Deep learning for cephalometric landmark detection: systematic review and meta-analysis

Schwendicke, Falk; Chaurasia, Akhilanand; Arsiwala, Lubaina Tayeb; Lee, Jae Hong; Elhennawy, Karim; Jost‐Brinkmann, Paul‐Georg; Demarco, Flávio Fernando; Krois, Joachim

doi:10.1007/s00784-021-03990-w

Cited by 87 publications

(68 citation statements)

References 41 publications

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“…Nowadays, artificial intelligence (AI) has gained a giant leap in dentistry, assisting clinicians in a variety of fields, e.g., detection of periapical lesions and root fractures, optimizing implant designs, diagnosis of oral cancer [ 9 , 10 , 11 ]. Deep learning is a key field of AI, which uses a learning model to extract features of the labelled dataset and eventually can predict labels on a new dataset [ 12 ]. Mimicking the way that human brain neurons signal to another, neural networks are widely used in deep learning.…”

Section: Introductionmentioning

confidence: 99%

Development of an Artificial Intelligence System for the Automatic Evaluation of Cervical Vertebral Maturation Status

Zhou

et al. 2021

Diagnostics

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Background: Cervical vertebral maturation (CVM) is widely used to evaluate growth potential in the field of orthodontics. This study is aimed to develop an artificial intelligence (AI) system to automatically determine the CVM status and evaluate the AI performance. Methods: A total of 1080 cephalometric radiographs, with the age of patients ranging from 6 to 22 years old, were included in the dataset (980 in training dataset and 100 in testing dataset). Two reference points and thirteen anatomical points were labelled and the cervical vertebral maturation staging (CS) was assessed by human examiners as gold standard. A convolutional neural network (CNN) model was built to train on 980 images and to test on 100 images. Statistical analysis was conducted to detect labelling differences between AI and human examiners, AI performance was also evaluated. Results: The mean labelling error between human examiners was 0.48 ± 0.12 mm. The mean labelling error between AI and human examiners was 0.36 ± 0.09 mm. In general, the agreement between AI results and the gold standard was good, with the intraclass correlation coefficient (ICC) value being up to 98%. Moreover, the accuracy of CVM staging was 71%. In terms of F1 score, CS6 stage (85%) ranked the highest accuracy. Conclusions: In this study, AI showed a good agreement with human examiners, being a useful and reliable tool in assessing the cervical vertebral maturation.

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Section: Introductionmentioning

confidence: 99%

Development of an Artificial Intelligence System for the Automatic Evaluation of Cervical Vertebral Maturation Status

Zhou

et al. 2021

Diagnostics

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“…The beauty industry has seen rapid growth in multiple countries, and due to its applications in entertainment, the analysis and assessment of facial attractiveness have received attention from scientists, physicians, and artists because of digital media, plastic surgery, and cosmetics. An analysis of techniques is used to assess facial beauty that considers facial ratios and facial qualities as elements to predict facial beauty [81,82,[138][139][140]. A popular and famous free app using AI is FaceApp, which uses neural networks to enhance, age or otherwise change 2D digital photos of users uploading them using this application (Figure 7).…”

Section: Artificial Intelligence Implementation In Soft-tissue Face Prediction From Skull and Vice Versamentioning

confidence: 99%

Use of Advanced Artificial Intelligence in Forensic Medicine, Forensic Anthropology and Clinical Anatomy

et al. 2021

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Three-dimensional convolutional neural networks (3D CNN) of artificial intelligence (AI) are potent in image processing and recognition using deep learning to perform generative and descriptive tasks. Compared to its predecessor, the advantage of CNN is that it automatically detects the important features without any human supervision. 3D CNN is used to extract features in three dimensions where input is a 3D volume or a sequence of 2D pictures, e.g., slices in a cone-beam computer tomography scan (CBCT). The main aim was to bridge interdisciplinary cooperation between forensic medical experts and deep learning engineers, emphasizing activating clinical forensic experts in the field with possibly basic knowledge of advanced artificial intelligence techniques with interest in its implementation in their efforts to advance forensic research further. This paper introduces a novel workflow of 3D CNN analysis of full-head CBCT scans. Authors explore the current and design customized 3D CNN application methods for particular forensic research in five perspectives: (1) sex determination, (2) biological age estimation, (3) 3D cephalometric landmark annotation, (4) growth vectors prediction, (5) facial soft-tissue estimation from the skull and vice versa. In conclusion, 3D CNN application can be a watershed moment in forensic medicine, leading to unprecedented improvement of forensic analysis workflows based on 3D neural networks.

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“…In the case of AI, there is intense debate around standardization and regulation, mainly as current applications are not all robust, generalizable, and explainable; that is, they may suffer from undetected bias and performance gaps (Liu et al 2020; Nagendran et al 2020; Schwendicke, Chaurasia, et al 2021). Generally, AI applications are approached in a similar way as other medical devices (e.g., non-AI software), with a risk-based approach considering patients, users, or third parties to ensure safety and performance (e.g., IEC 62304 2015, “Software Life Cycle”; ISO 14971, “Risk Management Medical Devices”).…”

Section: New Challengesmentioning

confidence: 99%

Normative Approaches for Oral Health: Standards, Specifications, and Guidelines

2021

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Normative approaches have been developed with the aim of providing high-quality methods and strict criteria that, when applied correctly, lead to reliable results. Standards, specifications, and guidelines are needed to facilitate exchange of goods or information and secure comparability of data derived from different laboratories and sources. They are available along the whole flow from study development to test selection, study conduct, and reporting and are widely used for the evaluation of medical devices, market approval, and harmonization of terms and devices. Standards are developed by specific national and international organizations or by dedicated interest groups, mainly scientists in their respective fields. ISO (International Organization for Standardization) standards are developed following stringent regulations, and groups of experts formulate such standards. They should come from different areas (multistakeholder approach) to have as much and as broad input as possible and to avoid single-interest dominance. However, the presence of academia in such groups has been comparatively low. There is a clear need and responsibility of the oral health community to participate in the development of normative documents to provide methodological knowledge and experience, balance the interests of other stakeholders, and finally improve oral health. This will help to ensure that rapidly advancing fields of research, such as the oral health impacts of COVID-19 or the application of artificial intelligence in dentistry, benefit from standardization of approaches and reporting.

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Deep learning for cephalometric landmark detection: systematic review and meta-analysis

Cited by 87 publications

References 41 publications

Development of an Artificial Intelligence System for the Automatic Evaluation of Cervical Vertebral Maturation Status

Development of an Artificial Intelligence System for the Automatic Evaluation of Cervical Vertebral Maturation Status

Use of Advanced Artificial Intelligence in Forensic Medicine, Forensic Anthropology and Clinical Anatomy

Normative Approaches for Oral Health: Standards, Specifications, and Guidelines

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