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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Jurnal Penelitian Fisika dan Aplikasinya (JPFA) Jurnal Sains dan Teknologi Nuklir Indonesia (Indonesian Journal of Nuclear Science and Technology) Jurnal Sains dan Teknologi Jurnal Fisika Unand SEMIRATA 2015 JURNAL ILMIAH PENDIDIKAN FISIKA AL BIRUNI JOURNAL ONLINE OF PHYSICS INDONESIAN JOURNAL OF APPLIED PHYSICS Jurdimas (Jurnal Pengabdian Kepada Masyarakat) Royal Jurnal Ilmu Fisika BERNAS: Jurnal Pengabdian Kepada Masyarakat Jurnal Pengabdian kepada Masyarakat Nusantara Warta Pengabdian Andalas: Jurnal Ilmiah Pengembangan Dan Penerapan Ipteks Indonesian Journal of Community Empowerment and Service
Sri Oktamuliani
Departemen Fisika, Fakultas Matematika Dan Ilmu Pengetahuan Alam, Universitas Andalas, Kampus Unand Limau Manis, Padang, 25163
Religion Agriculture, Biological Sciences & Forestry Humanities Astronomy Biochemistry, Genetics & Molecular Biology Computer Science & IT Decision Sciences, Operations Research & Management Dentistry Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Languange, Linguistic, Communication & Media Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Neuroscience Nursing Physics Public Health Social Sciences Veterinary Other

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Peningkatan Akurasi Interpretasi Aliran Darah pada Citra Color Doppler Echocardiography (CDE) dengan Metode De-Aliasing Shindy Yohanda; Sri Oktamuliani
Jurnal Fisika Unand Vol 14 No 6 (2025)
Publisher : Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/jfu.14.6.644-652.2025

Abstract

Color Doppler Echocardiography (CDE) is an echocardiographic imaging technique that utilizes the Doppler effect principle to produce images of the heart. However, CDE images often contain aliasing artifacts that can hinder the interpretation of blood flow. This study aims to eliminate aliasing in CDE images of the apical 4-chamber view through the application of a de-aliasing method using MATLAB R2016b, and to evaluate the performance of this method in improving the accuracy of blood flow interpretation across all cardiac structures. The de-aliasing method corrects folded velocity values by extending the Nyquist velocity range. The results show that the maximum velocity value increased after the de-aliasing process, indicating that the Nyquist velocity range was successfully expanded. Furthermore, the method effectively reconstructed the folded velocity values back into a valid range. Visual errors in the form of color inversion were corrected by adjusting the color scheme of the image. Evaluation of the de-aliasing method’s performance demonstrated an improvement in the accuracy of blood flow interpretation throughout the cardiac structures.
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.
Characterization of Left Ventricle Main Flow Axis Line Using Echodynamography Sri Oktamuliani; Kaoru Hasegawa; Tadanori Minagawa; Yoshifumi Saijo
INDONESIAN JOURNAL OF APPLIED PHYSICS Vol 11, No 2 (2021): October
Publisher : Department of Physics, Sebelas Maret University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.13057/ijap.v11i2.51896

Abstract

Left ventricular (LV) blood flow analysis may play an essential role in evaluating cardiac function besides the classical analysis of wall motion. Echodynamography is an imaging method in which two-dimensional (2D) blood flow vectors are deduced by blood flow information obtained by color Doppler echocardiography. Echodynamography has provided useful information on the blood flow pattern in healthy and abnormal LV. The main flow axis line (MFAL) is defined as a maximum velocity magnitude of blood flow from the LV's apex to LV's outflow, which is a new hemodynamic parameter for cardiac assessment. The present study's objective is to compare blood flow patterns between healthy and abnormal LV by investigating the MFAL and its correlation to vorticity and velocity distribution on MFAL. This study enrolled 12 participants, four healthy volunteers, and eight abnormal patients. Echodynamography analyzed frame by frame Doppler image of apical three-chamber views. The results showed MFAL superimposed on vorticity mapping during ventricular ejection and MFAL path coincide with the irrotational flow of zero vorticity path, ω = 0. A significant difference was observed in the velocity distribution curve (VDC) on the MFAL during early, mid, and late systoles compared to healthy and abnormal LV. VDC showed the linear upward curve and the highest velocity magnitude during the early systole phase in healthy LV. In contrast with abnormal LV, VDC showed the downward convex curve and the highest velocity magnitude during mid systole phase. Furthermore, the gradient and slope angle of the VDC on the MFAL was compared. The result showed that the maximum gradient and slope angle were not significantly different between healthy and abnormal LV. In conclusion, the study of MFAL and the correlation to vorticity based on the Echodynamography computational program provides additional insights for representing a cardiac function, and thus, the clinical implications of MFAL warrant further investigation.
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 Adrial, Rico Afdal Afdal Afdal Afdal Afdal Afdal, Afdal Afdhal Muttaqin Afifah Nabilah Ahmad Fauzi Pohan Alief, Iqbal Alimin Mahyudin Arif Budiman Astuti Astuti Astuti Astuti Aulia Firma Betta Centaury Caredek, Puspa Tirta Dahyunir Dahlan Damayanti, Elok Dedi Mardiansyah Dian Fitriyani Dian Milvita Dinda Nurul Syifa Dinda Nurul Syifa Dwi Pujiastuti Dwi Pujiastuti Dwi Puryanti Eli Defira Elistia Liza Namigo, Elistia Elvaswer Elvaswer Faza Atika An'umillah Faza Atika An’umillah Feriska Handayani Irka, Feriska Handayani Fiarka, Fulki Fitra Ramadana Fulki Fiarka Gunawan Gunawan Gunawan Gunawan Haq, Haritsul Haritsul Haq Haritsul Haq Harmadi Harmadi Hasnel Sofyan Ica Dewi Monica Imam Taufiq Imam Taufiq Intan Apriliani Syaridatul Mu'minah Iqbal Alief Iqbal Ramadhan Iqbal Ramadhan Ishii, Takuro Kaoru Hasegawa Kusdiana Kusdiana Kusdiana Kusdiana Kusdiana Kusdiana Kusdiana Kusdiana, Kusdiana Lazuardi Umar Leli Nirwani Leony Chantika Leony Luqyana Mahdiyah M. Dlorifun Naqiyyun Mahdiyah, Luqyana Marzuki Marzuki Marzuki Marzuki Meqorry Yusfi Mohammad Ali Shafii Mohammad Randy Alhafiz Muhammad Arif Muhammad Arif Muhammad Ilyas Muhammad Kahfi Muhammad Kahfi Muji Wiyono Muldarisnur, Mulda Mutya Vonnisa Nabilah, Afifah Naela Amalia Zulfa Naela Amalia Zulfa Nazri MZ Nazri MZ, Nazri Nehru Nesa Perdana Putri Nini Firmawati Nirwani, Leli Nunung Nuraeni Nunung Nuraeni, Nunung Nurul Hasanah Nurul Khaira Sabila Pratama, Andra Puspa Tirta Caredek Rahmat Rasyid Rahmat Rasyid Ramacos Fardela Rinnesa Apria Ernando Saijo, Yoshifumi Salim Muhaimin Samsidar Samsidar Shindy Yohanda Siyami, Rizka Mutik sparzinanda, eif Sri Handani Syifa, Dinda Nurul Tadanori Minagawa Trengginas Eka Putra Sutantyo Tsany Najmah Aziz Yenuuar Usna, Sri Rahayu Alfitri Vanessa Illona Giovanni Veithzal Rivai Zainal Viesca Fredilla Hanif Wahyudi Wahyudi Wahyudi Wahyudi Wildian Wildian Wiyono, Muji Wulandani Liza Putri Yoshifumi Saijo Yuliandari, Annisa Yusfi, Meqqory Zulfi Zulfi Zulfi Zulfi Zulfi Zulfi, Zulfi