Bone development shows certain regularity with age. The regularity can be used to infer age and serve many fields such as justice, medicine, archaeology, etc. As a non-invasive evaluation method of the epiphyseal development stage, MRI is widely used in living age estimation. In recent years, the rapid development of machine learning has significantly improved the effectiveness and reliability of living age estimation, which is one of the main development directions of current research. This paper summarizes the analysis methods of age estimation by knee joint MRI, introduces the current research trends, and future application trend.
Epiphyses/diagnostic imaging* ; Age Determination by Skeleton/methods* ; Reproducibility of Results ; Magnetic Resonance Imaging/methods* ; Knee Joint/diagnostic imaging*

OBJECTIVES: To find the appropriate method for age estimation for different ages and sexes. METHODS: The costal cartilage, sternum and pubic symphysis of 91 unknowns from 2000 to 2020 from the Forensic Department of the Criminal Investigation Team of Shanghai Public Security Bureau were collected. Costal cartilage, sternal and pubic symphysis inferences were used to estimate the age, and the consistency between the estimated results and the actual physiological age of the unknowns was tested. The accuracy of age estimation of different samples was compared, and the relationship between accuracy and age and sex was analyzed. RESULTS: Using the costal cartilage method, the inference errors of males, females and the whole population under 40 years old were (0.608±2.298) years, (0.429±1.867) years and (0.493±2.040) years, while those over 40 years old were (-1.707±3.770) years, (-3.286±4.078) years and (-2.625±4.029) years. The differences between different age groups in these three populations were statistically significant (P<0.05). Using the sternum method, the inference errors of males and females under the age of 40 were (0.921±3.019) years and (0.452±1.451) years, while those over the age of 40 were (-5.903±5.088) years and (-1.429±2.227) years. The differences between different age groups in males and females were statistically significant (P<0.05). Using the pubic symphysis method, the inference errors of males and females under 40 years old were (-0.204±1.876) years and (0.238±2.477) years, while those over 40 years old were (1.500±2.156) years and (-2.643±4.270) years. The differences between different age groups in males and females were statistically significant (P<0.05). Using the sternum method and pubic symphysis method for age estimation of over 40 years old, the difference between different sexes was statistically significant (P<0.05). CONCLUSIONS All three methods of age estimation are stable and effective and more accurate for people under 40 years old. For age estimation of unknowns over 40 years old, the pubic symphysis method is preferred in males and the sternum method is preferred in females.
Adult ; Age Determination by Skeleton/methods* ; Child, Preschool ; China ; Female ; Forensic Anthropology/methods* ; Forensic Medicine ; Humans ; Infant ; Male ; Pubic Symphysis/anatomy & histology*

Background: The incidence rate of Angle Class I and Class II malocclusions in mixed dentition is higher than Class III. In orthodontic interceptive treatment, it is necessary to identify pubertal growth spurt peak individually because the best growth modification could be obtained during this period. One of the methods in assessing the pubertal growth spurt peak is cervical vertebrae maturation (CVM), which is done using a lateral cephalometric radiograph. CVM evaluates potential growth and skeletal maturity by assessing cervical vertebrae anatomy. Identifying the duration of growth spurt peak on both malocclusion classes is the most pivotal aspect of optimizing remodeling and correction of children’s malocclusion. Objective: Distinguishing the duration of pubertal growth spurt peak of children with Angle Class I and II malocclusions based on CVM analysis in Deutero-Malay children so that it can be used in determining optimal orthodontic treatment plan and timing in children with Class I and Angle II malocclusion for Deutero-Malay children. Methods: Analytical observational with cross-sectional approach was applied using lateral cephalometric radiographic images from patients’ medical records attending or had attended orthodontic treatment in the Pediatric Dentistry Clinic, Airlangga University Dental Hospital, Surabaya, Indonesia, in 2014-2019 that met the inclusion criteria and were analyzed with Baccetti’s method of CVM analysis. This study involved 66 conventional lateral cephalometric photographs that were selected using total sampling. The data were analyzed using Independent T-Test and Mann Whitney U Test. Result: The duration of pubertal growth spurt peak in Angle Class I and II malocclusions was 11 and 7 months, respectively. The age of onset for Class I with CS3 was 9 years and 5 months, while for Angle Class II malocclusion starts entering the stage at 10 years 3 months of age, while for CS4 skeletal maturity we found that the age of onset for subjects with Angle Class I and II were 11 years 2 months and 12 years 4 months, respectively. The average duration of the pubertal growth spurt peak in female and male patients was 11.3 months and 18.2 months, respectively. All of these results were statistically significant (p ≤ 0.001) and representative of the population, in this case, Deutero-Malays. Conclusion Four-month differences in the duration of pubertal growth spurt peak of children with Angle Class I and II were found. This may lead to a shorter treatment duration of 4 months in children with Angle Class II malocclusion when compared to children with Angle Class I malocclusion. Angle Class II malocclusion exhibit shorter pubertal growth spurt peak duration, which may account for the difference in mandibular growth on the two malocclusion classes.
Puberty ; Malocclusion ; Malocclusion, Angle Class I ; Malocclusion, Angle Class II ; Cervical Vertebrae ; Age Determination by Skeleton ; Cephalometry ; Asian People ; Age of Onset

Individual identification based on imaging data of the skeleton of a corpse is a key technique for forensic identification. To reduce the influence of artificial factors, computer-aided semi-automatic or automatic individual identification has become one of the research directions of skeleton-based individual identification in forensic radiology. Therefore, this paper reviews and summarizes literatures related to estimation of anthropological information such as, age and sex by computer-aided forensic radiology bone characteristics and individual identification based on bone imaging characteristics, in order to provide reference on skeleton-based individual identification in forensic radiology.
Age Determination by Skeleton ; Bone and Bones ; Computers ; Forensic Anthropology ; Radiology

Bone age assessment has always been one of the key issues and difficulties in forensic science. With the gradual development of machine learning in many industries, it has been widely introduced to imageology, genomics, oncology, pathology, surgery and other medical research fields in recent years. The reason why the above research fields can be closely combined with machine learning, is because the research subjects of the above branches of medicine belong to the computer vision category. Machine learning provides unique advantages for computer vision research and has made breakthroughs in medical image recognition. Based on the advantages of machine learning in image recognition, it was combined with bone age assessment research, in order to construct a recognition model suitable for forensic skeletal images. This paper reviews the research progress in bone age assessment made by scholars at home and abroad using machine learning technology in recent years.
Age Determination by Skeleton ; Humans ; Machine Learning

Bone age is an important indicator of human growth and development, which can objectively reflect the growth level and maturity of individuals. Traditional manual bone age assessment usually compares the X-ray of the left wrist with the reference standard to obtain the corresponding bone age. This method is time-consuming and its results vary with different observers. In recent years, with the continuous development of computer science, bone age assessment has began to change from traditional manual assessment to automatic assessment. Although there has already been numerous researches on automatic bone age assessment, most of them are still in the experimental stage. This paper reviews related research and progress on automatic bone age assessment at home and abroad in recent years, in order to provide reference and research ideas for relevant researchers.
Age Determination by Skeleton ; Humans ; Wrist ; X-Rays

OBJECTIVE: To explore the integration method and technical realization of artificial intelligence bone age assessment system with the hospital RIS-PACS network and workflow. METHODS: Two sets of artificial intelligence based on bone age assessment systems (CHBoneAI 1.0/2.0) were developed. The intelligent system was further integrated with RIS-PACS based on the http protocol in Python flask web framework. RESULTS: The two sets of systems were successfully integrated into the local network and RIS-PACS in hospital. The deployment has been smoothly running for nearly 3 years. Within the current network setting, it takes less than 3 s to complete bone age assessment for a single patient. CONCLUSIONS The artificial intelligence based bone age assessment system has been deployed in clinical RIS-PACS platform and the "running in parallel", which is marking a success of Stage-I and paving the way to Stage-II where the intelligent systems can evolve to become more powerful in particular of the system self-evolution and the "running alternatively".
Age Determination by Skeleton ; Artificial Intelligence ; Bone and Bones ; Hospital Information Systems ; Hospitals ; Humans ; Radiology Information Systems ; Systems Integration

One of the major tasks in the forensic field is age estimation in living individuals, especially in adolescents and young adults. The X-ray examination of left hand, panoramic radiograph and CT scan of the sternal end of clavicles are mature means that are widely used. However, the X-ray technique has great radiation on the human body, and imaging radiation for non-diagnosis and treatment purposes does not conform to the current mainstream medical ethics. MRI is nonradioactive tomographic imaging and is one of the research and development directions in forensic age estimation in living individuals now. This paper summarizes the common indicators and analysis methods of MRI in previous research of age estimation, in order to get better understanding of its trends and provide a clue for future relevant studies.
Adolescent ; Age Determination by Skeleton ; Clavicle/diagnostic imaging* ; Forensic Anthropology ; Hand ; Humans ; Magnetic Resonance Imaging ; Young Adult

Adult age determination plays an important role in individual identification, criminal investigation and social welfare. The most popular adult age determination indicators are pubic symphysis, iliac auricular surface, costal cartilage, cranial sutures, teeth, laryngeal cartilage, etc. In recent years, with the progress of CT imaging and 3D reconstruction technology, the adult age determination study gradually has transferred from a time-consuming general observation of bones with complex pre-processing in the past to the non-destructive, convenient, time-saving and easy to store image analysis technology. To explore more accurate, rapid and convenient adult age determination methods, multiple imaging methods and artificial intelligence have been applied in adult age determination. This paper reviews the common methods and research progress of adult age determination at home and abroad, infers the development direction of adult age determination, in order to provide reference for the improvement and optimization of forensic adult age determination.
Age Determination by Skeleton ; Artificial Intelligence ; Forensic Anthropology ; Imaging, Three-Dimensional ; Pubic Symphysis/anatomy & histology* ; Research

Objective To compare the performance of three deep-learning models （VGG19, Inception-V3 and Inception-ResNet-V2） in automatic bone age assessment based on pelvic X-ray radiographs. Methods A total of 962 pelvic X ray radiographs taken from adolescents （481 males, 481 females） aged from 11.0 to 21.0 years in five provinces and cities of China were collected, preprocessed and used as objects of study. Eighty percent of these X ray radiographs were divided into training set and validation set with random sampling method and used for model fitting and hyper-parameters adjustment. Twenty percent were used as test sets, to evaluate the ability of model generalization. The performances of the three models were assessed by comparing the root mean square error （RMSE）, mean absolute error （MAE） and Bland-Altman plots between the model estimates and the chronological ages. Results The mean RMSE and MAE between bone age estimates of the VGG19 model and the chronological ages were 1.29 and 1.02 years, respectively. The mean RMSE and MAE between bone age estimates of the Inception-V3 model and the chronological ages were 1.17 and 0.82 years, respectively. The mean RMSE and MAE between bone age estimates of the Inception-ResNet-V2 model and the chronological ages were 1.11 and 0.84 years, respectively. The Bland-Altman plots showed that the mean value of differences between bone age estimates of Inception-ResNet-V2 model and the chronological ages was the lowest. Conclusion In the automatic bone age assessment of adolescent pelvis, the Inception-ResNet-V2 model performs the best while the Inception-V3 model achieves a similar accuracy as VGG19 model.
Adolescent ; Adult ; Age Determination by Skeleton ; Child ; China ; Female ; Humans ; Male ; Pelvis ; Radiography ; Young Adult