A Deep Learning-Based Approach to Detect Lamina Dura Loss on Periapical Radiographs.
This study aimed to develop a custom artificial intelligence (AI) model for detecting lamina dura (LD) loss around the roots of anterior and posterior teeth on intraoral periapical radiographs. A total of 701 periapical radiographs of the anterior and posterior regions retrieved from the Dentomaxill...
| Publicado en: | Journal of Imaging Informatics in Medicine Vol. 38; no. 1; pp. 545 - 556 |
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| Autores principales: | , |
| Formato: | diagnostic images research tables/charts Journal Article |
| Publicado: |
Springer Nature
Feb2025
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=184471501&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 184471501 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 29482925 NR3A jtl: Journal of Imaging Informatics in Medicine issn: 29482925 maglogo: N pubinfo: dt: Feb2025 vid: 38 iid: 1 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 184471501 184471501 184471501 10.1007/s10278-025-01405-w 184471501 ppf: 545 ppct: 11 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: A Deep Learning-Based Approach to Detect Lamina Dura Loss on Periapical Radiographs. aug: au: Şahin, Büşra Eninanç, İlknur affil: https://ror.org/04f81fm77 Department of Oral and Maxillofacial Radiology, Faculty of Dentistry, Sivas Cumhuriyet University, Sivas, Turkey sug: subj: Deep Learning Machine Learning Algorithms Tooth Root Radiography Radiography, Dental Image Processing, Computer Assisted Convolutional Neural Networks Human Alveolar Process Radiography Artificial Intelligence Sensitivity and Specificity kappa Statistic Descriptive Statistics Interrater Reliability ab: This study aimed to develop a custom artificial intelligence (AI) model for detecting lamina dura (LD) loss around the roots of anterior and posterior teeth on intraoral periapical radiographs. A total of 701 periapical radiographs of the anterior and posterior regions retrieved from the Dentomaxillofacial Radiology archives were reviewed. Images were cropped to include only the teeth exhibiting LD loss and those without LD loss, which were labeled as "1" and "0," respectively. The dataset was diversified using image preprocessing and data augmentation techniques. Among the radiographs, 72% were used for training, 18% for validation, and 10% for testing. A custom AI model, consisting of 4 blocks and 49 layers, with a total of 21.2 million parameters, was developed using the TensorFlow library and residual blocks introduced in ResNet architecture. Sensitivity, specificity, accuracy, precision, F1 score, and kappa (κ) coefficients (for intra-observer agreement) were calculated to evaluate the performance of the AI model. When applied to a test set of 71 images, the AI model showed good performance in detecting LD loss, achieving an average sensitivity of 0.730, specificity of 0.706, accuracy of 0.718, precision of 0.730, and an F1 score of 0.730, regardless of the dental region. This study represents the first known application of an AI algorithm tailored to detect LD loss on periapical radiographs. The developed AI model could aid clinicians in making accurate diagnosis and help prevent misdiagnosis. pubtype: Academic Journal doctype: diagnostic images research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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