Inferior alveolar nerve canal segmentation by local features based neural network model

Maheswari, P. Uma; Banumathi, A.; Ulaganathan, G; Yoganandha, R.

doi:10.1049/ipr2.12375

Cited by 4 publications

(3 citation statements)

References 33 publications

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“…Candidate regions were selected from the enhanced image by the proposed Multi Hidden Layer Extreme Learning Machine Artificial Neural Network (MELMANN) model. Experimental results indicate that this method effectively delineated the IAC [ 48 ].…”

Section: Discussionmentioning

confidence: 99%

An artificial intelligence study: automatic description of anatomic landmarks on panoramic radiographs in the pediatric population

Bağ,

Bilgir,

Bayrakdar

et al. 2023

BMC Oral Health

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Background Panoramic radiographs, in which anatomic landmarks can be observed, are used to detect cases closely related to pediatric dentistry. The purpose of the study is to investigate the success and reliability of the detection of maxillary and mandibular anatomic structures observed on panoramic radiographs in children using artificial intelligence. Methods A total of 981 mixed images of pediatric patients for 9 different pediatric anatomic landmarks including maxillary sinus, orbita, mandibular canal, mental foramen, foramen mandible, incisura mandible, articular eminence, condylar and coronoid processes were labelled, the training was carried out using 2D convolutional neural networks (CNN) architectures, by giving 500 training epochs and Pytorch-implemented YOLO-v5 models were produced. The success rate of the AI model prediction was tested on a 10% test data set. Results A total of 14,804 labels including maxillary sinus (1922), orbita (1944), mandibular canal (1879), mental foramen (884), foramen mandible (1885), incisura mandible (1922), articular eminence (1645), condylar (1733) and coronoid (990) processes were made. The most successful F1 Scores were obtained from orbita (1), incisura mandible (0.99), maxillary sinus (0.98), and mandibular canal (0.97). The best sensitivity values were obtained from orbita, maxillary sinus, mandibular canal, incisura mandible, and condylar process. The worst sensitivity values were obtained from mental foramen (0.92) and articular eminence (0.92). Conclusions The regular and standardized labelling, the relatively larger areas, and the success of the YOLO-v5 algorithm contributed to obtaining these successful results. Automatic segmentation of these structures will save time for physicians in clinical diagnosis and will increase the visibility of pathologies related to structures and the awareness of physicians.

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Section: Discussionmentioning

confidence: 99%

An artificial intelligence study: automatic description of anatomic landmarks on panoramic radiographs in the pediatric population

Bağ,

Bilgir,

Bayrakdar

et al. 2023

BMC Oral Health

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“…The MELMANN method successfully differentiates and separates potential MC and non-MC areas in panoramic radiographs using features generated from HOG, LBP, and GLCM. These characteristics are used as a function vector in the MELMbased ANN Classifier [82].…”

Section: Multi Hidden Layer Extreme Learning Machine Artificial Neura...mentioning

confidence: 99%

“…Maheswari et al [82] Panoramic The proposed approach combines a transformer architecture, cl-Dice loss, and pixel-level feature fusion to enhance the model's sensitivity to fine-grained details and connectivity of the MC.…”

Section: Joo Et Al [77]mentioning

confidence: 99%

Deep Learning for Mandibular Canal Segmentation in Digital Dental Radiographs: A Systematic Literature Review

Farid Naufal,

Fatichah,

Renwi Astuti

et al. 2024

IEEE Access

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Context: Several recent studies in dentistry and maxillofacial imaging have concentrated on the mandibular canal (MC) segmentation in digital dental radiographs. In this research domain, deep learning approaches have demonstrated promising outcomes. Objective: This systematic literature review (SLR) aims to comprehensively analyze and synthesize the recent advancements in applying deep learning techniques for MC segmentation in digital dental radiographs. Method: This review encompasses studies published between 2018 and 2023, sourced from reputable databases, including PubMed, ScienceDirect, IEEE Xplore, and Google Scholar. Results: This study identified 30 primary research papers focusing on MC segmentation in digital dental radiographs. The review categorizes papers into two groups based on the digital dental radiograph type. The first group uses a 2D digital dental radiograph from a panoramic radiograph and 2D Cone Beam Computed Tomography (CBCT) scans. The second group uses 3D datasets from volumetric data from CBCT scans. Conclusion:The synthesized knowledge from this review is intended to guide researchers, dentists, and oral surgeons in leveraging deep learning advancements in MC segmentation for oral and maxillofacial surgery. Prior studies have faced challenges, including limited public datasets, variations in MC anatomy, time consumption and observer variability in MC annotation, complexities in deep learning models, and lack of practical implementation. To overcome these challenges, it is suggested that more public datasets be collaboratively created and shared within the research community, focusing on handling anatomy variability, improving digital dental radiograph quality, streamlining annotation processes through automated tools, and simplifying deep learning models for practical implementation.

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Automatic segmentation of inferior Alveolar Nerve Canal in dental OPG images

Pandiyan,

Sindha,

Balakrishnan

et al. 2023

24th Topical Conference on Radio-Frequency Power in Plasmas

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Inferior alveolar nerve canal segmentation by local features based neural network model

Cited by 4 publications

References 33 publications

An artificial intelligence study: automatic description of anatomic landmarks on panoramic radiographs in the pediatric population

An artificial intelligence study: automatic description of anatomic landmarks on panoramic radiographs in the pediatric population

Deep Learning for Mandibular Canal Segmentation in Digital Dental Radiographs: A Systematic Literature Review

Automatic segmentation of inferior Alveolar Nerve Canal in dental OPG images

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