Age estimation is a key component in forensic analysis, be it in legal proceedings or archeological research. Current methods in forensic odontology are based on manual measurements of a wide array ...of morphometric parameters, typically from dental x-ray images, and occasionally from material remains. While those parameters follow a set progression during human development, thereby allowing current methods to precisely estimate the age of juveniles, estimation for adults and seniors proves to be more difficult. In this study, we explore the applicability of deep learning to the problem of chronological age estimation. We determine the best convolutional neural network model derived from state-of-the-art architectures, we determine the best performing model parameters using pretrained general-purpose vision model parameters as the starting point, and we perform ablation experiments to highlight which anatomical regions of the dental system contribute the most to the estimation. The proposed approach attains the lowest estimation error in literature for adult and senior subjects, which we verify on one of the largest datasets of panoramic dental x-ray images in literature. The dataset consists of 4035 panoramic dental x-ray images of male and female subjects with ages between 19 and 90 years. This study also evaluates the feasibility of the proposed model for age estimations of individual teeth, achieving an estimation error competitive with current methods while being fully automated. The estimation error is verified on our dataset of 76416 individual tooth images, which is the largest dataset to date in forensic odontology literature. Unlike current methods, dental alterations, decay, illnesses, or missing teeth do not pose a problem to the proposed model. With a median estimation error of 2.95 years for panoramic dental x-ray images and 4.68 years for individual teeth, and by deriving the model from state-of-the-art architectures, verifying those results on the largest dataset in forensic odontology literature and demonstrating the importance of different anatomical regions of the dental system for estimation, this study sets the baseline for future research of automated chronological age estimation in forensic odontology.
•Age estimation of adults is a hard problem in forensic odontology.•Only manual measurement methods are currently used in practice.•This deep learning approach eclipses expert performance in a fraction of the time.•The interpretability analysis gives new insight into anatomical changes due to age.•The method is verified on the largest dataset in literature.
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.
Development of a convolutional neural network that can precisely and quickly identify teeth from x-ray images, without using neighbouring structures as a frame of reference.
Using a database of 11403 ...x-ray images that were precisely annotated by dental professionals we have trained, validated and tested a convolutional neural network (CNN) that can identify teeth according to their position in the oral cavity. Four “levels” were tested, the first one being classification according to the type of the tooth morphologically. This consisted of 4 categories: incisor, canine, premolar and molar. The second “level” added the differentiation between types of incisors, premolars and molars. This “level” had 8 categories, imitating a dental quadrant. The third “level” added maxillary or mandibular origin and a total of 16 categories. Finally, the fourth “level” had 32 categories, meaning every tooth had its own.
The first level offered an 97.83% accuracy on unseen data. The second level offered 92.13%. “Level” three offered 91.14%. The fourth level, while being the most demanding, offered a 91.13%.
The results were the best in the 4 category “level” and the least successful in the 32 category “level”. Interestingly, the difference between the 32 and 16 category level was not significant at all. The developed CNN can identify the morphological type of the tooth with a very high accuracy rate. This opens a door into implementation of artificial intelligence in rapid analysis and cross referencing in (forensic) dental medicine.
This study has been supported as a part of the Croatian Science Foundation under the project IP-2020-02-9423.
Artificial intelligence has been applied in various fields throughout history, but its integration into daily life is more recent. The first applications of AI were primarily in academia and ...government research institutions, but as technology has advanced, AI has also been applied in industry, commerce, medicine and dentistry.
Considering that the possibilities of applying artificial intelligence are developing rapidly and that this field is one of the areas with the greatest increase in the number of newly published articles, the aim of this paper was to provide an overview of the literature and to give an insight into the possibilities of applying artificial intelligence in medicine and dentistry. In addition, the aim was to discuss its advantages and disadvantages.
The possibilities of applying artificial intelligence to medicine and dentistry are just being discovered. Artificial intelligence will greatly contribute to developments in medicine and dentistry, as it is a tool that enables development and progress, especially in terms of personalized healthcare that will lead to much better treatment outcomes.
Introduction: Artificial intelligence has been applied in various fields throughout history, but its integration into daily life is more recent. The first applications of AI were primarily in ...academia and government research institutions, but as technology has advanced, AI has also been applied in industry, commerce, medicine and dentistry. Objective: Considering that the possibilities of applying artificial intelligence are developing rapidly and that this field is one of the areas with the greatest increase in the number of newly published articles, the aim of this paper was to provide an overview of the literature and to give an insight into the possibilities of applying artificial intelligence in medicine and dentistry. In addition, the aim was to discuss its advantages and disadvantages. Conclusion: The possibilities of applying artificial intelligence to medicine and dentistry are just being discovered. Artificial intelligence will greatly contribute to developments in medicine and dentistry, as it is a tool that enables development and progress, especially in terms of personalized healthcare that will lead to much better treatment outcomes. Uvod: Umjetna inteligencija (UI) primjenjivala se u proslosti u razlicitim podrucjima, no njezina integracija u svakodnevni zivot novija je pojava. Najprije se koristila uglavnom u akademskim i vladinim istrazivackim ustanovama, no kako je tehnologija napredovala, pocela se primjenjivati u industriji, trgovini, medicini i stomatologiji. Cilj: Uzimajuci u obzir ubrzani razvoj i sirenje primjene umjetne inteligencije i zato sto je to jedno od podrucja s najvecim rastom kad je rijec o broju objavljenih clanaka, svrha ovoga rada jest dati pregled literature i uvid u mogucnosti primjene umjetne inteligencije u podrucju medicine i stomatologije, osobito s naglaskom na prednosti i nedostatke. Zakljucak: Mogucnosti primjene umjetne inteligencije u medicini i stomatologiji tek se otkrivaju. Umjetna inteligencija vazan je dio buduceg razvoja medicine i stomatologije jer je to orude koje osigurava razvoj i napredak, osobito kad je rijec o individualiziranoj zdravstvenoj skrbi koja obecava znacajno poboljsane ishode lijecenja.
This review article aims to highlight the current possibilities for applying Artificial Intelligence in modern forensic medicine and forensic dentistry and present the advantages and disadvantages of ...its use. For this purpose, the relevant academic literature was searched using PubMed, Web of Science and Scopus. The application of Artificial Intelligence in forensic medicine and forensic dentistry is still in its early stages. However, the possibilities are great, and the future will show what is applicable in daily practice. Artificial Intelligence will improve the accuracy and efficiency of work in forensic medicine and forensic dentistry; it can automate some tasks; and enhance the quality of evidence. Disadvantages of the application of Artificial Intelligence may be related to discrimination, transparency, accountability, privacy, security, ethics and others. Artificial Intelligence systems should be used as a support tool, not as a replacement for forensic experts.
Identifying the gender of a person is one of the fundamental tasks in forensic medicine. One possible application is right after a catastrophic event such as a mass disaster with a high victim count. ...In such cases it is necessary to identify the people involved which can require a high number of forensic experts, depending on the scale of the event. With panoramic dental x-ray images the biological gender of a person can be estimated by analyzing skeletal structures that express sexual dimorphism. Current methods require the manual measurement of a wide array of mandibular parameters which are then manually compared to references based on these measurements and assumed ethnicity of the people involved. We propose an automated solution based on deep learning techniques using convolutional neural networks. Our data consists of 4000 panoramic dental x-ray images of patients with European origin, with the images being taken by a wide range of orthopantomographs. Our automated method can estimate 64 images per second on contemporary hardware, it doesn't require human intervention for estimation and it achieves state-of-the-art results with an accuracy of 96.87% ± 0.96%.
Sex assessment is an important step of the forensic process. Dental remains are often the only remains left to examine due to their resistance to decay and external factors. Contemporary forensic ...odontology literature describes multiple methods for sex assessment from mandibular parameters, all of which require manual measurements and expert training. This study aims to explore the applicability of deep learning and image analysis methods to automate this task, thus allowing for easier reproducibility of assessments, reduction of the time experts lose on repetitive tasks, and potentially better performance. We have evaluated state-of-the-art deep learning models and components on the largest dataset of individual adult tooth x-ray images, consisting of 76293 samples. This study also explores the usage of decayed or structurally altered teeth, with which contemporary methods struggle. Two types of models are constructed, a family of models specialized for specific tooth types, and a general model that can assess the sex from any tooth type. We examine the performance of those models per tooth type and age group, as well as the impact of decayed and structurally altered teeth. The specialized models achieve an overall accuracy of 72.40%, and the general model reaches an overall accuracy of 72.68%.
One of the primary steps in forensic dental analysis is age estimation. Alongside sex estimation, this is offers basic categorization of subjects. Whether it is used in person-identification or ...archaeological analysis and research, a forensic dentist will observe these parameters when starting his work. Orthopantomographic x-ray images offer a lot of data and basically represent the golden standard for identification in forensic stomatology. Deep convolutional neural networks are establishing their presence in numerous fields of medicine and therefore we have explored the possibility of their implementation in age estimation in forensic dentistry. We developed a deep convolutional neural network, based on a dataset of 4035 orthopantomographic images, captured by and kindly provided by University of Zagreb’s, School of Dental medicine. A quick, automated and accurate model was formed that opens a new door in the field of forensic dentistry. The developed convolutional neural network was used to estimate the age of 89 archaeological skull remains. The skulls were scanned with an orthopantomography x-ray machine and the received images were used as a testing dataset. The results offered a noteworthy 73% accuracy of placing the images in correct age groups.
This paper discusses the importance of adopting and applying new technologies in scientific fields to increase the rate of progress. It emphasises the need for networking and multidisciplinary ...collaboration to apply technologies developed for other purposes to solve scientific or professional issues. The paper reviews modern technologies used in archaeology and bioarchaeology, including ground penetrating radar, LiDAR, drones, 3D printing, remote sensing, GIS, and portable X-ray fluorescence. It also presents modern technologies in bioarchaeology such as DNA analysis, stable isotope analysis, radiocarbon dating, microscopic analysis, CT and MRI, and proteomics. The paper introduces palaeoradiology, a branch of radiology that uses imaging technologies to examine bioarchaeological or even archaeological material, and discusses its importance in gaining knowledge about the health, lifestyle, and causes of death of past populations.
PDF
