Deep Learning Neural Modelling as a Precise Method in the Assessment of the Chronological Age of Children and Adolescents Using Tooth and Bone Parameters

Zaborowicz, Maciej; Zaborowicz, Katarzyna; Biedziak, Barbara; Garbowski, Tomasz

doi:10.3390/s22020637

Cited by 20 publications

(31 citation statements)

References 49 publications

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“…In order to fairly compare the results obtained with the use of the model presented in this paper with the results obtained with the use of very popular and widely used neural networks based on deep learning, only the test sets obtained by both models were selected for comparison. Deep learning network results for the same case can be found in our recent paper [ 47 ]. Since in the work by Zaborowicz et al [ 47 ] the test set consisted of 25% of the whole set, the model presented in this work was also rebuilt so as to fairly compare the results.…”

Section: Discussionmentioning

confidence: 85%

“…Deep learning network results for the same case can be found in our recent paper [ 47 ]. Since in the work by Zaborowicz et al [ 47 ] the test set consisted of 25% of the whole set, the model presented in this work was also rebuilt so as to fairly compare the results. Ultimately, the error MAE = 8.8

1.0 month was obtained, while the same error for test set obtained with the use of deep learning neural networks was MAE = 10.0 months.…”

Section: Discussionmentioning

confidence: 85%

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Robust Estimation of the Chronological Age of Children and Adolescents Using Tooth Geometry Indicators and POD-GP

Zaborowicz

Garbowski

Biedziak

et al. 2022

IJERPH

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Determining the chronological age of children or adolescents is becoming an extremely necessary and important issue. Correct age-assessment methods are especially important in the process of international adoption and in the case of immigrants without valid documents confirming their identity. It is well known that traditional, analog methods widely used in clinical evaluation are burdened with a high error rate and are characterized by low accuracy. On the other hand, new digital approaches appear in medicine more and more often, which allow the increase of the accuracy of these estimates, and thus equip doctors with a tool for reliable estimation of the chronological age of children and adolescents. In this study, the work on a fast and effective metamodel is continued. Metamodels have one great advantage over all other analog and quasidigital methods—if they are well trained, a priori, on a representative set of samples, then in the age-assessment phase, results are obtained in a fraction of a second and with little error (reduced to ±7.5 months). In the here-proposed method, the standard deviation for each estimate is additionally obtained, which allows the assessment of the certainty of each result. In this study, 619 pantomographic photos of 619 patients (296 girls and 323 boys) of different ages were used. In the numerical procedure, on the other hand, a metamodel based on the Proper Orthogonal Decomposition (POD) and Gaussian Processes (GP) were utilized. The accuracy of the trained model was up to 95%.

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Section: Discussionmentioning

confidence: 85%

1.0 month was obtained, while the same error for test set obtained with the use of deep learning neural networks was MAE = 10.0 months.…”

Section: Discussionmentioning

confidence: 85%

Robust Estimation of the Chronological Age of Children and Adolescents Using Tooth Geometry Indicators and POD-GP

Zaborowicz

Garbowski

Biedziak

et al. 2022

IJERPH

Self Cite

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“…Studies classifying child samples [64] into Stages D to H of the Demirjian developmental groups [15], achieving an accuracy of 82.50%. A new study combines the classical approach of manual measurements of indicators with the modeling capabilities of deep learning [65], achieving an error between 2.34 and 4.61 months for their child and adolescent samples.…”

Section: Related Workmentioning

confidence: 99%

A Comprehensive Exploration of Neural Networks for Forensic Analysis of Adult Single Tooth X-Ray Images

et al. 2022

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Determining the demographic characteristics of a person post-mortem is a fundamental task for forensic experts, and the dental system is a crucial source of those information. Those characteristics, namely age and sex, can reliably be determined. The mandible and individual teeth survive even the harshest conditions, making them a prime target for forensic analysis. Current methods in forensic odontology rely on time-consuming manual measurements and reference tables, many of which rely on the correct determination of the tooth type. This study thoroughly explores the applicability of deep learning for sex assessment, age estimation, and tooth type determination from x-ray images of individual teeth. A series of models that use state-of-the-art feature extraction architectures and attention have been trained and evaluated. Their hyperparameters have been explored and optimized using a combination of grid and random search, totaling over a thousand experiments and 14076 hours of GPU compute time. Our dataset contains 86495 individual tooth x-ray image samples, with a subset of 7630 images having additional information about tooth alterations. The best-performing models are fine-tuned, the impact of tooth alterations is analyzed, and model performance is compared to current methods in forensic odontology literature. We achieve an accuracy of 76.41% for sex assessment, a median absolute error of 4.94 years for age estimation, and an accuracy of 87.24% to 99.15% for tooth type determination. The constructed models are fully automated and fast, their results are reproducible, and the performance is equal to or better than current state-of-the-art methods in forensic odontology.

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“…Their goal is to enhance the efficiency and quality of delivered services. AI implementations are invisible algorithms in software tools in the majority of these processes [ 5 , 6 , 31 , 32 , 33 , 34 , 35 , 36 , 37 ]. They often overlap various dental specialties and categorizations.…”

Section: Introductionmentioning

confidence: 99%

Where Is the Artificial Intelligence Applied in Dentistry? Systematic Review and Literature Analysis

et al. 2022

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This literature research had two main objectives. The first objective was to quantify how frequently artificial intelligence (AI) was utilized in dental literature from 2011 until 2021. The second objective was to distinguish the focus of such publications; in particular, dental field and topic. The main inclusion criterium was an original article or review in English focused on dental utilization of AI. All other types of publications or non-dental or non-AI-focused were excluded. The information sources were Web of Science, PubMed, Scopus, and Google Scholar, queried on 19 April 2022. The search string was “artificial intelligence” AND (dental OR dentistry OR tooth OR teeth OR dentofacial OR maxillofacial OR orofacial OR orthodontics OR endodontics OR periodontics OR prosthodontics). Following the removal of duplicates, all remaining publications were returned by searches and were screened by three independent operators to minimize the risk of bias. The analysis of 2011–2021 publications identified 4413 records, from which 1497 were finally selected and calculated according to the year of publication. The results confirmed a historically unprecedented boom in AI dental publications, with an average increase of 21.6% per year over the last decade and a 34.9% increase per year over the last 5 years. In the achievement of the second objective, qualitative assessment of dental AI publications since 2021 identified 1717 records, with 497 papers finally selected. The results of this assessment indicated the relative proportions of focal topics, as follows: radiology 26.36%, orthodontics 18.31%, general scope 17.10%, restorative 12.09%, surgery 11.87% and education 5.63%. The review confirms that the current use of artificial intelligence in dentistry is concentrated mainly around the evaluation of digital diagnostic methods, especially radiology; however, its implementation is expected to gradually penetrate all parts of the profession.

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Deep Learning Neural Modelling as a Precise Method in the Assessment of the Chronological Age of Children and Adolescents Using Tooth and Bone Parameters

Cited by 20 publications

References 49 publications

Robust Estimation of the Chronological Age of Children and Adolescents Using Tooth Geometry Indicators and POD-GP

Robust Estimation of the Chronological Age of Children and Adolescents Using Tooth Geometry Indicators and POD-GP

A Comprehensive Exploration of Neural Networks for Forensic Analysis of Adult Single Tooth X-Ray Images

Where Is the Artificial Intelligence Applied in Dentistry? Systematic Review and Literature Analysis

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