Deep focus approach for accurate bone age estimation from lateral cephalogram

Age and sex estimation in cephalometric radiographs based on multitask convolutional neural networks

OBJECTIVES

Age and sex characteristics are evident in cephalometric radiographs (CRs), yet their accurate estimation remains challenging due to the complexity of these images. This study aimed to harness deep learning to automate age and sex estimation from CRs, potentially simplifying their interpretation.

STUDY DESIGN

We compared the performance of 4 deep learning models (SVM, R-net, VGG16-SingleTask, and our proposed VGG16-MultiTask) in estimating age and sex from the testing dataset, utilizing a VGG16-based multitask deep learning model on 4,557 CRs. Gradient-weighted class activation mapping (Grad-CAM) was incorporated to identify sex. Performance was assessed using mean absolute error (MAE), specificity, sensitivity, F1 score, and the area under the curve (AUC) in receiver operating characteristic analysis.

RESULTS

The VGG16-MultiTask model outperformed the others, with the lowest MAE (0.864±1.602) and highest sensitivity (0.85), specificity (0.88), F1 score (0.863), and AUC (0.93), demonstrating superior efficacy and robust performance.

CONCLUSIONS

The VGG multitask model demonstrates significant potential in enhancing age and sex estimation from cephalometric analysis, underscoring the role of AI in improving biomedical interpretations.

Age Estimation Using Machine Learning Algorithms with Parameters Obtained from X-ray Images of the Calcaneus

BACKGROUND

Determination of bone age is a critical issue for forensics, surgery, and basic sciences.

AIM

This study aims to estimate age with high accuracy and precision using Machine Learning (ML) algorithms with parameters obtained from calcaneus x-ray images of healthy individuals.

METHOD

The study was carried out by retrospectively examining the foot X-ray images of 341 people aged 18-65 years. Maximum width of the calcaneus (MW), body width (BW), maximum length (MAXL), minimum length (MINL), facies articularis cuboidea height (FACH), maximum height (MAXH), and tuber calcanei width (TKW) parameters were measured from the images. The measurements were then grouped as 20-45 years of age, 46-64 years of age, 65 and older, and age estimation was made by using these at the input of ML models.

RESULTS

As a result of the ML input of the measurements obtained, a 0.85 Accuracy (Acc) rate was obtained with the Extra Tree Classifier algorithm. The accuracy rate of other algorithms was found to vary between 0.78 and 0.82. The contribution of parameters to the overall result was evaluated by using the shapley additive explanations (SHAP) analyzer of Random Forest algorithm and the MAXH parameter was found to have the highest contribution in age estimation.

CONCLUSIONS

As a result of our study, calcaneus bone was found to have high accuracy and precision in age estimations.

A family-centered orthodontic screening approach using a machine learning-based mobile application

Background/purpose

Skeletal orthodontic deformities can have functional and aesthetic consequences, making early detection critical. This study aimed to address the issue of parents bringing their children for routine orthodontic checkups after the ideal treatment age has passed. To address this, we developed a mobile application that uses machine-learning to make a preliminary diagnosis of skeletal malocclusion using just one photograph.

Materials and methods

A retrospective study was conducted on 524 pre-pubertal children, aged between 5 and 12 years, to evaluate the accuracy of the machine learning based mobile application. The application detects multiple points in photographs taken from the mobile camera and generates a signal indicating the diagnosis of skeletal malocclusion.

Results

The final accuracy of the Class III vs not Class III model deployed to the mobile application was above 81%, indicating its ability to accurately identify skeletal malocclusion. On a separate validation dataset of 145 patients diagnosed by 5 different clinicians, the accuracy of Class II vs Class I model was 69%; And pg 4, ln 61: as Class II vs Class I with 69% accuracy.

Conclusion

The application provides parents with important information about the orthodontic problem, age of treatment, and various treatment options. This enables parents to seek further advice from an orthodontist at an earlier stage and make informed decisions. However, the diagnosis should still be confirmed by an orthodontist. This approach has the potential to improve access to orthodontic care, especially in underserved communities.

Artificial Intelligence and Its Clinical Applications in Orthodontics: A Systematic Review

This review aims to analyze different strategies that make use of artificial intelligence to enhance diagnosis, treatment planning, and monitoring in orthodontics. Orthodontics has seen significant technological advancements with the introduction of digital equipment, including cone beam computed tomography, intraoral scanners, and software coupled to these devices. The use of deep learning in software has sped up image processing processes. Deep learning is an artificial intelligence technology that trains computers to analyze data like the human brain does. Deep learning models are capable of recognizing complex patterns in photos, text, audio, and other data to generate accurate information and predictions.

MATERIALS AND METHODS

Pubmed, Scopus, and Web of Science were used to discover publications from 1 January 2013 to 18 October 2023 that matched our topic. A comparison of various artificial intelligence applications in orthodontics was generated.

RESULTS

A final number of 33 studies were included in the review for qualitative analysis.

CONCLUSIONS

These studies demonstrate the effectiveness of AI in enhancing orthodontic diagnosis, treatment planning, and assessment. A lot of articles emphasize the integration of artificial intelligence into orthodontics and its potential to revolutionize treatment monitoring, evaluation, and patient outcomes.

Third Molar Eruption in Dental Panoramic Radiographs as a Feature for Forensic Age Assessment-Presentation of a New Non-Staging Method Based on Measurements

The evaluation of third molar eruption in dental panoramic radiographs (DPRs) constitutes an evidence-based approach for forensic age assessment in living individuals. Existing methodologies involve staging morphological radiographic findings and comparing them to reference populations. Conversely, the existing literature presents an alternative method where the distance between third molars and the occlusal plane is measured on dental plaster models. The aim of this study was to adapt this measurement principle for DPRs and to determine correlation between eruption and chronological age. A total of 423 DPRs, encompassing 220 females and 203 males aged 15 to 25 years, were examined, including teeth 38 [FDI] and 48. Two independent examiners conducted the measurements, with one examiner providing dual assessments. Ultimately, a quotient was derived by comparing orthogonal distances from the mesial cementoenamel junctions of the second and third molars to a simplified radiological occlusal plane. This quotient was subsequently correlated with the individual's age. We estimated correlations between age and quotients, as well as inter- and intra-rater reliability. Correlation coefficients (Spearman's rho) between measurements and individuals' ages ranged from 0.555 to 0.597, conditional on sex and tooth. Intra-rater agreement (Krippendorf's alpha) ranged from 0.932 to 0.991, varying according to the tooth and sex. Inter-rater agreement ranged from 0.984 to 0.992, with distinctions drawn for different teeth and sex. Notably, all observer agreement values fell within the "very good" range. In summary, assessing the distance of third molars from a simplified occlusal plane in DPRs emerges as a new and promising method for evaluating eruption status in forensic age assessment. Subsequent reference studies should validate these findings.

Cervical Vertebral Maturation Assessment using various Machine Learning techniques on Lateral cephalogram: A systematic literature review

Importance

For the assessment of optimum treatment timing in dentofacial orthopedics, understanding the growth process is of paramount importance. The evaluation of skeletal maturity based on study of the morphology of the cervical vertebrae has been devised to minimize radiation exposure of a patient due to hand wrist radiography. Cervical vertebral maturation assessment (CVMA) predictions have been examined in the state-of-the-art machine learning techniques in the recent past which require more attention and validation by clinicians and practitioners.

Objectives

This paper aimed to answer the question "How are machine learning techniques being employed in studies concerning cervical vertebral maturation assessment using lateral cephalograms?"

Method

A systematic search through the available literature was performed for this work based upon the Population, Intervention, Comparison and Outcome (PICO) framework.

Data sources study selection data extraction and synthesis

The searches were performed in Ovid Medline, Embase, PubMed and Cochrane Central Register of Controlled Trials (CENTRAL) and Cochrane Database of Systematic Reviews (CDSR). A search of the grey literature was also performed in Google Scholar and OpenGrey. We also did a hand-searching in the Angle Orthodontist, Journal of Orthodontics and Craniofacial Research, Progress in Orthodontics, and the American Journal of Orthodontics and Dentofacial Orthopedics. References from the included articles were also searched.

Main outcomes and measures results

A total of 25 papers which were assessed for full text, and 13 papers were included for the systematic review. The machine learning methods used were scrutinized according to their performance and comparison to human observers/experts. The accuracy of the models ranged between 60 and 90% or above, and satisfactory agreement and correlation with the human observers.

Conclusions and relevance

Machine learning models can be used for detection and classification of the cervical vertebrae maturation. In this systematic review (SR), the studies were summarized in terms of ML techniques applied, sample data, age range of sample and conventional method for CVMA, which showed that further studies with a uniform distribution of samples equally in stages of maturation and according to the gender is required for better training of the models in order to generalize the outputs for prolific use to target population.

Artificial intelligence in forensic medicine and forensic dentistry

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.