Deep Learning for Age Estimation from Panoramic Radiographs: A Systematic Review and Meta-Analysis

Journal of Dentistry, Год журнала: 2025, Номер unknown, С. 105560 - 105560

Опубликована: Янв. 1, 2025

Язык: Английский

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Zero-Day Exploits in Cybersecurity: Case Studies and Countermeasure

Опубликована: Июль 29, 2024

Zero-day threats are a more severe and constantly developing menace to various participants including large companies, government offices, educational establishments. These entities may contain valuable information essential operations that attract cyber attackers. exploits especially devastating as they target weaknesses an organization’s vendors not even aware of, making them have no protection against them. This paper focuses on the background use of zero-day exploitation structure technologies these complex malware attacks. We examine two notable real-life cases: case ‘HAFNIUM targeting Exchange Servers with exploits’ was investigated by Microsoft 365 Security Threat Intelligence, ‘Log4j vulnerability’ reported National Cyber Centre. cases show critical effects vulnerabilities measures taken combat Additionally, this outlines different strategies can be used prevent attacks help modern technologies. fast patch release, effective IDS/IPS, security model involves constant vigilance behavioral analytics. Thus, studying lifecycle exploits, one enhance organization invisible traditional systems. extensive survey is designed useful in understanding characteristics vulnerabilities, for their mitigation, threat development field cybersecurity. it possible strengthen develop time analyzing previous events predicting potential problems.

Язык: Английский

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Performance of Artificial Intelligence Models Designed for Automated Estimation of Age Using Dento-Maxillofacial Radiographs—A Systematic Review

Diagnostics, Год журнала: 2024, Номер 14(11), С. 1079 - 1079

Опубликована: Май 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.

Язык: Английский

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A population-based study to assess two convolutional neural networks for dental age estimation

BMC Oral Health, Год журнала: 2023, Номер 23(1)

Опубликована: Фев. 17, 2023

Abstract Background Dental age (DA) estimation using two convolutional neural networks (CNNs), VGG16 and ResNet101, remains unexplored. In this study, we aimed to investigate the possibility of artificial intelligence-based methods in an eastern Chinese population. Methods A total 9586 orthopantomograms (OPGs) (4054 boys 5532 girls) Han population aged from 6 20 years were collected. DAs automatically calculated CNN model strategies. Accuracy, recall, precision, F1 score models used evaluate ResNet101 for estimation. An threshold was also employed models. Results The network outperformed terms prediction performance. However, effect less favorable than that other ranges 15–17 group. results younger groups acceptable. 6-to 8-year-old group, accuracy can reach up 93.63%, which higher 88.73% network. implies has a smaller age-difference error. Conclusions This study demonstrated performed better when dealing with DA via OPGs on wholescale. CNNs such as hold great promise future use clinical practice forensic sciences.

Язык: Английский

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Efficacy of the methods of age determination using artificial intelligence in panoramic radiographs — a systematic review

International Journal of Legal Medicine, Год журнала: 2024, Номер 138(4), С. 1459 - 1496

Опубликована: Фев. 24, 2024

Язык: Английский

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Artificial intelligence in age and sex determination using maxillofacial radiographs: A systematic review.

PubMed, Год журнала: 2024, Номер 42(1), С. 30 - 37

Опубликована: Апрель 30, 2024

In the past few years, there has been an enormous increase in application of artificial intelligence and its adoption multiple fields, including healthcare. Forensic medicine forensic odontology have tremendous scope for development using AI. cases severe burns, complete loss tissue, or partial bony structure, decayed bodies, mass disaster victim identification, etc., is a need prompt identification remains. The mandible, strongest bone facial region, highly resistant to undue mechanical, chemical physical impacts widely used many studies determine age sexual dimorphism. Radiographic estimation jaw sex more workable since it simple can be applied equally both dead living aid process. Hence, this systematic review focused on various AI tools determination maxillofacial radiographs. data was obtained through searching articles across search engines, published from January 2013 March 2023. QUADAS 2 qualitative synthesis, followed by Cochrane diagnostic test accuracy risk bias analysis included studies. results are optimistic. precision comparable those human examiner. These models, when designed with right kind data, use medico legal scenarios identification.

Язык: Английский

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Künstliche Intelligenz in der forensisch-radiologischen Altersdiagnostik

Rechtsmedizin, Год журнала: 2025, Номер unknown

Опубликована: Фев. 27, 2025

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Dental age estimation using a convolutional neural network algorithm on panoramic radiographs: A pilot study in Indonesia

Imaging Science in Dentistry, Год журнала: 2025, Номер 55

Опубликована: Янв. 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.

Язык: Английский

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Chemical Imaging for Biological Systems: Techniques, AI-Driven Processing, and Applications

Journal of Materials Chemistry B, Год журнала: 2025, Номер unknown

Опубликована: Янв. 1, 2025

Visualization of chemical compositions in biological samples has advanced through technologies such as single-molecule imaging, mass spectrometry, photoacoustic, Raman, and electrochemical microscopies, probes, AI-assisted processing.

Язык: Английский

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Application of entire dental panorama image data in artificial intelligence model for age estimation

BMC Oral Health, Год журнала: 2023, Номер 23(1)

Опубликована: Дек. 15, 2023

Abstract Background Accurate age estimation is vital for clinical and forensic purposes. With the rapid advancement of artificial intelligence(AI) technologies, traditional methods relying on tooth development, while reliable, can be enhanced by leveraging deep learning, particularly neural networks. This study evaluated efficiency an AI model applying entire panoramic image estimation. The outcome performances were analyzed through supervised learning (SL) models. Methods Total 27,877 dental panorama images from 5 to 90 years classified 2 types grouping. In type 1 they each in 2, heuristic grouping, over 20 every years. Wide ResNet (WRN) DenseNet (DN) used learning. addition, analysis with ± 3 deviation both performed. Results For DN model, grouping achieved accuracy 0.1016 F1 score 0.058, 0.3146 0.2027. Incorporating 3years deviation, 0.281, 0.7323 respectively; 0.1768, 0.6583 respectively. WRN 0.1041 0.0599, 0.3182 0.2071. 0.2716, 0.1709, 0.6437 Conclusions application data classification heuristics models demonstrated satisfactory

Язык: Английский

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