Automatic diagnosis and detection of dental caries in bitewing radiographs using pervasive deep gradient based LeNet classifier model

Vimalarani, G.; Ramachandraiah, Uppu

doi:10.1016/j.micpro.2022.104654

Cited by 10 publications

(7 citation statements)

References 14 publications

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“…While the ACC metric was measured in nine studies, it was never used in thirtytwo. In a study using Faster R-CNN to diagnose gingivitis from intraoral photographs, the highest ACC value of 100% was obtained [26]. The lowest ACC value of 69% was obtained in a study using ResNet18 to diagnose dental caries on NILT images [67].…”

Section: Resultsmentioning

confidence: 99%

“…In a study [27], a parallel 1D CNN was used as a YOLOv5 classifier as an image preprocessing method. To optimize the weights, methods that combine CNN with different optimization algorithms, such as antlion [83] and pervasive deep gradient [26], have also been proposed. In sixty studies, the ACC metric was used as the primary performance measurement method.…”

Section: Resultsmentioning

confidence: 99%

“…Examples of dental anomalies and diseases on dental imaging techniques; a. Mesiodens on panoramic radiographs [21], b. Apical lesions on periapical radiographs [22], c. Temporomandibular joint osteoarthritis on orthopantomograms [23], d. Missing tooth on cone beam computed tomography [24], e. Dental caries on near-infrared-light transillumination [25], f. Dental caries on bite viewing radiographs [26], g. Dental calculus and inflammation on optical color images [27], h. Gingivitis on intraoral photos [28]. (B).…”

Section: Figure 1 (A)mentioning

confidence: 99%

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Deep Learning in Diagnosis of Dental Anomalies and Diseases: A Systematic Review

et al. 2023

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Deep learning and diagnostic applications in oral and dental health have received significant attention recently. In this review, studies applying deep learning to diagnose anomalies and diseases in dental image material were systematically compiled, and their datasets, methodologies, test processes, explainable artificial intelligence methods, and findings were analyzed. Tests and results in studies involving human-artificial intelligence comparisons are discussed in detail to draw attention to the clinical importance of deep learning. In addition, the review critically evaluates the literature to guide and further develop future studies in this field. An extensive literature search was conducted for the 2019–May 2023 range using the Medline (PubMed) and Google Scholar databases to identify eligible articles, and 101 studies were shortlisted, including applications for diagnosing dental anomalies (n = 22) and diseases (n = 79) using deep learning for classification, object detection, and segmentation tasks. According to the results, the most commonly used task type was classification (n = 51), the most commonly used dental image material was panoramic radiographs (n = 55), and the most frequently used performance metric was sensitivity/recall/true positive rate (n = 87) and accuracy (n = 69). Dataset sizes ranged from 60 to 12,179 images. Although deep learning algorithms are used as individual or at least individualized architectures, standardized architectures such as pre-trained CNNs, Faster R-CNN, YOLO, and U-Net have been used in most studies. Few studies have used the explainable AI method (n = 22) and applied tests comparing human and artificial intelligence (n = 21). Deep learning is promising for better diagnosis and treatment planning in dentistry based on the high-performance results reported by the studies. For all that, their safety should be demonstrated using a more reproducible and comparable methodology, including tests with information about their clinical applicability, by defining a standard set of tests and performance metrics.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Figure 1 (A)mentioning

confidence: 99%

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Deep Learning in Diagnosis of Dental Anomalies and Diseases: A Systematic Review

et al. 2023

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“…In dental diagnostics, this specific iteration of LeNet has been meticulously tailored. Equipped with an uncanny ability to parse nuanced patterns within data, it scrutinizes dental images precisely, distinguishing pivotal features and subsequently classifying them based on the presence or absence of caries [59]. The model's integration of the "pervasive deep gradient-based" methodology is particularly noteworthy.…”

Section: Pervasive Deep Gradient-based Lenetmentioning

confidence: 99%

Revolutionizing Dental Caries Diagnosis through Artificial Intelligence

Anil,

Sudeep,

Saratchandran

et al. 2024

Dental Caries Perspectives - A Collection of Thoughtful Essays

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The diagnosis and management of dental caries, a prevalent global oral health issue, have traditionally depended on clinical examination and the interpretation of radiographic images. However, with the rapid advancements in technology, the landscape of dental diagnostics is transforming. This chapter delves into the revolutionary impact of artificial intelligence (AI) on detecting and managing dental caries. Dental professionals can now achieve enhanced diagnostic accuracy by harnessing the power of machine learning algorithms and image recognition technologies, even identifying early-stage caries that conventional methods might overlook. The integration of AI into dentistry not only promises improved patient outcomes by facilitating timely interventions and streamlining clinical workflows, potentially redefining the future of oral healthcare. While the prospects are promising, it is imperative to concurrently address the challenges and ethical considerations accompanying AI-driven diagnostics to ensure that the technology augments, rather than supplants, the expertise of dental professionals. The chapter serves as a comprehensive overview of the current state of AI in dental caries diagnosis, its potential benefits, and the road ahead.

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“…This problem is exacerbated in low- and middle-income countries where expensive radiographic instruments are not widely available. Automatic diagnostic methods for periodontal disease via machine learning have been implemented in specific research using X-ray and hyperspectral images as the inputs [ 27 , 28 , 29 , 30 ]. However, both of these methods are not practical compared to diagnosis by a medical practitioner using X-rays because of the high cost of imaging and the protracted processes involved.…”

Section: Introductionmentioning

confidence: 99%

The Optimization of the Light-Source Spectrum Utilizing Neural Networks for Detecting Oral Lesions

et al. 2022

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Any change in the light-source spectrum modifies the color information of an object. The spectral distribution of the light source can be optimized to enhance specific details of the obtained images; thus, using information-enhanced images is expected to improve the image recognition performance via machine vision. However, no studies have applied light spectrum optimization to reduce the training loss in modern machine vision using deep learning. Therefore, we propose a method for optimizing the light-source spectrum to reduce the training loss using neural networks. A two-class classification of one-vs-rest among the classes, including enamel as a healthy condition and dental lesions, was performed to validate the proposed method. The proposed convolutional neural network-based model, which accepts a 5 × 5 small patch image, was compared with an alternating optimization scheme using a linear-support vector machine that optimizes classification weights and lighting weights separately. Furthermore, it was compared with the proposed neural network-based algorithm, which inputs a pixel and consists of fully connected layers. The results of the five-fold cross-validation revealed that, compared to the previous method, the proposed method improved the F1-score and was superior to the models that were using the immutable standard illuminant .

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Automatic diagnosis and detection of dental caries in bitewing radiographs using pervasive deep gradient based LeNet classifier model

Cited by 10 publications

References 14 publications

Deep Learning in Diagnosis of Dental Anomalies and Diseases: A Systematic Review

Deep Learning in Diagnosis of Dental Anomalies and Diseases: A Systematic Review

Revolutionizing Dental Caries Diagnosis through Artificial Intelligence

The Optimization of the Light-Source Spectrum Utilizing Neural Networks for Detecting Oral Lesions

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