Tooth Numbering and Condition Recognition on Dental Panoramic Radiograph Images Using CNNs

Lin, Szu-Yin; Chang, Hsu-Hao

doi:10.1109/access.2021.3136026

Cited by 19 publications

(24 citation statements)

References 23 publications

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“…In Table 1 , we present the different image analysis tasks on PAN. Seventeen studies reported on detection tasks—where bounding-box-based annotations were performed within an image; 8 , 9 , 15–17 , 19 , 21 , 26 , 27 , 33 , 35 , 39 , 40 , 42 , 43 , 46 , 48 26 studies focused on classification tasks, indicating studies that assigned a label to the entire image—either binary or multi-class; 7–13 , 17 , 20 , 22 , 24 , 25 , 28 , 32 , 34 , 36–38 , 40–45 , 47 , 48 3 studies conducted identification tasks, also bounding-box-based discrimination of object, but among other objects within the same class (e.g. facial recognition).…”

Section: Resultsmentioning

confidence: 99%

“…Among the studies, 14 used multi-stage architectures combining different tasks in a pipeline to produce an output. 8 , 9 , 11 , 13 , 17 , 26–28 , 36 , 39 , 40 , 42 , 43 , 48 In contrast, 27 studies used a single model to carry out distinct tasks. 7 , 10 , 12 , 14–16 , 19–25 , 29–35 , 37 , 38 , 41 , 44–47 …”

Section: Resultsmentioning

confidence: 99%

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Panoramic imaging errors in machine learning model development: a systematic review

Delamare,

Fu,

Huang

et al. 2024

Dentomaxillofacial Radiology

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Objectives To investigate the management of imaging errors from panoramic radiography (PAN) datasets used in the development of machine learning (ML) models. Methods This systematic literature followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses and used three databases. Keywords were selected from relevant literature. Eligibility criteria PAN studies that used ML models and mentioned image quality concerns. Results Out of 400 articles, 41 papers satisfied the inclusion criteria. All the studies used ML models, with 35 papers using deep learning (DL) models. PAN quality assessment was approached in 3 ways: acknowledgement and acceptance of imaging errors in the ML model, removal of low-quality radiographs from the dataset before building the model, and application of image enhancement methods prior to model development. The criteria for determining PAN image quality varied widely across studies and were prone to bias. Conclusions This study revealed significant inconsistencies in the management of PAN imaging errors in ML research. However, most studies agree that such errors are detrimental when building ML models. More research is needed to understand the impact of low-quality inputs on model performance. Prospective studies may streamline image quality assessment by leveraging DL models, which excel at pattern recognition tasks.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Panoramic imaging errors in machine learning model development: a systematic review

Delamare,

Fu,

Huang

et al. 2024

Dentomaxillofacial Radiology

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“…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. Orthodontics, dental restoration, endodontic therapy, implant, impaction, and dental prosthesis are among the dental problems that are instantly detected [ 84 ]. To categorize the teeth positions by using a proposed correlation module that makes use of the information between teeth positions, Zhang et al [ 85 ] employed DL methods and used a particular label creation methodology to partition the teeth classification job before using a multi-task CNN.…”

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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“…Factors such as fatigue, emotions and inexperience can contribute to misdiagnosis [9], which impairs the quality of dental treatment [10].…”

Section: Introductionmentioning

confidence: 99%

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

Carneiro,

Zancan,

Tirapelli

et al. 2023

Preprint

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Purpose: Oral health is an indicator of individuals' health and quality of life. Consequently, it is a concerning topic faced everyday by healthcare professionals. X-ray images are essential resources for detecting oral problems. Artificial Intelligence has gained attention in the detection of oral diseases and conditions. We carry out a systematic mapping in the literature regarding the application of deep learning in dental radiographs for detection, segmentation, and classification of teeth, caries, and restorations. Methods: Through automated searches in the ACM Digital Library, IEEE Xplore Digital Library, PubMed, and Scopus, 394 primary articles were found, published between 2012-2023. After applying inclusion and exclusion criteria, 69 articles were read and analyzed considering: consistency and adequacy of the used databases, implemented techniques, and results. Results: 40.82% of the analyzed articles did not present clear information on the approval of ethics committees to carry out the research. Although the application of computational techniques in oral health is an interdisciplinary topic, 37.68% of the 69 surveys were conducted by teams composed only of professionals from one area. 66.67% of the articles explored only panoramic radiographs and the most used metrics were accuracy, recall, and precision. The U-Net and Mask R-CNN networks were the most applied. Conclusion: Despite the increase in the number of investigations, some challenges, such as the low availability of public data sets, the lack of details in the developed methodologies, and the lack of standardization when presenting results, complicate a fair comparison between papers, which is an obstacle to be overcome.

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Tooth Numbering and Condition Recognition on Dental Panoramic Radiograph Images Using CNNs

Abstract: Date of publication xxxx 00, 0000, date of current version xxxx 00, 0000.

Cited by 19 publications

References 23 publications

Panoramic imaging errors in machine learning model development: a systematic review

Panoramic imaging errors in machine learning model development: a systematic review

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

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

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