Accurate age classification using manual method and deep convolutional neural network based on orthopantomogram images

Guo, Yucheng; Han, Mengqi; Chi, Yuting; Li, Hong; Zhang, Dong; Yang, Jing; Yang, Yang; Chen, Teng; Du, Shaoyi

doi:10.1007/s00414-021-02542-x

Cited by 47 publications

(42 citation statements)

References 42 publications

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“…Kök et al (2019) 14 reported a mean accuracy of 77.02%, whereas the accuracy reported by Amasya et al (2020) 15 was 58.3%. Another way to estimate the age of a person is to focus on the dental age, which Guo et al (2021) did in their study, with an accuracy of 94.15%, 16 finding that CNN models were able to surpass humans in age classification.…”

Section: Resultsmentioning

confidence: 99%

Artificial intelligence in orthodontics: Where are we now? A scoping review

Monill-González

Rovira-Calatayud

d'Oliveira

et al. 2021

Orthod Craniofacial Res

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Objective This scoping review aims to determine the applications of Artificial Intelligence (AI) that are extensively employed in the field of Orthodontics, to evaluate its benefits, and to discuss its potential implications in this speciality. Recent decades have witnessed enormous changes in our profession. The arrival of new and more aesthetic options in orthodontic treatment, the transition to a fully digital workflow, the emergence of temporary anchorage devices and new imaging methods all provide both patients and professionals with a new focus in orthodontic care. Materials and methods This review was performed following the Preferred Reporting Items for Systematic reviews and Meta‐Analyses extension for Scoping Reviews (PRISMA‐ScR) guidelines. The electronic literature search was performed through MEDLINE/PubMed, Scopus, Web of Science, Cochrane and IEEE Xplore databases with a 11‐year time restriction: January 2010 till March 2021. No additional manual searches were performed. Results The electronic literature search initially returned 311 records, and 115 after removing duplicate references. Finally, the application of the inclusion criteria resulted in 17 eligible publications in the qualitative synthesis review. Conclusion The analysed studies demonstrated that Convolution Neural Networks can be used for the automatic detection of anatomical reference points on radiological images. In the growth and development research area, the Cervical Vertebral Maturation stage can be determined using an Artificial Neural Network model and obtain the same results as expert human observers. AI technology can also improve the diagnostic accuracy for orthodontic treatments, thereby helping the orthodontist work more accurately and efficiently.

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Section: Resultsmentioning

confidence: 99%

Artificial intelligence in orthodontics: Where are we now? A scoping review

Monill-González

Rovira-Calatayud

d'Oliveira

et al. 2021

Orthod Craniofacial Res

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“…There is considerable interest in accelerating identification procedures, and experts are involved in machine learning in forensic procedures. They use X-ray images [103][104][105][106][107][108], MRI images [8,109], photography [90,110,111], CT scans [112][113][114][115][116][117] of the head or other bones such as the collarbone, femur, teeth, etc. and use databases to teach artificial intelligence to identify people's age or gender.…”

Section: Overview Of Used Artificial Intelligence For Forensic Age and Sex Determinationmentioning

confidence: 99%

Section: Overview Of Used Artificial Intelligence For Forensic Age and Sex Determinationmentioning

confidence: 99%

“…Several research teams have tried neural network learning based on X-ray images [103,104,108]. Guo et al [103] used 10,257 samples of dental orthopantomograms and, similar to Farhadian et al [115], compared logistic regression linear models for each legal age limit (14, 16 and 18 years) with the neural network. The results showed that neural networks work better (linear regression models: 92.5%, 91.3% and 91.8% and neural networks: 95.9%, 95.4% and 92.3% success rate for age limits 14, 16 and 18 years).…”

Section: Overview Of Used Artificial Intelligence For Forensic Age and Sex Determinationmentioning

confidence: 99%

“…The accuracy was 59% compared to 61% for the specialist. In addition, Porto et al [88] sought to determine the legal age of offenders at 14 and 18 years as Guo et al [103]. They based on a database of photographs of 18,000 faces of men and women based on photo anthropometric indices from cephalometric landmarks marked and checked by forensic experts.…”

Section: Overview Of Used Artificial Intelligence For Forensic Age and Sex Determinationmentioning

confidence: 99%

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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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