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All Journal Sriwijaya Journal of Neurology Crown: Journal of Dentistry and Health Research
Serena Jackson
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Biochemistry, Genetics & Molecular Biology Dentistry Health Professions Medicine & Pharmacology Neuroscience Public Health

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Artificial Intelligence for Early Detection of Motor Neuron Disease Using Gait Analysis and Speech Patterns in Pekanbaru, Indonesia Sari Sulistyoningsih; Louisa Istarini; Dedi Sucipto; Serena Jackson; Agnes Mariska; Linda Purnama; Imanuel Simbolon
Sriwijaya Journal of Neurology Vol. 1 No. 2 (2023): Sriwijaya Journal of Neurology
Publisher : Phlox Institute: Indonesian Medical Research Organization

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59345/sjn.v1i1.28

Abstract

Introduction: Motor neuron disease (MND) is a devastating neurodegenerative disorder characterized by progressive muscle weakness, atrophy, and ultimately, paralysis. This study investigated the potential of artificial intelligence (AI) to detect MND in its early stages using gait analysis and speech pattern recognition in a population in Pekanbaru, Indonesia. Methods: A cross-sectional study was conducted at the Neurology Department of a tertiary referral hospital in Pekanbaru, Indonesia. A total of 150 participants aged 40-75 years were recruited and categorized into three groups. Gait analysis was performed using wearable sensors to collect data on stride length, cadence, swing time, stance time, and gait variability. Machine learning algorithms, including support vector machines (SVM), random forest (RF), and deep learning models like convolutional neural networks (CNN), were trained on the combined gait and speech data to classify participants into the three groups. Results: Significant differences were observed in gait parameters between the MND group and the other two groups. Individuals with MND exhibited shorter stride length (p<0.001), slower cadence (p<0.001), increased swing time variability (p=0.002), and reduced stance time (p=0.003). Speech analysis revealed distinct patterns in the MND group, including reduced speech rate (p<0.001), increased pause duration (p=0.004), and decreased vocal intensity (p=0.001). The AI models, particularly the CNN model, demonstrated high accuracy in differentiating individuals with MND from healthy controls and those with other neurological conditions. The CNN model achieved an accuracy of 94.7%, sensitivity of 92%, specificity of 96%, and an area under the receiver operating characteristic curve (AUC) of 0.98. Conclusion: AI-powered gait analysis and speech pattern recognition show promise as a non-invasive and cost-effective tool for the early detection of MND in Pekanbaru, Indonesia. This technology has the potential to improve diagnostic accuracy and facilitate timely intervention, ultimately enhancing the quality of life for individuals with MND.
Influence of Preparation Design on the Fracture Resistance of Endodontically Treated Teeth Restored with Full-Coverage Crowns in Jakarta, Indonesia Alexander Mulya; Nabila Saraswati; Serena Jackson; Made Swastika; Zainal Abidin Hasan
Crown: Journal of Dentistry and Health Research Vol. 1 No. 2 (2023): Crown: Journal of Dentistry and Health Research
Publisher : Phlox Institute: Indonesian Medical Research Organization

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59345/crown.v1i2.89

Abstract

Introduction: Endodontically treated teeth are more susceptible to fracture due to the loss of tooth structure and moisture. Full-coverage crowns are often used to restore these teeth and enhance their fracture resistance. However, the influence of different preparation designs on the fracture resistance of endodontically treated teeth remains a topic of investigation. This study aimed to evaluate the fracture resistance of endodontically treated teeth restored with full-coverage crowns with different preparation designs in Jakarta, Indonesia. Methods: Forty extracted human premolars were endodontically treated and divided into four groups (n=10): Group 1: Butt-joint margin with a 1 mm chamfer finish line; Group 2: Shoulder margin with a 1.5 mm chamfer finish line; Group 3: Deep chamfer margin with a 2 mm chamfer finish line; and Group 4: Shoulder margin with a rounded shoulder finish line. All teeth were prepared for full-coverage crowns and restored with standardized metal-ceramic crowns. A universal testing machine was used to apply compressive load to the teeth until fracture. The fracture resistance values were recorded in Newtons (N) and analyzed using one-way ANOVA and Tukey's post-hoc test (α=0.05). Results: The mean fracture resistance values (N) were as follows: Group 1 (1250 ± 150), Group 2 (1480 ± 180), Group 3 (1180 ± 130), and Group 4 (1550 ± 200). One-way ANOVA revealed significant differences in fracture resistance among the groups (p<0.05). Tukey's post-hoc test indicated that Group 4 exhibited significantly higher fracture resistance than Group 1 and Group 3 (p<0.05). Group 2 also demonstrated significantly higher fracture resistance than Group 3 (p<0.05). Conclusion: Within the limitations of this study, the shoulder margin with a rounded shoulder finish line provided the highest fracture resistance for endodontically treated teeth restored with full-coverage crowns. The butt-joint margin and deep chamfer margin preparations exhibited lower fracture resistance.
Co-Authors Agnes Mariska Alexander Mulya Dedi Sucipto Imanuel Simbolon Linda Purnama Louisa Istarini Made Swastika Nabila Saraswati Sari Sulistyoningsih Zainal Abidin Hasan