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All Journal Dental Journal (Majalah Kedokteran Gigi) Indonesian Journal of Dental Medicine
Marya, Anand
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Dentistry

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Evaluation of foot analysis in the presence of dental malocclusion: A systematic review Sofyanti, Ervina; Alhasyimi, Ananto Ali; Farmasyanti, Cendrawasih Andusyana; Purbiati, Maria; Mardiati, Endah; Narmada, Ida Bagus; Utomo, Haryono; Gio, Prana Ugiana; Marya, Anand
Dental Journal (Majalah Kedokteran Gigi) Vol. 57 No. 4 (2024): December
Publisher : Faculty of Dental Medicine, Universitas Airlangga https://fkg.unair.ac.id/en

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/j.djmkg.v57.i4.p317-323

Abstract

Background: Previous studies have reported that the imbalance of occlusion, cervical spine, length of the limb, and asymmetric leg length are related to the presence of muscle compensation. These issues influence some strategies in orthodontic diagnosis for developing an updated stomatognathic system over multidisciplinary care issues. Purpose: This study aimed to obtain orthodontic findings and an orthopedic relationship between foot posture/function and dental malocclusion based on cross-sectional studies. Review: Some findings about postural system disequilibrium in static and dynamic foot postural related to occlusal changes have been reported following the mixed dentition phase. From 248 articles, 5 articles were selected and assessed with the Joanna Briggs Institute critical appraisal cross-sectional checklist and analyzed. Conclusions: Although the selected eligible articles demonstrated a low risk of bias in agreement (p > 0.05) among authors, the evaluation of those cross-sectional studies is still limited and requires further longitudinal studies with precise diagnostic parameters. Those parameters for evaluating the relationship between foot posture and malocclusion should encompass both static and dynamic approaches, including those related to mandibular position, tongue posture and function, bad habits, and temporomandibular disorder.
Application of artificial intelligence for dental age estimation in children and adolescents: A review Kurniawan, Arofi; Chusida, An'nisaa; Rahmaputri, Annisa; Nurmalia, Salsabila; Prasetyo, Aulia Imani Sri; Akbar, Aeeshah Aswi; Maritza, Yasmina Putri; Rizky, Beta Novia; Marini, Maria Istiqomah; Alias, Aspalilah; Marya, Anand
Indonesian Journal of Dental Medicine Vol. 8 No. 2 (2025): Indonesian Journal of Dental Medicine
Publisher : Faculty of Dental Medicine Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/ijdm.v8i2.2025.90-93

Abstract

Background: Accurate dental age estimation is essential across multiple disciplines, including forensic identification, pediatric dentistry, and legal medicine. Conventional approaches, while extensively utilized, are constrained by observer subjectivity, population-dependent variation, and limited reproducibility. The emergence of artificial intelligence (AI) particularly through machine learning (ML) and deep learning (DL) technologies has introduced a transformative shift in age estimation, offering automated, data-driven alternatives that enhance precision, consistency, and efficiency. Purpose: This review aims to critically examine the current applications of AI in dental age estimation for children and adolescents. Review: An online literature search was conducted in the PubMed database using a structured set of keywords, complemented by manual searches through Google Scholar to ensure comprehensive coverage. Nine relevant studies were identified, encompassing a range of artificial intelligence (AI) approaches, including artificial neural networks (ANN), convolutional neural networks (CNN), support vector machines (SVM), and other machine learning (ML) algorithms. These models were applied to established dental age estimation methods such as those proposed by Demirjian, Willems, Cameriere, and Al-Qahtani. Overall, AI-based models demonstrated superior performance compared to traditional techniques, showing lower mean absolute error values and higher classification accuracy across various age categories. Notably, several models achieved accuracy levels exceeding 90%, highlighting the potential of AI to enhance precision and reliability in dental age estimation. Conclusion: Artificial intelligence demonstrates significant potential in improving the accuracy, efficiency, and reproducibility of dental age estimation in children and adolescents. While current findings are promising, further validation across diverse populations and standardized protocols is necessary before widespread forensic and clinical adoption.
Co-Authors Akbar, Aeeshah Aswi Alias, Aspalilah Ananto Ali Alhasyimi Arofi Kurniawan Beta Novia Rizky Chusida, An'nisaa Endah Mardiati, Endah Ervina Sofyanti Farmasyanti, Cendrawasih Andusyana Gio, Prana Ugiana Haryono Utomo Ida Bagus Narmada Maria Istiqomah Marini Maria Purbiati Maritza, Yasmina Putri Nurmalia, Salsabila Prasetyo, Aulia Imani Sri Rahmaputri, Annisa