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All Journal Journal of Dentistry Indonesia International Journal of Artificial Intelligence Research
Nguyen, Trung Thanh
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Computer Science & IT Dentistry Electrical & Electronics Engineering

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Assessment of Mandibular Incisive Canal and Anterior Loop in Cone Beam Computed Tomography in Vietnamese Mature Patients: A Retrospective Study Le, Anh Kha; Tran, Thao Phuong; Nguyen, Tra Thu; Pham, Loc Nguyen Gia; Nguyen, Trung Thanh; Do, Viet Hoang
Journal of Dentistry Indonesia Vol. 31, No. 2
Publisher : UI Scholars Hub

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Abstract

Objective: This study aimed to assess the anatomical length of the mandibular incisive canal and anterior loop and the distance to surrounding structures. Methods: Our study was conducted on 70 cone beam computed tomography (CBCT) films of 70 Vietnamese adult outpatients (40 females, 30 males) at Hanoi Medical University, Vietnam. T-test was applied to assess the difference between the two sides and genders. Results: The mean length of the mandibular incisive canal (MIC) was 12.83 ± 5.13 mm. The anterior loop (AL) prevalence was 62.86%, with an average length of 2.37 ± 0.90 mm. The difference between the right and left sides was statistically significant (p < 0.05), measuring 2.51 ± 0.87 mm and 2.24 ± 0.92 mm, respectively. Our research findings revealed that the distance from the MIC to the alveolar border was approximately twice as long as the distance to the inferior border, with measurements exceeding 17 mm, and it was closer to the buccal cortical bone than the lingual border. Conclusion: The length of AL on the right side was greater. Due to the high prevalence of the MIC and the AL, clinicians should observe the mandibular incisive canal and anterior loop on CBCT scans before performing clinical procedures to avoid injuries.
Research on the Application of Artificial Intelligence in Hand Rehabilitation by Estimating Hand Grip Force using EMG Data Nguyen, Tien Manh; Takagi, Motoki; Nguyen, Trung Thanh; Tran, Hieu Huy; Dao, Khanh Viet Trong
International Journal of Artificial Intelligence Research Vol 9, No 1 (2025): June
Publisher : Universitas Dharma Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29099/ijair.v9i1.1381

Abstract

The human hand is a complex and functionally significant anatomical structure, playing a critical role in daily activities, communication, and professional tasks. Any impairment due to injury, neurological disorders, or musculoskeletal diseases can severely affect an individual's quality of life. Conditions such as stroke-induced hemiparesis, arthritis, carpal tunnel syndrome, and tendon injuries often necessitate rehabilitation to restore function, minimize pain, and prevent secondary complications. Traditional rehabilitation approaches, while beneficial, generally follow a standardized methodology, failing to account for individual variations in muscle strength, neuroplasticity, and adaptive capacity.Modern rehabilitation methods leverage advanced technologies such as electromyography (EMG) and hand grip force measurement to enhance therapy effectiveness. Additionally, artificial intelligence (AI) applications, particularly Long Short-Term Memory (LSTM) networks and Transformer models, have emerged as promising tools for personalized rehabilitation. These models analyze EMG signals to predict hand movement intentions, enabling adaptive rehabilitation strategies tailored to individual needs.  This study focuses on the construction of a real-time EMG signal acquisition system and uses them as input to LSTM and Transformer models to compare and analyze the performance of the two types of models. By demonstrating the superiority of applying AI for personalization over the general AI approach, this study highlights the potential of AI in hand rehabilitation in particular and healthcare in general with its ability to specialize for each individual patient.
Co-Authors Dao, Khanh Viet Trong Do, Viet Hoang Le, Anh Kha Nguyen, Tien Manh Nguyen, Tra Thu Pham, Loc Nguyen Gia Takagi, Motoki Tran, Hieu Huy Tran, Thao Phuong