Convolutional neural networks combined with classification algorithms for the diagnosis of periodontitis.
Objectives: We aim to develop a deep learning model based on a convolutional neural network (CNN) combined with a classification algorithm (CA) to assist dentists in quickly and accurately diagnosing the stage of periodontitis. Materials and methods: Periapical radiographs (PERs) and clinical data w...
| Publicado en: | Oral Radiology Vol. 40; no. 3; pp. 357 - 367 |
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| Autores principales: | , , , , , , , , |
| Formato: | diagnostic images research tables/charts Journal Article |
| Publicado: |
Springer Nature
Jul2024
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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=178995301&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 178995301 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 09116028 1P9 jtl: Oral Radiology issn: 09116028 maglogo: N pubinfo: dt: Jul2024 vid: 40 iid: 3 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 178995301 175593261 178995301 178995301 10.1007/s11282-024-00739-5 178995301 ppf: 357 ppct: 10 formats: tig: atl: Convolutional neural networks combined with classification algorithms for the diagnosis of periodontitis. aug: au: Dai, Fang Liu, Qiangdong Guo, Yuchen Xie, Ruixiang Wu, Jingting Deng, Tian Zhu, Hongbiao Deng, Libin Song, Li affil: https://ror.org/042v6xz23 Center of Stomatology, The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University, No.1, Minde Road, 330000, Nanchang, Jiangxi, China sug: subj: Convolutional Neural Networks Classification Algorithms Periodontitis Classification Periodontitis Diagnosis Deep Learning Dentists Diagnosis, Computer Assisted Decision Support Techniques Human Male Female Adult Middle Age Radiography, Dental Alveolar Bone Loss Random Forest Support Vector Machine Logistic Regression Cluster Analysis Disease Attributes Descriptive Statistics Age Factors Smoking Funding Source Retrospective Design China Adult: 19-44 years Middle Aged: 45-64 years Male Female ab: Objectives: We aim to develop a deep learning model based on a convolutional neural network (CNN) combined with a classification algorithm (CA) to assist dentists in quickly and accurately diagnosing the stage of periodontitis. Materials and methods: Periapical radiographs (PERs) and clinical data were collected. The CNNs including Alexnet, VGG16, and ResNet18 were trained on PER to establish the PER-CNN models for no periodontal bone loss (PBL) and PBL. The CAs including random forest (RF), support vector machine (SVM), naive Bayes (NB), logistic regression (LR), and k-nearest neighbor (KNN) were added to the PER-CNN model for control, stage I, stage II and stage III/IV periodontitis. Heat map was produced using a gradient-weighted class activation mapping method to visualize the regions of interest of the PER-Alexnet model. Clustering analysis was performed based on the ten PER-CNN scores and the clinical characteristics. Results: The accuracy of the PER-Alexnet and PER-VGG16 models with the higher performance was 0.872 and 0.853, respectively. The accuracy of the PER-Alexnet + RF model with the highest performance for control, stage I, stage II and stage III/IV was 0.968, 0.960, 0.835 and 0.842, respectively. Heat map showed that the regions of interest predicted by the model were periodontitis bone lesions. We found that age and smoking were significantly related to periodontitis based on the PER-Alexnet scores. Conclusion: The PER-Alexnet + RF model has reached high performance for whole-case periodontal diagnosis. The CNN models combined with CA can assist dentists in quickly and accurately diagnosing the stage of periodontitis. pubtype: Academic Journal doctype: diagnostic images research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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