression were developed and assessed. Accuracy, precision, recall, F1 score, as well as area under the ROC curve were among the performance indicators [13]. The Random Forest model proved to be more accurate in predicting diabetes than Logistic Regression, with a 98% accuracy rate vs a 77% rate.…”

Section: Experimental Setup and Implementation Experimental Setup And...mentioning

confidence: 94%

Artificial Intelligence and Machine Learning in Diagnostics and Treatment Planning

Tak

2023

J Arti Inte & Cloud Comp

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This paper explores how machine learning (ML) and artificial intelligence (AI) are transforming treatment planning and diagnosis in the healthcare industry. These technologies, which make use of sophisticated algorithms and computer models, have shown great promise for improving the precision, effectiveness, and customized nature of medical therapies. When using AI and ML for diagnostics, large datasets from patient records to medical images must be analyzed. These technologies facilitate prevention and treatment by enabling rapid and exact illness identification through deep learning and pattern recognition algorithms. Predictive modeling also makes it possible to anticipate how a disease will progress, which makes preemptive and customized treatment plans possible. AI and ML play a major role in optimizing therapeutic techniques during treatment planning. These technologies aid in the development of the best treatment plans based on distinct responses, genetic characteristics, and other pertinent aspects by evaluating data specific to each patient. This promotes a more a patient-focused healthcare paradigm by minimizing side effects and increasing therapeutic efficacy. The study also looks at the difficulties and moral issues surrounding the application of artificial intelligence and machine learning to medicine. Notwithstanding the encouraging results, it is crucial to underline the necessity for strong validation, openness, and responsible technology deployment in order to guarantee these technologies' moral and trustworthy use in healthcare contexts. In summary, the combination of AI and ML has enormous potential to transform treatment planning and diagnosis, presenting hitherto unheard-of chances for precision medicine and better patient outcomes. As these technologies develop further, the way they fit into clinical workflows might completely change the way healthcare is delivered and usher in a new era of tailored, data-driven treatments.

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Section: Experimental Setup and Implementation Experimental Setup And...mentioning

confidence: 94%

Artificial Intelligence and Machine Learning in Diagnostics and Treatment Planning

Tak

2023

J Arti Inte & Cloud Comp

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“…In the past few years, artificial intelligence (AI) technology, including machine learning (ML) algorithms, has witnessed a rapid advancement in identifying valuable radiographic features based on the measurement input. Thanks to its capacity to process enormous amounts of data through high-dimensional analytical methods, AI technology has been dramatically applied in orthodontic diagnosis, such as cephalometric analysis and skeletal-maturation-stage determination, treatment planning, such as treatment outcome prediction, as well as clinical practice, such as remote care [ 14 ]. Specifically, ML has been used for the diagnosis and treatment of Class III malocclusion to improve prediction accuracy, which is expected to aid in the diagnosis and treatment planning of Class III malocclusion cases, especially for non-specialists [ 8 ].…”

Section: Introductionmentioning

confidence: 99%

Radiographic Imaging for the Diagnosis and Treatment of Patients with Skeletal Class III Malocclusion

Li,

Hung,

et al. 2024

Diagnostics

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Skeletal Class III malocclusion is one type of dentofacial deformity that significantly affects patients’ facial aesthetics and oral health. The orthodontic treatment of skeletal Class III malocclusion presents challenges due to uncertainties surrounding mandibular growth patterns and treatment outcomes. In recent years, disease-specific radiographic features have garnered interest from researchers in various fields including orthodontics, for their exceptional performance in enhancing diagnostic precision and treatment effect predictability. The aim of this narrative review is to provide an overview of the valuable radiographic features in the diagnosis and management of skeletal Class III malocclusion. Based on the existing literature, a series of analyses on lateral cephalograms have been concluded to identify the significant variables related to facial type classification, growth prediction, and decision-making for tooth extractions and orthognathic surgery in patients with skeletal Class III malocclusion. Furthermore, we summarize the parameters regarding the inter-maxillary relationship, as well as different anatomical structures including the maxilla, mandible, craniofacial base, and soft tissues from conventional and machine learning statistical models. Several distinct radiographic features for Class III malocclusion have also been preliminarily observed using cone beam computed tomography (CBCT) and magnetic resonance imaging (MRI).

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“…Due to the wide use of cone tomography in dentistry, new technologies are constantly being developed to increase the effectiveness and precision of imaging. In recent years, a new direction has been the use of artificial intelligence, including convolutional neural networks and deep learning models [34][35][36][37]. The use of AI significantly shortens the work of clinicians by automatically analyzing and describing structures on CBCT images.…”

Section: Introductionmentioning

confidence: 99%

Temporary Skeletal Anchorage Devices and Cone Beam Tomography in Orthodontics—Current Application and New Directions of Development

Aebisher,

Serafin,

Bartusik-Aebisher

2024

Applied Sciences

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Continuous progress in dentistry and orthodontics is crucial to ensuring high-quality diagnosis and treatment of patients, especially since malocclusions occur in up to half of the population. In addition to limiting the physiological functions of the masticatory system, they are often an aesthetic defect that may directly affect the well-being and even self-esteem of patients, especially in their teenage years and early adulthood. A holistic model of perceiving and treating orthodontic diseases, such as the Biocreative Orthodontic Strategy, focusing not only on the correction of the defect itself but also taking into account the least possible interference in the physiology of the masticatory system, limiting the use of appliances to a minimum, and taking into account the patient’s preferences, is a special alternative to conventional therapeutic models. In this review, we are presenting the current knowledge regarding the applications of temporary skeletal anchorage devices (TSAD) and cone beam computed tomography (CBCT) in orthodontics.

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Application of Artificial Intelligence in Orthodontics: Current State and Future Perspectives

Cited by 11 publications

References 187 publications

Artificial Intelligence and Machine Learning in Diagnostics and Treatment Planning

Artificial Intelligence and Machine Learning in Diagnostics and Treatment Planning

Radiographic Imaging for the Diagnosis and Treatment of Patients with Skeletal Class III Malocclusion

Temporary Skeletal Anchorage Devices and Cone Beam Tomography in Orthodontics—Current Application and New Directions of Development

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