Artificial Intelligence and Its Clinical Applications in Orthodontics: A Systematic Review

Dipalma, Gianna; Inchingolo, Alessio Danilo; Inchingolo, Angelo Michele; Piras, Fabio; Carpentiere, Vincenzo; Garofoli, Grazia; Azzollini, Daniela; Campanelli, Merigrazia; Paduanelli, Gregorio; Palermo, Andrea; Inchingolo, Francesco

doi:10.3390/diagnostics13243677

Cited by 7 publications

(3 citation statements)

References 66 publications

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“…The constant updates and improvements driven by ongoing research keep TensorFlow at the forefront of machine learning technology, allowing users to leverage the latest advancements for optimal model performance. In our application, TensorFlow's adaptability, scalability, community support, and visualization tools are instrumental, making it an indispensable asset for achieving optimal results and advancing our specific goals [63], [64], [65], [66]. Thus, the neural network is trained using the squares identified by the Learning Focal Point (LFP) algorithm, illustrated in Fig.…”

Section: Fig 3 Points Of Difference Between the Female And Male Bodymentioning

confidence: 99%

Using Learning Focal Point Algorithm to Classify Emotional Intelligence

Sakhi,

Mansour,

Sekkaki

2024

Journal of Robotics and Control

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Recognizing the fundamental role of learners' emotions in the educational process, this study aims to enhance educational experiences by incorporating emotional intelligence (EI) into teacher robots through artificial intelligence and image processing technologies. The primary hurdle addressed is the inadequacy of conventional methods, particularly convolutional neural networks (CNNs) with pooling layers, in imbuing robots with emotional intelligence. To surmount this challenge, the research proposes an innovative solution—introducing a novel learning focal point (LFP) layer to replace pooling layers, resulting in significant enhancements in accuracy and other vital parameters. The distinctive contribution of this research lies in the creation and application of the LFP algorithm, providing a novel approach to emotion classification for teacher robots. The results showcase the LFP algorithm's superior performance compared to traditional CNN approaches. In conclusion, the study highlights the transformative impact of the LFP algorithm on the accuracy of classification models and, consequently, on emotionally intelligent teacher robots. This research contributes valuable insights to the convergence of artificial intelligence and education, with implications for future advancements in the field.

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Section: Fig 3 Points Of Difference Between the Female And Male Bodymentioning

confidence: 99%

Using Learning Focal Point Algorithm to Classify Emotional Intelligence

Sakhi,

Mansour,

Sekkaki

2024

Journal of Robotics and Control

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“…Consequently, orthodontists rely on arch shape classification to tailor treatment plans that align with the unique dental structures of their patients. Innovations in diagnostic technologies, including the application of artificial intelligence for arch shape prediction, have markedly enhanced the accuracy and reliability of these classifications, promising improved treatment outcomes and patient satisfaction [25].…”

Section: Introductionmentioning

confidence: 99%

DentalArch: AI-Based Arch Shape Detection in Orthodontics

Tamayo-Quintero,

Gómez-Mendoza,

Guevara-Pérez

2024

Applied Sciences

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Objective: This study aims to introduce and assess a novel AI-driven tool developed for the classification of orthodontic arch shapes into square, ovoid, and tapered categories. Methods: Between 2016 and 2019, we collected 450 digital dental models. Applying our inclusion and exclusion criteria, we refined our dataset to 50 models, ensuring a focused and detailed analysis. Plaster casts were digitized into 3D models with AutoScan-DS-EX. Three trained evaluators then measured mesiodistal and arch widths using MeshLab. The development of DentalArch was undertaken in two versions: the first version incorporates 18 input parameters, including mesiodistal widths (from the first molar to the first molar, totaling 14) and arch widths (1 intercanine, 2 interpremolar, and 1 intermolar, totaling 4); the second version uses only 4 parameters related to arch widths. Both versions aim to predict the arch shape. An evaluation of 28 machine learning methods through a k = 5-fold cross-validation was conducted to determine the most effective techniques. Results: In the tests, the performance evaluation of the DentalArch software in detecting arch shapes revealed that version 1, which analyzes 18 parameters, achieved an accuracy of 94.7% for the lower arch and 93% for the upper arch. The more streamlined version 2, which assesses only four parameters, also showed high precision with an accuracy of 93.0% for the lower arch and 92.7% for the upper arch. Conclusions: DentalArch provides a tool with potential use in orthodontic diagnostics, particularly in the task of arch shape classification. The software offers a less subjective and data-driven approach to arch shape determination. Moreover, the open-source nature of DentalArch ensures its global availability and encourages contributions from the orthodontic community.

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“…eHealth solutions can facilitate primary healthcare delivery and contribute to the realization of universal health coverage [ 70 , 71 ]. eHealth technologies include mobile devices, telemedicine, artificial intelligence, and big data analytics [ 72 , 73 , 74 , 75 , 76 , 77 , 78 , 79 ]. The application of eHealth tools in PC leads to improvements at various levels, e.g., prevention, diagnostics, therapy, patient management, homecare for the elderly, risk prediction, clinical decision support, accuracy of diagnosis, optimization of healthcare resources, patient satisfaction of healthcare, equity of care, and health cost reduction [ 61 , 80 , 81 , 82 , 83 , 84 , 85 ].…”

Section: Introductionmentioning

confidence: 99%

Management of Rheumatoid Arthritis in Primary Care: A Scoping Review

Inchingolo,

Fatone

et al. 2024

IJERPH

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Rheumatoid arthritis (RA) can lead to severe joint impairment and chronic disability. Primary care (PC), provided by general practitioners (GPs), is the first level of contact for the population with the healthcare system. The aim of this scoping review was to analyze the approach to RA in the PC setting. PubMed, Scopus, and Web of Science were searched using the MESH terms “rheumatoid arthritis” and “primary care” from 2013 to 2023. The search strategy followed the PRISMA-ScR guidelines. The 61 articles selected were analyzed qualitatively in a table and discussed in two sections, namely criticisms and strategies for the management of RA in PC. The main critical issues in the management of RA in PC are the following: difficulty and delay in diagnosis, in accessing rheumatological care, and in using DMARDs by GPs; ineffective communication between GPs and specialists; poor patient education; lack of cardiovascular prevention; and increase in healthcare costs. To overcome these criticisms, several management strategies have been identified, namely early diagnosis of RA, quick access to rheumatology care, effective communication between GPs and specialists, active patient involvement, screening for risk factors and comorbidities, clinical audit, interdisciplinary patient management, digital health, and cost analysis. PC appears to be the ideal healthcare setting to reduce the morbidity and mortality of chronic disease, including RA, if a widespread change in GPs’ approach to the disease and patients is mandatory.

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Artificial Intelligence and Its Clinical Applications in Orthodontics: A Systematic Review

Cited by 7 publications

References 66 publications

Using Learning Focal Point Algorithm to Classify Emotional Intelligence

Using Learning Focal Point Algorithm to Classify Emotional Intelligence

DentalArch: AI-Based Arch Shape Detection in Orthodontics

Management of Rheumatoid Arthritis in Primary Care: A Scoping Review

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