New software for cervical vertebral geometry assessment and its relationship to skeletal maturation--a pilot study

Mapping an intelligent algorithm for predicting female adolescents' cervical vertebrae maturation stage with high recall and accuracy

BACKGROUNDS AND OBJECTIVES

The present study was designed to define a novel algorithm capable of predicting female adolescents' cervical vertebrae maturation stage with high recall and accuracy.

METHODS

A total of 560 female cephalograms were collected, and cephalograms with unclear vertebral shapes and deformed scales were removed. 480 films from female adolescents (mean age: 11.5 years; age range: 6-19 years) were used for the model development phase, and 80 subjects were randomly and stratified allocated to the validation cohort to further assess the model's performance. Derived significant predictive parameters from 15 anatomic points and 25 quantitative parameters of the second to fourth cervical vertebrae (C2-C4) to establish the ordinary logistic regression model. Evaluation metrics including precision, recall, and F1 score are employed to assess the efficacy of the models in each identified cervical vertebrae maturation stage (iCS). In cases of confusion and mispredictions, the model underwent modification to improve consistency.

RESULTS

Four significant parameters, including chronological age, the ratio of D3 to AH3 (D3:AH3), anterosuperior angle of C4 (@4), and distance between C3lp and C4up (C3lp-C4up) were administered into the ordinary regression model. The primary predicting model that implements the novel algorithm was built and the performance evaluation with all stages of 93.96% for accuracy, 93.98% for precision, 93.98% for recall, and 93.95% for F1-score were obtained. Despite the hybrid logistic-based model achieving high accuracy, the unsatisfactory performance of stage estimation was noticed for iCS3 in the primary cohort (89.17%) and validation cohort (85.00%). Through bivariate logistic regression analysis, the posterior height of C4 (PH4) was further selected in the iCS3 to establish a corrected model, thus the evaluation metrics were upgraded to 95.83% and 90.00%, respectively.

CONCLUSIONS

An unbiased and objective assessment of the cervical vertebrae maturation (CVM) method can function as a decision-support tool, assisting in the evaluation of the optimal timing for treatment in growing adults. Our novel proposed logistic model yielded individual formulas for each specific CVM stage and attained exceptional performance, indicating the capability to function as a benchmark for maturity evaluation in clinical craniofacial orthopedics for Chinese female adolescents.

Use of artificial intelligence in determination of bone age of the healthy individuals: A scoping review

BACKGROUND

Bone age assessment, as an indicator of biological age, is widely used in orthodontics and pediatric endocrinology. Owing to significant subject variations in the manual method of assessment, artificial intelligence (AI), machine learning (ML), and deep learning (DL) play a significant role in this aspect. A scoping review was conducted to search the existing literature on the role of AI, ML, and DL in skeletal age or bone age assessment in healthy individuals.

METHODS

A literature search was conducted in PubMed, Scopus, and Web of Science from January 2012 to December 2022 using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses-Extension for Scoping Reviews (PRISMA-ScR) and Joanna Briggs Institute guidelines. Grey literature was searched using Google Scholar and OpenGrey. Hand-searching of the articles in all the reputed orthodontic journals and the references of the included articles were also searched for relevant articles for the present scoping review.

RESULTS

Nineteen articles that fulfilled the inclusion criteria were included. Ten studies used skeletal maturity indicators based on hand and wrist radiographs, two studies used magnetic resonance imaging and seven studies used cervical vertebrae maturity indicators based on lateral cephalograms to assess the skeletal age of the individuals. Most of these studies were published in non-orthodontic medical journals. BoneXpert automated software was the most commonly used software, followed by DL models, and ML models in the studies for assessment of bone age. The automated method was found to be as reliable as the manual method for assessment.

CONCLUSIONS

This scoping review validated the use of AI, ML, or DL in bone age assessment of individuals. A more uniform distribution of sufficient samples in different stages of maturation, use of three-dimensional inputs such as magnetic resonance imaging, and cone beam computed tomography is required for better training of the models to generalize the outputs for use in the target population.

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.

Using artificial intelligence models to evaluate envisaged points initially: A pilot study

The morphology of the finger bones in hand-wrist radiographs (HWRs) can be considered as a radiological skeletal maturity indicator, along with the other indicators. This study aims to validate the anatomical landmarks envisaged to be used for classification of the morphology of the phalanges, by developing classical neural network (NN) classifiers based on a sub-dataset of 136 HWRs. A web-based tool was developed and 22 anatomical landmarks were labeled on four region of interests (proximal (PP3), medial (MP3), distal (DP3) phalanges of the third and medial phalanx (MP5) of the fifth finger) and the epiphysis-diaphysis relationships were saved as "narrow,""equal,""capping" or "fusion" by three observers. In each region, 18 ratios and 15 angles were extracted using anatomical points. The data set is analyzed by developing two NN classifiers, without (NN-1) and with (NN-2) the 5-fold cross-validation. The performance of the models was evaluated with percentage of agreement, Cohen's (cκ) and Weighted (wκ) Kappa coefficients, precision, recall, F1-score and accuracy (statistically significance: p < 0.05). Method error was found to be in the range of cκ: 0.7-1. Overall classification performance of the models was changed between 82.14% and 89.29%. On average, performance of the NN-1 and NN-2 models were found to be 85.71% and 85.52%, respectively. The cκ and wκ of the NN-1 model were changed between -0.08 (p > 0.05) and 0.91 among regions. The average performance was found to be promising except the regions without adequate samples and the anatomical points are validated to be used in the future studies, initially.

Bone age assessment from lateral cephalograms using deep learning algorithms in the Indian population

Purpose

The assessment of bone age has applications in a wide variety of fields: from orthodontics to immigration. The traditional non-automated methods are time-consuming and subject to inter- and intra-observer variability. This is the first study of its kind done on the Indian population. In this study, we analyse different pre-processing techniques and architectures to determine the degree of maturation (i.e. cervical vertebral maturation [CVM]) from cephalometric radiographs using machine learning algorithms.

Methods

Cephalometric radiographs-labelled with the correct CVM stage using Baccetti et al. method-from 383 individuals aged between 10 and 36 years were used in the study. Data expansion and in-place data augmentation were used to handle high data imbalances. Different pre-processing techniques like Sobel filters and canny edge detectors were employed. Several deep learning convolutional neural network (CNN) architectures along with numerous pre-trained models like ResNet-50 and VGG-19 were analysed for their efficacy on the dataset.

Results

Models with 6 and 8 convolutional layers trained on 64 × 64-size grayscale images trained the fastest and achieved the highest accuracy of 94%. Pre-trained ResNet-50 with the first 49 layers frozen and VGG-19 with 10 layers frozen to training had remarkable performances on the dataset with accuracies of 91% and 89%, respectively.

Conclusions

Custom deep CNN models with 6-8 layers on 64 × 64-sized greyscale images were successfully used to achieve high accuracies to classify the majority classes. This study is a launchpad in the development of an automated method for bone age assessment from lateral cephalograms for clinical purposes.

Estimating Skeletal Maturity by Segmented Linear Modeling of Key AP Knee Radiographic Parameters

BACKGROUND

The recently described Modified Fels knee system allows for accurate skeletal maturity estimation using a single anteroposterior knee radiograph but requires evaluation of 7 parameters. A faster method may have clinical utility in the outpatient setting.

METHODS

Seven anteroposterior knee radiographic parameters associated with 90% of the final height (an enhanced skeletal maturity standard compared with peak height velocity) were analyzed in 78 children. Segmented linear regression and generalized estimating equation analyses were used to identify the subsets of parameters most important for accurate skeletal maturity estimation for different patient demographics and parameter scores. This process produced abbreviated skeletal maturity systems, which include fewer parameters and are quicker to use. The accuracy of the resulting abbreviated skeletal maturity systems was evaluated and compared with the full 7-parameter Modified Fels knee system and with the Greulich and Pyle (GP) left-hand bone age.

RESULTS

A total of 326 left knee radiographs from 41 girls (range, 7 to 15 y) and 37 boys (range, 9 to 17 y) were included. Models generated by segmented regression and generalized estimating equation analysis required fewer parameters (range, 1 to 5 parameters) than the full Modified Fels knee system (7 parameters). Skeletal age estimates produced by segmented regression models were more accurate than GP (P<0.05) and not significantly different from the full Modified Fels system (P>0.05). The percentage of outlier estimations (estimations >1 y off from actual skeletal age) made by segmented regression models was not significantly different from GP (P>0.05) or the Modified Fels knee system (P>0.05).

CONCLUSION

An abbreviated version of the Modified Fels knee system estimates skeletal maturity more accurately than the GP system with just 2 to 3 radiographic knee parameters.

CLINICAL RELEVANCE

The abbreviated Modified Fels knee system may allow for rapid skeletal age estimation (~30 s) appropriate for routine outpatient practice.

Development of a multi-stage model for intelligent and quantitative appraising of skeletal maturity using cervical vertebras cone-beam CT images of Chinese girls

PURPOSE

Nowadays, the integration of Artificial intelligence algorithms and quantified radiographic imaging-based diagnostic procedures is hailing amplified deliberation particularly in assessment of skeletal maturity. So we intend to formulate a logistic regression model for intelligent and quantitative estimation of Fishman skeletal maturation index (SMI) based on the parameters attained from the cervical vertebrae CBCT images of Chinese girls.

METHODS

From 709 hand wrist radiographs and CBCT images, 447 samples were randomly selected (called as G1) to build a logistic regression model. The reliability and reproducibility were assessed by the intraclass correlation coefficient (ICC) and weighted Cohen's kappa, followed by Spearman's rank correlation coefficient to identify the parameters significantly associated with the SMI. Two hundred and sixty-two other subjects (named G2) were recruited for external examination of the models by direct visual comparison and the receiver operating characteristic (ROC) curve. In cases of confusion and mispredictions, the model was modified to improve the consistency.

RESULTS

Five significant parameters (Chronological age, C3 height (H3)[Formula: see text], C4 upper width (UW4), C4 lower width (LW4), and the ratio of posterior height to lower width of C4 ([Formula: see text]) were administered into logistic regression model. Despite total agreement percentage which was 84% (total AUC = 0.92), unsatisfactory performance was noticed for the 6th and 8th stages which were confused with their neighboring stages. After adjustments of the models, the total agreement percentage and AUC were upgraded to 88% and 0.96, respectively.

CONCLUSION

Consistency and fitness evaluation of our models demonstrated adequate prediction percentage and reliability for automated classification of skeletal maturation. The presented constructed logistic regression model has the potential to serve as a maturity evaluation index in clinical craniofacial orthopedics in Chinese girls. The proposed model in this study showed promising strength for being expended in the event of other clinical multi-stage conditions.

Evaluation of the Artificial Neural Network and Naive Bayes Models Trained with Vertebra Ratios for Growth and Development Determination

Objective

This study aimed to evaluate the success rates of the artificial neural network models (NNMs) and naive Bayes models (NBMs) trained with various cervical vertebra ratios in cephalometric radiographs for determining growth and development.

Methods

Our retrospective study was performed on 360 individuals between the ages of 8 and 17 years, whose cephalometric radiographs were taken. According to the evaluation of cephalometric radiographs, growth and development periods were divided into 6 vertebral stages. Each stage was considered as a group, each group had 30 girls and 30 boys. Twenty-eight cervical vertebral ratios were obtained by using 10 horizontal and 13 vertical measurements. These 28 vertebral ratios were combined in 4 different combinations, leading to 4 different datasets. Each dataset was split into 2 parts as training and testing. To prevent the overfitting, a 5-cross fold validation technique was also used in the training phase. The experiments were conducted on 2 different train/test ratios as 80%-20% and 70%-30% for both NNMs and NBMs.

Results

The highest determination success rate was obtained in NNM 3 (0.95) and the lowest in NBM 4 (0.50). The determination success of NBM 1 and NBM 3 was almost similar (0.60). The success of NNM 2 did not differ much from that of NNM 1 (0.94). The determination success of stage 5 was relatively lower than the others in NNM 1 and NNM 2 (0.83).

Conclusion

The NNMs were more successful than the NBMs in our developed models. It is important to determine the effective ratio and/or measurements that will be useful for differentiation.

Validation of cervical vertebral maturation stages: Artificial intelligence vs human observer visual analysis

INTRODUCTION

This study aimed to develop an artificial neural network (ANN) model for cervical vertebral maturation (CVM) analysis and validate the model's output with the results of human observers.

METHODS

A total of 647 lateral cephalograms were selected from patients with 10-30 years of chronological age (mean ± standard deviation, 15.36 ± 4.13 years). New software with a decision support system was developed for manual labeling of the dataset. A total of 26 points were marked on each radiograph. The CVM stages were saved on the basis of the final decision of the observer. Fifty-four image features were saved in text format. A new subset of 72 radiographs was created according to the classification result, and these 72 radiographs were visually evaluated by 4 observers. Weighted kappa (wκ) and Cohen's kappa (cκ) coefficients and percentage agreement were calculated to evaluate the compatibility of the results.

RESULTS

Intraobserver agreement ranges were as follows: wκ = 0.92-0.98, cκ = 0.65-0.85, and 70.8%-87.5%. Interobserver agreement ranges were as follows: wκ = 0.76-0.92, cκ = 0.4-0.65, and 50%-72.2%. Agreement between the ANN model and observers 1, 2, 3, and 4 were as follows: wκ = 0.85 (cκ = 0.52, 59.7%), wκ = 0.8 (cκ = 0.4, 50%), wκ = 0.87 (cκ = 0.55, 62.5%), and wκ = 0.91 (cκ = 0.53, 61.1%), respectively (P <0.001). An average of 58.3% agreement was observed between the ANN model and the human observers.

CONCLUSIONS

This study demonstrated that the developed ANN model performed close to, if not better than, human observers in CVM analysis. By generating new algorithms, automatic classification of CVM with artificial intelligence may replace conventional evaluation methods used in the future.

Cervical vertebral maturation assessment on lateral cephalometric radiographs using artificial intelligence: comparison of machine learning classifier models

OBJECTIVES

This study aimed to develop five different supervised machine learning (ML) classifier models using artificial intelligence (AI) techniques and to compare their performance for cervical vertebral maturation (CVM) analysis. A clinical decision support system (CDSS) was developed for more objective results.

METHODS

A total of 647 digital lateral cephalometric radiographs with visible C2, C3, C4 and C5 vertebrae were chosen. Newly developed software was used for manually labelling the samples, with the integrated CDSS developed by evaluation of 100 radiographs. On each radiograph, 26 points were marked, and the CDSS generated a suggestion according to the points and CVM analysis performed by the human observer. For each sample, 54 features were saved in text format and classified using logistic regression (LR), support vector machine, random forest, artificial neural network (ANN) and decision tree (DT) models. The weighted κ coefficient was used to evaluate the concordance of classification and expert visual evaluation results.

RESULTS

Among the CVM stage classifier models, the best result was achieved using the ANN model (κ = 0.926). Among cervical vertebrae morphology classifier models, the best result was achieved using the LR model (κ = 0.968) for the presence of concavity, and the DT model (κ = 0.949) for vertebral body shapes.

CONCLUSIONS

This study has proposed ML models for CVM assessment on lateral cephalometric radiographs, which can be used for the prediction of cervical vertebrae morphology. Further studies should be done especially of forensic applications of AI models through CVM evaluations.

Deep Learning and Artificial Intelligence for the Determination of the Cervical Vertebra Maturation Degree from Lateral Radiography

Deep Learning (DL) and Artificial Intelligence (AI) tools have shown great success in different areas of medical diagnostics. In this paper, we show another success in orthodontics. In orthodontics, the right treatment timing of many actions and operations is crucial because many environmental and genetic conditions may modify jaw growth. The stage of growth is related to the Cervical Vertebra Maturation (CVM) degree. Thus, determining the CVM to determine the suitable timing of the treatment is important. In orthodontics, lateral X-ray radiography is used to determine it. Many classical methods need knowledge and time to look and identify some features. Nowadays, ML and AI tools are used for many medical and biological diagnostic imaging. This paper reports on the development of a Deep Learning (DL) Convolutional Neural Network (CNN) method to determine (directly from images) the degree of maturation of CVM classified in six degrees. The results show the performances of the proposed method in different contexts with different number of images for training, evaluation and testing and different pre-processing of these images. The proposed model and method are validated by cross validation. The implemented software is almost ready for use by orthodontists.

Computer Based Assessment of Cervical Vertebral Maturation Stages Using Digital Lateral Cephalograms

Objective:

This study was aimed to investigate the reliability of a computer application for assessment of the stages of cervical vertebra maturation in order to determine the stage of skeletal maturity.

Material and methods:

For this study, digital lateral cephalograms of 99 subjects (52 females and 47 males) were examined. The following selection criteria were used during the sample composition: age between 9 and 16 years, absence of anomalies of the vertebrae, good general health, no history of trauma at the cervical region. Subjects with lateral cephalograms of low quality were excluded from the study. For the purpose of this study a computer application Cephalometar HF V1 was developed. This application was used to mark the contours of the second, third and fourth cervical vertebrae on the digital lateral cephalograms, which enabled a computer to determine the stage of cervical vertebral maturation. The assessment of the stages of cervical vertebral maturation was carried out by an experienced orthodontist. The assessment was conducted according to the principles of the method proposed by authors Hassel and Farman. The degree of the agreement between the computer application and the researcher was analyzed using by statistical Cohen Kappa test.

Results:

The results of this study showed the agreement between the computer assessment and the researcher assessment of the cervical vertebral maturation stages, where the value of the Cohen Kappa coefficient was 0.985.

Conclusion:

The computer application Cephalometar HF V1 proved to be a reliable method for assessing the stages of cervical vertebral maturation. This program could help the orthodontists to identify the stage of cervical vertebral maturation when planning the orthodontic treatment for the patients with skeletal disharmonies.