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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Jurnal Fisika Unand Dentino: Jurnal Kedokteran Gigi Jurnal Ilmu Kesehatan Masyarakat (The Public Health Science Journal) B-Dent, Jurnal Kedokteran Gigi Universitas Baiturrahmah Buletin Ilmiah Nagari Membangun Jurnal EduHealth Jurnal Kesehatan Gigi dan Mulut (JKGM) Andalas Dental Journal Cakradonya Dental Journal Jurnal Ilmu Kesehatan Masyarakat (The Public Health Science Journal) Jurnal of Syiah Kuala Dentistry Society
Wulandani Liza Putri
Fakultas Kedokteran Gigi, Universitas Andalas
Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Dentistry Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Medicine & Pharmacology Nursing Physics Public Health Veterinary Other

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Effectiveness of online education about actions of 3G motions to maintain children's oral health in the new normality era of the COVD-19 Pandemic Reno Wiska WULANDARI; Didin KUSTANTININGTYASTUTI; Suci RAHMASARI; Wulandani Liza PUTRI; Fildzah Nurul FAJRIN; Riska Ananda PRATIWI; Safira FAJRIATI
Journal of Syiah Kuala Dentistry Society Vol 7, No 1 (2022): Juni 2022
Publisher : Dentistry Faculty

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24815/jds.v7i1.27248

Abstract

The actions of 3G are three motions to maintain oral health: brushing teeth, using mouthwash, andchanging the toothbrush once in 3 months. People have to brush their teeth twice a day in the morning and nightbefore sleeping, using mouthwash, and evolving their toothbrush once in 3 months is the third movement of 3G.These actions can clean teeth from bacteria and viruses. This study aims at analyzing the effectiveness of onlineeducation about the actions of 3G to maintain oral health during the COVID-19 Pandemic. This research is a crosssectionalanalytical study with data collection using a questionnaire conducted online through an electronicquestionnaire form held on June 17, 2021. Respondents in this study were 52 fourth-grade SD Kartika 1-11 Padangstudents selected by purposive sampling technique. The research data were processed with SPSS statistical software.This study reveals education about 3 G increase the knowledge of children about the importance of brushing teeth,using mouthwash, and changing toothbrush once in 3 months, besides the low effectiveness of online educationabout 3 G. This study concludes that online education was not effective to increase respondent's knowledge aboutactions of 3 G to maintain oral health during the Covid 19 pandemic.KEYWORDS: Oral health education, toothbrush, mouthwash, 3G, COVID-19 virus
Penerapan Algoritma Deep Learning YOLOv8 pada Platform Roboflow untuk Segmentasi Citra Panoramik Mahdiyah, Luqyana; Oktamuliani, Sri; Putri, Wulandani Liza
Jurnal Fisika Unand Vol 14 No 3 (2025)
Publisher : Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/jfu.14.3.228-234.2025

Abstract

Dental panoramic image segmentation plays a crucial role in dental diagnosis, as it aids in the identification of dental conditions and other oral structures more quickly and accurately. However, manual segmentation processes are often time-consuming and require specialized expertise. Therefore, Artificial Intelligence (AI)-based technology presents a potential solution to enhance efficiency. This study aims to develop a deep learning algorithm for automatic segmentation of dental panoramic images. The model used was trained with 302 dental panoramic images across 32 classes, encompassing 9009 teeth. The segmentation process was carried out on the Roboflow platform, which provides evaluation metrics to assess the model’s performance. Evaluation results revealed a mean Average Precision (mAP) of 95%, recall of 93.1%, and precision of 93.7%, indicating a high level of accuracy in detecting and segmenting teeth. However, challenges arise in certain image conditions, such as teeth that are reduced to roots or teeth positioned abnormally. Overall, the model demonstrates significant potential to improve the efficiency and accuracy of dental panoramic image analysis. This research contributes significantly to the development of faster and more accurate AI-based dental diagnostic systems .
Perawatan Kasus Maloklusi Skeletal Klas III disertai Gigitan Terbuka Anterior menggunakan Peranti Cekat Sistim Ligasi Pasif Putri, Wulandani Liza
Cakradonya Dental Journal Vol 14, No 2 (2022): Agustus 2022
Publisher : FKG Universitas Syiah Kuala

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24815/cdj.v14i2.22052

Abstract

Peningkatan Kualitas Citra Radiograf Panoramik Menggunakan Metode Filter Laplacian Haq, Haritsul; Oktamuliani, Sri; Putri, Wulandani Liza; Gunawan, Gunawan; Adnan, Surma
Jurnal Fisika Unand Vol 13 No 5 (2024)
Publisher : Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/jfu.13.5.624-629.2024

Abstract

Image quality enhancement of panoramic radiographs using the Laplacian filter method has been carried out. This study aims to improve the quality of panoramic radiography images (Acteon X-Mind Prime 3D) in the radiology installation of RSGM Andalas University. This study used secondary data from 46 panoramic images in .BMP format, which were performed by applying Laplacian filter and evaluating image quality by Structural Similarity Index (SSI), Contrast-to-Noise Ratio (CNR), Contrast Improvement Ratio (CIR) and Spatial Frequency (SF). The results showed that the SSI value was 0.636 ± 0.0732, CNR 5.36 ± 0.412, CIR 0.740 ± 0.124, SF 0.605 ± 0.0983. These results mean that CNR shows good image contrast, SSI shows dissimilarity between the original image and the image after Laplacian filter processing, CIR shows good contrast improvement, while the SF value of 0.749 indicates that the image's ability to display structural details is still lacking. Skewness and Shapiro-Wilk tests indicate that SSI and SF are abnormally distributed, while CIR and CNR are normally distributed. T-test analysis was performed on the CIR and CNR metrics data, which showed that the significance of this study indicated that the Laplacian filter method was significant p < 0.001. Thus, the use of the Laplacian filter method significantly improves the image quality of panoramic radiographs.
EDUKASI MANFAAT NUTRISI BAGI TUMBUH KEMBANG GIGI DAN MULUT ANAK USIA PRA SEKOLAH PADA MURID TK ADZKIA III DI KOTA PADANG Gita Dwi Jiwanda Sovira; Wulandani Liza Putri; Nila Kasuma
BULETIN ILMIAH NAGARI MEMBANGUN Vol. 7 No. 1 (2024)
Publisher : LPPM (Institute for Research and Community Services) Universitas Andalas Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/bina.v7i1.515

Abstract

Children's dental health is very important to ensure the child's nutritional status, speaking ability and attractive appearance. Nutrition, diet and dental health in children are closely related, especially in children who are in the growth and development phase. Good quality and proper nutrition is very important to support dental health. Likewise, dental health is also important for adequate nutritional intake. If the teeth and mouth are healthy, then nutritional intake will be optimal, and if nutrition is optimal then the growth and development of the teeth and mouth will also be optimal. As many as 89% of children in Indonesia suffer from dental disease. A number of surveys show that more than a third of the toddler population has experienced toothache and one sixth of them have had at least one tooth extracted. The growth and development of teeth is influenced by nutrition. Healthy dentition will occur if all nutritional elements are available in sufficient quantities. The aim of the activity: to provide education to young children about the importance of nutrition for the growth and development of teeth and mouth. The activity method used is the tell-show-do method with presentations using power point media, posters, models of teeth and toothbrushes, as well as examination of children's teeth and mouths. This method is one of the most appropriate methods for providing education to early childhood. Results of the activity: increased knowledge of children and the emergence of a sense of courage when having a dental examination by a dentist. Conclusion: This activity has a positive impact on young children because children understand better that there is a connection between nutrition and oral health.
Dental and Craniofacial Anatomical Variations and Their Impact on Orthodontic Diagnosis and Treatment Planning: A Systematic Review Gita Dwi Jiwanda Sovira; Wulandani Liza Putri; Yona Ladyventini
Jurnal EduHealth Vol. 16 No. 04 (2025): Jurnal EduHealt, Edition October-December , 2025
Publisher : Sean Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Background: Dental and craniofacial anatomical variations are fundamental determinants of orthodontic diagnosis and treatment planning. Variability in tooth morphology, skeletal relationships, and alveolar bone anatomy may influence biomechanical feasibility, treatment selection, and the risk of complications. Objective: This systematic review aimed to synthesize current evidence on dental and craniofacial anatomical variations and to evaluate their impact on orthodontic diagnosis and treatment planning. Methods:A systematic literature search was conducted in PubMed, Scopus, and Web of Science in accordance with the PRISMA 2020 guidelines. Studies published in English within the last 10 years and involving human subjects with permanent dentition were considered. Data extraction and study selection were performed independently, and findings were synthesized narratively due to methodological heterogeneity. Results: Twenty studies were included in the qualitative synthesis. Dental anatomical variations, particularly root morphology and tooth anomalies, were associated with biomechanical limitations and increased risk of root resorption. Craniofacial skeletal variations influenced malocclusion patterns, growth assessment, and decisions between orthodontic camouflage and combined orthodontic surgical treatment. Alveolar bone anatomical variability defined the biological limits of orthodontic tooth movement and was closely linked to periodontal risk. 3D imaging techniques, especially cone-beam computed tomography, enhanced the identification of clinically relevant anatomical constraints. Conclusions: Dental, craniofacial, and alveolar anatomical variations play a critical role in orthodontic diagnosis and treatment planning. An anatomy driven approach supported by appropriate imaging improves diagnostic accuracy, facilitates individualized treatment strategies, and reduces the risk of adverse outcomes. These findings support the integration of comprehensive anatomical assessment into precision orthodontic practice.
Deteksi Landmark pada Citra Sefalogram Lateral Menggunakan YOLOv11 Ica Dewi Monica; Sri Oktamuliani; Wulandani Liza Putri
Jurnal Fisika Unand Vol 15 No 4 (2026)
Publisher : Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/jfu.15.4.382-388.2026

Abstract

Landmark identification on lateral cephalogram images plays a crucial role in orthodontic diagnosis and treatment planning because it forms the basis for analyzing the skeletal relationship between facial structures and teeth in the oral cavity. However, manual landmark identification is time-consuming and potentially leads to subjective errors. Therefore, Artificial Intelligence (AI)-based technology is a potential solution to improve the efficiency and consistency of analysis. This study aims to develop an automatic anatomical landmark detection system on lateral cephalogram images using the YOLOv11 algorithm. The dataset used consisted of 50 lateral cephalogram images obtained from the Radiology Installation of RSGM Andalas University and annotated according to the American Board of Orthodontics (ABO) standards. Then, augmentation was performed to obtain a total of 110 images. The model training and testing process was carried out using the YOLOv11 variant “yolo11s-pose”. The evaluation results showed an accuracy, precision, recall, and F-score of 1.0, with a mean Average Precision (mAP) of 0.995. Overall, this model shows good potential in improving the efficiency of cephalogram landmark identification, but it requires increasing the amount and variety of data for more reliable performance in clinical applications.
YOLO-based deep learning for tooth detection, segmentation, and numbering in panoramic radiographs Sri Oktamuliani; Luqyana Mahdiyah; Haritsul Haq; Nesa Perdana Putri; Wulandani Liza Putri
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 4: August 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i4.pp3591-3602

Abstract

Panoramic dental radiographs are essential for diagnosis and treatment planning, but are difficult to interpret due to overlapping anatomical structures and limited image quality. Automating tooth detection, segmentation, and numbering can enhance clinical efficiency and reduce observer variability. This study developed a you only look once version 8 (YOLOv8)-based deep learning model for automatic detection and segmentation of teeth in panoramic radiographs. A dataset of 302 images containing 9,009 annotated teeth was divided into 70% training, 20% validation, and 10% testing sets. Teeth were labeled using the Federation Dentaire Internationale (FDI) two-digit numbering system with bounding boxes and segmentation masks. Model performance was assessed using precision, recall, F1-score, and mean average precision (mAP) at intersection over union (IoU) thresholds of 0.5 and 0.5–0.95. The model achieved bounding box precision, recall, and F1-score of 0.9075, 0.9352, and 0.9212, respectively, and segmentation scores of 0.9078, 0.9344, and 0.9209. Bounding box mAP@0.5 reached 0.9510 and mask mAP@0.5 was 0.9504, while stricter thresholds lowered performance to 0.7763 and 0.6896. Class-wise analysis showed high accuracy overall but reduced performance on posterior teeth due to anatomical overlap and peripheral image quality. YOLOv8 enables fast, accurate, and robust tooth detection and segmentation, supporting real-time computer-aided diagnosis in orthodontics, prosthodontics, and forensic dentistry.
Co-Authors Aninda Putri Mulyani Aulia, Rahmi Khairani Dedi Sumantri Didin Kustantiningtyastuti DWI SURYANTO Dyana Putri Fildzah Nurul Fajrin, Fildzah Nurul Gita Dwi Jiwanda Sovira Gita Dwi Jiwanda Sovira Gunawan Gunawan Gunawan Gunawan Haq, Haritsul Haria Fitri Haritsul Haq Haritsul Haq Haru Setyo Anggani Ica Dewi Monica Krisnawati Krisnawati Krisnawati, Krisnawati Luqyana Mahdiyah M. Iqbal Mahdiyah, Luqyana Nesa Perdana Putri Nila Kasuma Puji Kurnia Reno Wiska Wulandari Riska Ananda PRATIWI Safira FAJRIATI Sri Oktamuliani Sri Ramayanti Suci Rahmasari Yona Ladyventini Yudi Setiawan