Automated Sex Assessment of Individual Adult Tooth X-Ray Images

Milošević, Denis; Vodanović, Marin; Galić, Ivan; Subašić, Marko

doi:10.1109/ispa52656.2021.9552124

Cited by 6 publications

(7 citation statements)

References 30 publications

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“…A multiple feature fusion model was later developed, achieving an accuracy of 94.6% ± 0.58% on their dataset of 19976 panoramic dental x-ray images [67]. Our initial study for the assessment of sex based on individual tooth x-ray images [68] achieves an accuracy of 72.68% on all samples, and preliminary results on a small subset of clinically perfect teeth achieved an accuracy of 85% with the same model. With a sample size of 1000 panoramic dental x-ray images, a study employing ResNet50 [69] and denoising claims an accuracy of 98.27% [70].…”

Section: Related Workmentioning

confidence: 99%

“…Additionally, in contrast to [2], this study not only performs hyperparameter tuning specifically for individual tooth xray images but also takes into account age estimation of imperfect, damaged, or otherwise altered teeth and provides a detailed analysis of the impact tooth alterations have on age estimation performance. For sex assessment, our preliminary study has been extended with the evaluation of a broader range of models with more experiments [68].…”

Section: Related Workmentioning

confidence: 99%

“…In our initial studies [1]- [3], [68] some form of hyperparameter search was done, either directly or indirectly. The knowledge about the search space and sensible parameter ranges were learned from our initial study on sex assessment from panoramic dental x-ray images [1].…”

Section: Hyperparameter Searchmentioning

confidence: 99%

“…For age estimation, in addition to running grid search over the entire search space like in [2] for individual teeth x-rays only, we have evaluated an additional 381 experiments with random search. For sex assessment, we have evaluated an additional 256 experiments with grid search to complete the random search we have done in [68]. Tooth type determination in [3] was done on a smaller dataset, and the search consisted of 64 experiments.…”

Section: Hyperparameter Searchmentioning

confidence: 99%

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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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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Hyperparameter Searchmentioning

confidence: 99%

Section: Hyperparameter Searchmentioning

confidence: 99%

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

et al. 2022

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“…Another DL approach applied to the panoramic radiographs has been proposed by Milošević et al (2021), in which several pre-trained models were tested: DenseNet201 (Huang et al, 2017), InceptionResNetV2 (Szegedy et al, 2017), ResNet50 (He et al, 2016), VGG16, VGG19 (Simonyan and Zisserman, 2015), and Xception (Chollet, 2017). Hyperparameter tuning was done to determine the best DL model for the study.…”

Section: Sex Determinationmentioning

confidence: 99%

Applications of contemporary artificial intelligence technology in forensic odontology as primary forensic identifier: A scoping review

Mohammad

Ahmad

Kurniawan

et al. 2022

Front. Artif. Intell.

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BackgroundForensic odontology may require a visual or clinical method during identification. Sometimes it may require forensic experts to refer to the existing technique to identify individuals, for example, by using the atlas to estimate the dental age. However, the existing technology can be a complicated procedure for a large-scale incident requiring a more significant number of forensic identifications, particularly during mass disasters. This has driven many experts to perform automation in their current practice to improve efficiency.ObjectiveThis article aims to evaluate current artificial intelligence applications and discuss their performance concerning the algorithm architecture used in forensic odontology.MethodsThis study summarizes the findings of 28 research papers published between 2010 and June 2022 using the Arksey and O'Malley framework, updated by the Joanna Briggs Institute Framework for Scoping Reviews methodology, highlighting the research trend of artificial intelligence technology in forensic odontology. In addition, a literature search was conducted on Web of Science (WoS), Scopus, Google Scholar, and PubMed, and the results were evaluated based on their content and significance.ResultsThe potential application of artificial intelligence technology in forensic odontology can be categorized into four: (1) human bite marks, (2) sex determination, (3) age estimation, and (4) dental comparison. This powerful tool can solve humanity's problems by giving an adequate number of datasets, the appropriate implementation of algorithm architecture, and the proper assignment of hyperparameters that enable the model to perform the prediction at a very high level of performance.ConclusionThe reviewed articles demonstrate that machine learning techniques are reliable for studies involving continuous features such as morphometric parameters. However, machine learning models do not strictly require large training datasets to produce promising results. In contrast, deep learning enables the processing of unstructured data, such as medical images, which require large volumes of data. Occasionally, transfer learning was used to overcome the limitation of data. In the meantime, this method's capacity to automatically learn task-specific feature representations has made it a significant success in forensic odontology.

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