Age Identification System with Panoramic Image Processing Digital Molar Dental Radiograph with Adaptive Region Growing Approach Method DOI Open Access
Hilman Fauzi,

Fajri Tsani,

Fahmi Oscandar

et al.

Journal of Measurements Electronics Communications and Systems, Journal Year: 2023, Volume and Issue: 10(2), P. 44 - 44

Published: Dec. 31, 2023

Forensics plays a crucial role in legal enforcement, particularly cases where objects or human victims undergoing forensic identification have suffered significant damage. Teeth offer robust solution the process due to their resilience various circumstances. Forensic odontology focuses on dental for judicial purposes. One parameter is age estimation. Generally, an individual's development directly related age, which can be observed through pulp. The pulp tends narrow widen with increasing age. In this study, image processing system using Adaptive Region Growing Approach (ARGA) method was developed molar radiograph images. Subsequently, images were classified Support Vector Machine (SVM) method. research encompassed data collection, processing, feature extraction, and size classification. results demonstrated accuracy of over 80% system, specific parameters such as adjustment threshold OTSU 1.15, clip limit histogram Equalization 0.1, polynomial kernel type, one against coding type classification into four classes. This study concludes that effectively implemented estimation panoramic has potential applications odontology, supporting victim enforcement.

Language: Английский

Deep Learning for Age Estimation from Panoramic Radiographs: A Systematic Review and Meta-Analysis DOI
Rata Rokhshad, Fatemeh Nasiri,

Naghme Saberi

et al.

Journal of Dentistry, Journal Year: 2025, Volume and Issue: unknown, P. 105560 - 105560

Published: Jan. 1, 2025

Language: Английский

Citations

4
Automated Age Estimation from OPG Images and Patient Records Using Deep Feature Extraction and Modified Genetic–Random Forest DOI Creative Commons
Gülfem ÖZLÜ UÇAN,

Omar Abboosh Hussein Gwassi,

Burak Kerem APAYDIN

et al.

Diagnostics, Journal Year: 2025, Volume and Issue: 15(3), P. 314 - 314

Published: Jan. 29, 2025

Background/Objectives: Dental age estimation is a vital component of forensic science, helping to determine the identity and actual an individual. However, its effectiveness challenged by methodological variability biological differences between individuals. Therefore, overcome drawbacks such as dependence on manual measurements, requiring lot time effort, difficulty routine clinical application due large sample sizes, we aimed automatically estimate tooth from panoramic radiographs (OPGs) using artificial intelligence (AI) algorithms. Methods: Two-Dimensional Deep Convolutional Neural Network (2D-DCNN) One-Dimensional (1D-DCNN) techniques were used extract features patient records. To perform feature information, Genetic algorithm (GA) Random Forest (RF) modified, combined, defined Modified Genetic–Random Algorithm (MG-RF). The performance system in our study was analyzed based MSE, MAE, RMSE, R2 values calculated during implementation code. Results: As result applied algorithms, MSE value 0.00027, MAE 0.0079, RMSE 0.0888, score 0.999. Conclusions: findings indicate that AI-based employed herein effective tool for detection. Consequently, propose this technology could be utilized sciences future.

Language: Английский

Citations

1
Performance of Artificial Intelligence Models Designed for Automated Estimation of Age Using Dento-Maxillofacial Radiographs—A Systematic Review DOI Creative Commons
Sanjeev B. Khanagar, Farraj Albalawi, Aram Alshehri

et al.

Diagnostics, Journal Year: 2024, Volume and Issue: 14(11), P. 1079 - 1079

Published: May 22, 2024

Automatic age estimation has garnered significant interest among researchers because of its potential practical uses. The current systematic review was undertaken to critically appraise developments and performance AI models designed for automated using dento-maxillofacial radiographic images. In order ensure consistency in their approach, the followed diagnostic test accuracy guidelines outlined PRISMA-DTA this review. They conducted an electronic search across various databases such as PubMed, Scopus, Embase, Cochrane, Web Science, Google Scholar, Saudi Digital Library identify relevant articles published between years 2000 2024. A total 26 that satisfied inclusion criteria were subjected a risk bias assessment QUADAS-2, which revealed flawless both arms patient-selection domain. Additionally, certainty evidence evaluated GRADE approach. technology primarily been utilized through tooth development stages, bone parameters, measurements, pulp–tooth ratio. employed studies achieved remarkably high precision 99.05% 99.98% stages respectively. application additional tool within realm demonstrates promise.

Language: Английский

Citations

6
Dental age estimation using a convolutional neural network algorithm on panoramic radiographs: A pilot study in Indonesia DOI Creative Commons
Arofi Kurniawan,

Michael Saelung,

Beta Novia Rizky

et al.

Imaging Science in Dentistry, Journal Year: 2025, Volume and Issue: 55

Published: Jan. 1, 2025

This study employed a convolutional neural network (CNN) algorithm to develop an automated dental age estimation method based on the London Atlas of Tooth Development and Eruption. The primary objectives were create validate CNN models trained panoramic radiographs achieve accurate predictions using standardized approach. A dataset 801 from outpatients aged 5 15 years was used. model for developed 16-layer architecture implemented in Python with TensorFlow Scikit-learn, guided by Development. included 6 layers feature extraction, each followed pooling layer reduce spatial dimensions maps. confusion matrix used evaluate key performance metrics, including accuracy, precision, recall, F1 score. proposed achieved overall score 74% validation set. highest scores observed 10-year 12-year groups, indicating superior these categories. In contrast, 6-year group demonstrated misclassification rate, highlighting potential challenges accurately estimating younger individuals. Integrating represents significant advancement forensic odontology. application AI improves both precision efficiency processes, providing results that are more reliable objective than those obtained via traditional methods.

Language: Английский

Citations

0
Integrating artificial intelligence and adult dental age estimation in forensic identification: A literature review DOI Creative Commons
Arofi Kurniawan,

Aisyah Novianti,

Feby Ayu Lestari

et al.

World Journal of Advanced Research and Reviews, Journal Year: 2024, Volume and Issue: 21(2), P. 1374 - 1379

Published: Feb. 26, 2024

Age estimation is crucial in various forensic fields, including medicine, anthropology, and demographic studies. Adult dental age affected by multiple factors, resulting discrepancies between chronological age. The development of artificial intelligence (AI) technology has led to extensive investigations sciences, encompassing several areas such as facial recognition, age, sex identification, DNA analysis. methods commonly used include the pulp-tooth ratio approach, Harris & Nortje method, Van Heerden method. AI approaches Fuzzy Logic (FL), Evolutionary Computing (EC), Machine Learning (ML) are being extensively applied. These techniques use algorithms imitate human thinking behavior. Deep learning techniques, explicitly using deep convolutional neural networks (DCNN), enable segmenting images making measurements, replicating cognitive processes radiologists when computing indices third molar maturity (I3M) index. Also, DCNNs automatically optimize teeth segmentation X-ray images, improving image refining analysis efficiency. integration dentistry improves precision effectiveness data processing while significantly accelerating individual identification procedures. Incorporating this shows potential for enhancing caliber dependability evidence investigations.

Language: Английский

Citations

1
Enhanced multistage deep learning for diagnosing anterior disc displacement in temporomandibular joint using magnetic resonance imaging DOI
Chang‐Ki Min, Won Seok Jung, Subin Joo

et al.

Dentomaxillofacial Radiology, Journal Year: 2024, Volume and Issue: 53(7), P. 488 - 496

Published: July 18, 2024

This study aimed to propose a new method for the automatic diagnosis of anterior disc displacement temporomandibular joint (TMJ) using MRI and deep learning. By multistage approach, factors affecting final result can be easily identified improved.

Language: Английский

Citations

1
A Systematic Review: The Utilization of Artificial Intelligence in Forensic Odontology DOI
Muhamad Rodhi Supriyadi, Azurah A. Samah,

Hairudin Abdul Majid

et al.

Published: Oct. 9, 2024

Language: Английский

Citations

1
Age Identification System with Panoramic Image Processing Digital Molar Dental Radiograph with Adaptive Region Growing Approach Method DOI Open Access
Hilman Fauzi,

Fajri Tsani,

Fahmi Oscandar

et al.

Journal of Measurements Electronics Communications and Systems, Journal Year: 2023, Volume and Issue: 10(2), P. 44 - 44

Published: Dec. 31, 2023

Forensics plays a crucial role in legal enforcement, particularly cases where objects or human victims undergoing forensic identification have suffered significant damage. Teeth offer robust solution the process due to their resilience various circumstances. Forensic odontology focuses on dental for judicial purposes. One parameter is age estimation. Generally, an individual's development directly related age, which can be observed through pulp. The pulp tends narrow widen with increasing age. In this study, image processing system using Adaptive Region Growing Approach (ARGA) method was developed molar radiograph images. Subsequently, images were classified Support Vector Machine (SVM) method. research encompassed data collection, processing, feature extraction, and size classification. results demonstrated accuracy of over 80% system, specific parameters such as adjustment threshold OTSU 1.15, clip limit histogram Equalization 0.1, polynomial kernel type, one against coding type classification into four classes. This study concludes that effectively implemented estimation panoramic has potential applications odontology, supporting victim enforcement.

Language: Английский

Citations

0