Digitally Scanned Radiographs versus Conventional Films for Determining Clarity of Periapical Lesions and Quality of Root Canal Treatment

Al-Manei, Kholod Khalil; Alsulaimani, Rakan Siraj; Alfadda, Sarah; Albabtain, Sarah; Alsulaimani, Reem Siraj

doi:10.1155/2017/2427060

Cited by 4 publications

(2 citation statements)

References 14 publications

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“…Images that have been converted to digital format may be archived and shared more easily. Digital pictures have the potential to improve dental diagnosis, treatment planning, and follow-up procedures because to the technical opportunities provided by digital software 25 .…”

Section: Diagnostic Utilitymentioning

confidence: 99%

Digital Subtraction Radiography in Dentistry: An Overview

R. Sonar,

S. Panchbhai

2023

Int J Life Sci Pharm Res

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Digital imaging has transformed the field of medical and dental imaging by overcoming many of the drawbacks ofconventional film-based radiographs. These alterations, however, are hidden behind a background of typical anatomical featuresand are not readily visible, making it difficult for the human eye to discern them. One image-enhancing method or tool that solvesthis issue is digital subtraction radiography, which has recently found usage in a number of dental specialties. Quantitative measuresfrom digital subtraction pictures have been validated as a result of the development of digital imaging technology. Moreover,changes made to computer software have made the clinical use of subtraction imaging more adaptable. Digital subtraction radiologyhas significantly improved the ability to find oral and maxillofacial lesions. Comparing standardised radiographs acquired duringsuccessive examination sessions is done using digital subtraction radiography. Any structures that have not changed are removed,and these regions are shown in the subtraction picture as neutral grey areas, whilst parts that have altered are shown as deeperor brighter shades of grey. Yet, there is not much analysis of this often used method in the literature. The purpose of this articleis to examine this technology, its applications in different areas of dentistry, and its potential for detecting a range of disorders.This review article highlights its basic principles, applications, technique, advantages, disadvantages and future scope of digitalsubtraction radiography in dentistry.

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Section: Diagnostic Utilitymentioning

confidence: 99%

Digital Subtraction Radiography in Dentistry: An Overview

R. Sonar,

S. Panchbhai

2023

Int J Life Sci Pharm Res

View full text Add to dashboard Cite

show abstract

“…This process involves acquiring images digitally which are then manipulated using a computer. There are several ways of obtaining such images, including intraoral sensors, charge-coupled devices, and scanning of radiographs [ 6 ]. Due to the emergence and spread of low-cost connectivity in underdeveloped and developing countries, the Internet of Things (IoT) has enabled dental services to be more broadly leveraged, providing better dental health in developing countries and decreasing the overall demand for dental care.…”

Section: Introductionmentioning

confidence: 99%

Teeth Lesion Detection Using Deep Learning and the Internet of Things Post-COVID-19

Shafi,

Sajad,

Fatima

et al. 2023

Sensors

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With a view of the post-COVID-19 world and probable future pandemics, this paper presents an Internet of Things (IoT)-based automated healthcare diagnosis model that employs a mixed approach using data augmentation, transfer learning, and deep learning techniques and does not require physical interaction between the patient and physician. Through a user-friendly graphic user interface and availability of suitable computing power on smart devices, the embedded artificial intelligence allows the proposed model to be effectively used by a layperson without the need for a dental expert by indicating any issues with the teeth and subsequent treatment options. The proposed method involves multiple processes, including data acquisition using IoT devices, data preprocessing, deep learning-based feature extraction, and classification through an unsupervised neural network. The dataset contains multiple periapical X-rays of five different types of lesions obtained through an IoT device mounted within the mouth guard. A pretrained AlexNet, a fast GPU implementation of a convolutional neural network (CNN), is fine-tuned using data augmentation and transfer learning and employed to extract the suitable feature set. The data augmentation avoids overtraining, whereas accuracy is improved by transfer learning. Later, support vector machine (SVM) and the K-nearest neighbors (KNN) classifiers are trained for lesion classification. It was found that the proposed automated model based on the AlexNet extraction mechanism followed by the SVM classifier achieved an accuracy of 98%, showing the effectiveness of the presented approach.

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RETRACTED ARTICLE: Periapical dental X-ray image classification using deep neural networks

et al. 2022

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This paper studies the problem of detection of dental diseases. Dental problems affect the vast majority of the world's population. Caries, RCT (Root Canal Treatment), Abscess, Bone Loss, and missing teeth are some of the most common dental conditions that affect people of all ages all over the world. Delayed or incorrect diagnosis may result in mistreatment, affecting not only an individual's oral health but also his or her overall health, thereby making it an important research area in medicine and engineering. We propose a pipelined Deep Neural Network (DNN) approach to detect healthy and non-healthy periapical dental X-ray images. Even a minor enhancement or improvement in existing techniques can go a long way in providing significant health benefits in the medical field. This paper has made a successful attempt to contribute a different type of pipelined approach using AlexNet in this regard. The approach is trained on a large dataset of 16,000 dental X-ray images, correctly identifying healthy and non-healthy X-ray images. We use an optimized Convolutional Neural Networks and three state-of-the-art DNN models, namely Res-Net-18, ResNet-34, and AlexNet for disease classification. In our study, the AlexNet model outperforms the other models with an accuracy of 0.852. The precision, recall and F1 scores of AlexNet also surpass the other models with a score of 0.850 across all metrics. The area under ROC curve also signifies that both the false-positive rate and false-negative rate are low. We conclude that even with a big data set and raw X-ray pictures, the AlexNet model generalizes effectively to previously unseen data and can aid in the diagnosis of a variety of dental diseases.

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Digitally Scanned Radiographs versus Conventional Films for Determining Clarity of Periapical Lesions and Quality of Root Canal Treatment

Cited by 4 publications

References 14 publications

Digital Subtraction Radiography in Dentistry: An Overview

Digital Subtraction Radiography in Dentistry: An Overview

Teeth Lesion Detection Using Deep Learning and the Internet of Things Post-COVID-19

RETRACTED ARTICLE: Periapical dental X-ray image classification using deep neural networks

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