Automatic Feature Segmentation in Dental Periapical Radiographs

Abstract

While a large number of archived digital images make it easy for radiology to provide data for Artificial Intelligence (AI) evaluation; AI algorithms are more and more applied in detecting diseases. The aim of the study is to perform a diagnostic evaluation on periapical radiographs with an AI model based on Convoluted Neural Networks (CNNs). The dataset includes 1169 adult periapical radiographs, which were labelled in CranioCatch annotation software. Deep learning was performed using the U-Net model implemented with the PyTorch library. The AI models based on deep learning models improved the success rate of carious lesion, crown, dental pulp, dental filling, periapical lesion, and root canal filling segmentation in periapical images. Sensitivity, precision and F1 scores for carious lesion were 0.82, 0.82, and 0.82, respectively; sensitivity, precision and F1 score for crown were 1, 1, and 1, respectively; sensitivity, precision and F1 score for dental pulp, were 0.97, 0.87 and 0.92, respectively; sensitivity, precision and F1 score for filling were 0.95, 0.95, and 0.95, respectively; sensitivity, precision and F1 score for the periapical lesion were 0.92, 0.85, and 0.88, respectively; sensitivity, precision and F1 score for root canal filling, were found to be 1, 0.96, and 0.98, respectively. The success of AI algorithms in evaluating periapical radiographs is encouraging and promising for their use in routine clinical processes as a clinical decision support system.

Fully automated deep learning approach to dental development assessment in panoramic radiographs

“…Segmenting the tooth from the surrounding background can enhance the stage classification performance of the model, as the remaining surrounding tissues may obscure correct stage allocation [33]. U-Net, known for its high performance in segmenting teeth in panoramic and periapical images, as well as different features of teeth in periapical images [26,27,36], was employed to segment detected teeth in this study, achieving a high accuracy of 0.978. For tooth development staging, Merfietio Boedi et al suggested the full tooth segmentation type, which includes only the developing tooth structure [33].…”

“…Preceding the classification, tooth detection and segmentation would enhance the overall performance of stage classification compared to the classification procedure alone. While deep learning models have demonstrated high accuracy in tooth detection and segmentation [26,27,35,36], their performance for dental developmental stage classification remains insufficient. Previous studies on deep learning models for development stage classification have primarily focused on premolars or molars [12,32,33], with research on incisors and canines lacking.…”

Deep Learning to Detect and Classify Teeth, Dental Caries and Restorations: A Systematic Mapping

“…(to be continued on next page) (533/59/-) PAN 3 dentists (+10y) yes [77] (1250/-/125) PERI dentists (unspecified) yes [81] (3293/252/141) BTW 4 dentists (+3y) yes [82] (492/65/64) BTW 1 dentist + 1 radiologist (+9y) yes [15] (800/-/200) BTW 2 dentists (+10y) yes [83] photos + 120 x-rays * [57] Photos + X-Rays 2 dentists (+3y) yes [1] (1071/-/89) PAN 4 dentists (3-15y) yes [88] BTW 1 experienced dentist in oral radiology yes [38] PAN radiologists (unspecified) yes [34] (30/11/10) PAN 1 dentist + 1 radiologist yes [40] (447/127/61) PAN -yes [37] (485/69/139) PERI 2 dentists + 1 resident dentist yes [61] (80/-/20) PAN 2 dentists yes [84] (400/50/50) BTW 1 radiologist (11y) and a research assistant (3y) yes [62] (1200/150/150) PAN Clinicians (unspecified) yes [85] (935/117/117) PERI 1 radiologist (12y) and a research assistant (2y) yes [51] (76/-/32) PAN 3 dentists (+3y) yes [52] (980/-/420) PAN 3 dentists (+3y) yes [63] (90/-/10) PAN Radiologists (unspecified, +5y) yes [53] (1104/111/121) RVG Dentists (unspecified) yes [89] (175/-/75) PAN Dentists (unspecified, +5y) yes [87] (457/-/195) Unspecified 4 dentists yes [78] (193/83/1224) PAN -no [66] (2507/835/835) PAN 1 dentist + 1 dental student (last year) yes [79] PANs + 682 PERI/BTW * [3] PAN + PERI + BTW 3 dentists yes [86] PAN dentists (unspecified) yes [35] (1005/335/335) BTW 2 dentists yes [80] (1000/0/200) BTW Dentists (unspecified) no [41] ( (iii) number and expertise of professionals involved in the annotation task and (iv) whether the paper presents any information about the dataset annotation protocol. This information is used to investigate RQ1 Concerning RQ1.1, the selected papers exploited three different types of radiograph: panoramic (forty-six papers, 66.67%), periapical (nine papers, 13.04%), and bitewing (eight papers, 11.59%).…”