An End-to-end Entangled Segmentation and Classification Convolutional Neural Network for Periodontitis Stage Grading from Periapical Radiographic Images

Kabir, Tanjida; Lee, Chun‐Teh; Nelson, Jiman; Sheng, Sally; Meng, Hsiu-Wan; Chen, Luyao; Walji, Muhammad F.; Jiang, Xioaqian; Shams, Soheil

doi:10.1109/bibm52615.2021.9669422

Cited by 7 publications

(7 citation statements)

References 31 publications

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“…Here, we use the previously developed DL model which integrates the segmentation and classification models and image analysis methods to measure RBL percentage and assign periodontal stages using periapical radiographs [3,20] Assigning stages using the bone loss percentage is based on the 2018 periodontitis classification [43].…”

Section: Periodontal Diagnosismentioning

confidence: 99%

“…Deep learning (DL) models have been utilized in several medical image analyses to identify abnormalities such as brain tumor segmentation [14], breast cancer diagnosis [15], lung cancer [16], prostate cancer [17], and Parkinson's disease [18] achieving higher performance than other state-of-the-art methods. In the last few years, DL models have been developed in dentistry to diagnose diseases from dental radiographs, including caries [4], radiographic bone loss (RBL) [3,19,20], and periapical lesions [21]. However, although these models have good performance in detecting abnormalities, they cannot recognize tooth numbers to provide detailed diagnostic information on individual teeth, therefore limiting the clinical application of these DL models.…”

Section: Introductionmentioning

confidence: 99%

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A comprehensive artificial intelligence framework for dental diagnosis and charting

et al. 2022

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Background The aim of this study was to develop artificial intelligence (AI) guided framework to recognize tooth numbers in panoramic and intraoral radiographs (periapical and bitewing) without prior domain knowledge and arrange the intraoral radiographs into a full mouth series (FMS) arrangement template. This model can be integrated with different diseases diagnosis models, such as periodontitis or caries, to facilitate clinical examinations and diagnoses. Methods The framework utilized image segmentation models to generate the masks of bone area, tooth, and cementoenamel junction (CEJ) lines from intraoral radiographs. These masks were used to detect and extract teeth bounding boxes utilizing several image analysis methods. Then, individual teeth were matched with a patient’s panoramic images (if available) or tooth repositories for assigning tooth numbers using the multi-scale matching strategy. This framework was tested on 1240 intraoral radiographs different from the training and internal validation cohort to avoid data snooping. Besides, a web interface was designed to generate a report for different dental abnormalities with tooth numbers to evaluate this framework’s practicality in clinical settings. Results The proposed method achieved the following precision and recall via panoramic view: 0.96 and 0.96 (via panoramic view) and 0.87 and 0.87 (via repository match) by handling tooth shape variation and outperforming other state-of-the-art methods. Additionally, the proposed framework could accurately arrange a set of intraoral radiographs into an FMS arrangement template based on positions and tooth numbers with an accuracy of 95% for periapical images and 90% for bitewing images. The accuracy of this framework was also 94% in the images with missing teeth and 89% with restorations. Conclusions The proposed tooth numbering model is robust and self-contained and can also be integrated with other dental diagnosis modules, such as alveolar bone assessment and caries detection. This artificial intelligence-based tooth detection and tooth number assignment in dental radiographs will help dentists with enhanced communication, documentation, and treatment planning accurately. In addition, the proposed framework can correctly specify detailed diagnostic information associated with a single tooth without human intervention.

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Section: Periodontal Diagnosismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A comprehensive artificial intelligence framework for dental diagnosis and charting

et al. 2022

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“…Tajinda et al integrated segmentation and classification tasks for grading periodontitis from periapical radiography images to create the hybrid network for periodontitis stages from radiograph (HYNETS) end-to-end DL network. By combining segmentation networks and a classification network, HYNETS uses a multi-task learning technique to provide a complete, interpretable solution with extremely accurate and reliable results [ 83 ]. Together with data pre-processing and augmentation techniques, Szu-Yin Lin and Hao-Yun Chang have created an innovative and effective two-phase DPR detection and methodology to help dentists in diagnosis by using advanced DL algorithms.…”

Section: Resultsmentioning

confidence: 99%

“…Different types of images were used by different researchers based on the techniques they used in DI. Radiographic images [ 16 , 41 , 43 , 56 , 63 , 64 , 65 , 67 , 68 , 69 , 70 , 71 , 72 , 73 , 74 , 75 , 76 , 77 , 78 , 79 , 80 , 81 , 82 , 83 , 84 , 85 , 87 , 157 ], near-infrared light transillumination (NILT) [ 88 , 89 , 90 ], intraoral images [ 66 , 86 , 91 , 92 , 93 , 95 , 96 , 97 , 158 , 159 , 160 ], 3D model [ 102 , 113 , 114 , 115 , 161 ] were used in the research for dental diseases diagnostic on the 3D dental model. The studies on the dental disease’s diagnostic on the CBCT dental model used the CT images [ 124 ], 3D CT scans […”

Section: Resultsmentioning

confidence: 99%

Analysis of Deep Learning Techniques for Dental Informatics: A Systematic Literature Review

et al. 2022

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Within the ever-growing healthcare industry, dental informatics is a burgeoning field of study. One of the major obstacles to the health care system’s transformation is obtaining knowledge and insightful data from complex, high-dimensional, and diverse sources. Modern biomedical research, for instance, has seen an increase in the use of complex, heterogeneous, poorly documented, and generally unstructured electronic health records, imaging, sensor data, and text. There were still certain restrictions even after many current techniques were used to extract more robust and useful elements from the data for analysis. New effective paradigms for building end-to-end learning models from complex data are provided by the most recent deep learning technology breakthroughs. Therefore, the current study aims to examine the most recent research on the use of deep learning techniques for dental informatics problems and recommend creating comprehensive and meaningful interpretable structures that might benefit the healthcare industry. We also draw attention to some drawbacks and the need for better technique development and provide new perspectives about this exciting new development in the field.

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“…Furthermore, a neural network is a useful tool for full automated segmentation, and its architecture consists of different layers for convolution, pooling, and classification 11. The necessary training data and segmentation gold standard is usually obtained by manual segmentations 12–15. Kohl et al16 proposed U-Net neural convolution network for medical image segmentation, recognizing features and position of the region of interest, and extended the U-Net from a plane to a 3-dimensional (D) structure.…”

mentioning

confidence: 99%

Radiomics and Artificial Intelligence Study of Masseter Muscle Segmentation in Patients With Hemifacial Microsomia

Han

Xia

Zhang

et al. 2023

Journal of Craniofacial Surgery

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Background: Hemifacial microsomia (HFM) is one of the most common congenital craniofacial condition often accompanied by masseter muscle involvement. U-Net neural convolution network for masseter segmentation is expected to achieve an efficient evaluation of masseter muscle. Methods: A database was established with 108 patients with HFM from June 2012 to June 2019 in our center. Demographic data, OMENS classification, and 1-mm layer thick 3-dimensional computed tomography were included. Two radiologists manually segmented masseter muscles in a consensus reading as the ground truth. A test set of 20 cases was duplicated into 2 groups: an experimental group with the intelligent algorithm and a control group with manual segmentation. The U-net follows the design of 3D RoI-Aware U-Net with overlapping window strategy and references to our previous study of masseter segmentation in a healthy population system. Sorensen dice-similarity coefficient (DSC) muscle volume, average surface distance, recall, and time were used to validate compared with the ground truth. Results:The mean DSC value of 0.794 ± 0.028 for the experiment group was compared with the manual segmentation (0.885 ± 0.118) with α = 0.05 and a noninferiority margin of 15%. In addition, higher DSC was reported in patients with milder mandible deformity (r = 0.824, P < 0.05). Moreover, intelligent automatic segmentation takes only 6.4 seconds showing great efficiency. Conclusions: We first proposed a U-net neural convolutional network and achieved automatic segmentation of masseter muscles in patients with HFM. It is a great attempt at intelligent diagnosis and evaluation of craniofacial diseases.

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An End-to-end Entangled Segmentation and Classification Convolutional Neural Network for Periodontitis Stage Grading from Periapical Radiographic Images

Cited by 7 publications

References 31 publications

A comprehensive artificial intelligence framework for dental diagnosis and charting

A comprehensive artificial intelligence framework for dental diagnosis and charting

Analysis of Deep Learning Techniques for Dental Informatics: A Systematic Literature Review

Radiomics and Artificial Intelligence Study of Masseter Muscle Segmentation in Patients With Hemifacial Microsomia

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