Evaluation of the clinical efficacy of a TW3-based fully automated bone age assessment system using deep neural networks

Automatic and robust estimation of sex and chronological age from panoramic radiographs using a multi-task deep learning network: a study on a South Korean population

Sex and chronological age estimation are crucial in forensic investigations and research on individual identification. Although manual methods for sex and age estimation have been proposed, these processes are labor-intensive, time-consuming, and error-prone. The purpose of this study was to estimate sex and chronological age from panoramic radiographs automatically and robustly using a multi-task deep learning network (ForensicNet). ForensicNet consists of a backbone and both sex and age attention branches to learn anatomical context features of sex and chronological age from panoramic radiographs and enables the multi-task estimation of sex and chronological age in an end-to-end manner. To mitigate bias in the data distribution, our dataset was built using 13,200 images with 100 images for each sex and age range of 15-80 years. The ForensicNet with EfficientNet-B3 exhibited superior estimation performance with mean absolute errors of 2.93 ± 2.61 years and a coefficient of determination of 0.957 for chronological age, and achieved accuracy, specificity, and sensitivity values of 0.992, 0.993, and 0.990, respectively, for sex prediction. The network demonstrated that the proposed sex and age attention branches with a convolutional block attention module significantly improved the estimation performance for both sex and chronological age from panoramic radiographs of elderly patients. Consequently, we expect that ForensicNet will contribute to the automatic and accurate estimation of both sex and chronological age from panoramic radiographs.

Applications of artificial intelligence in the utilisation of imaging modalities in dentistry: A systematic review and meta-analysis of in-vitro studies

Background

In the past, dentistry heavily relied on manual image analysis and diagnostic procedures, which could be time-consuming and prone to human error. The advent of artificial intelligence (AI) has brought transformative potential to the field, promising enhanced accuracy and efficiency in various dental imaging tasks. This systematic review and meta-analysis aimed to comprehensively evaluate the applications of AI in dental imaging modalities, focusing on in-vitro studies.

Methods

A systematic literature search was conducted, in accordance with the PRISMA guidelines. The following databases were systematically searched: PubMed/MEDLINE, Embase, Web of Science, Scopus, IEEE Xplore, Cochrane Library, CINAHL (Cumulative Index to Nursing and Allied Health Literature), and Google Scholar. The meta-analysis employed fixed-effects models to assess AI accuracy, calculating odds ratios (OR) for true positive rate (TPR), true negative rate (TNR), positive predictive value (PPV), and negative predictive value (NPV) with 95 % confidence intervals (CI). Heterogeneity and overall effect tests were applied to ensure the reliability of the findings.

Results

9 studies were selected that encompassed various objectives, such as tooth segmentation and classification, caries detection, maxillofacial bone segmentation, and 3D surface model creation. AI techniques included convolutional neural networks (CNNs), deep learning algorithms, and AI-driven tools. Imaging parameters assessed in these studies were specific to the respective dental tasks. The analysis of combined ORs indicated higher odds of accurate dental image assessments, highlighting the potential for AI to improve TPR, TNR, PPV, and NPV. The studies collectively revealed a statistically significant overall effect in favor of AI in dental imaging applications.

Conclusion

In summary, this systematic review and meta-analysis underscore the transformative impact of AI on dental imaging. AI has the potential to revolutionize the field by enhancing accuracy, efficiency, and time savings in various dental tasks. While further research in clinical settings is needed to validate these findings and address study limitations, the future implications of integrating AI into dental practice hold great promise for advancing patient care and the field of dentistry.

Prediction of Fishman's skeletal maturity indicators using artificial intelligence

The present study aimed to evaluate the performance of automated skeletal maturation assessment system for Fishman's skeletal maturity indicators (SMI) for the use in dental fields. Skeletal maturity is particularly important in orthodontics for the determination of treatment timing and method. SMI is widely used for this purpose, as it is less time-consuming and practical in clinical use compared to other methods. Thus, the existing automated skeletal age assessment system based on Greulich and Pyle and Tanner-Whitehouse3 methods was further developed to include SMI using artificial intelligence. This hybrid SMI-modified system consists of three major steps: (1) automated detection of region of interest; (2) automated evaluation of skeletal maturity of each region; and (3) SMI stage mapping. The primary validation was carried out using a dataset of 2593 hand-wrist radiographs, and the SMI mapping algorithm was adjusted accordingly. The performance of the final system was evaluated on a test dataset of 711 hand-wrist radiographs from a different institution. The system achieved a prediction accuracy of 0.772 and mean absolute error and root mean square error of 0.27 and 0.604, respectively, indicating a clinically reliable performance. Thus, it can be used to improve clinical efficiency and reproducibility of SMI prediction.

The Interrater and Intrarater Reliability of the Humeral Head Ossification System and the Proximal Femur Maturity Index Assessments for Patients with Adolescent Idiopathic Scoliosis

Background

Skeletal maturity can evaluate the growth and development potential of children and provide a guide for the management of adolescent idiopathic scoliosis (AIS). Recent studies have demonstrated the advantages of the Humeral Head Ossification System (HHOS) and the Proximal Femur Maturity Index (PFMI), based on standard scoliosis films, in the management of AIS patients. We further assessed the HHOS and the PFMI method's reliability in the interrater and intrarater.

Methods

The data from 38 patients, including the humeral head and proximal femur on standard scoliosis films, were distributed to the eight raters in the form of a PowerPoint presentation. On 38 independent standard spine radiographs, raters utilized the HHOS and PFMI to assign grades. The PPT sequence was randomly changed and then reevaluated 2 weeks later. For every system, the 95% confidence interval (95% CI) and intraclass correlation coefficient (ICC) were calculated to evaluate the interrater and intrarater reliability.

Results

The HHOS was extremely reliable, with an intraobserver ICC of 0.802. In the first round, the interobserver ICC reliability for the HHOS was 0.955 (0.929-0.974), while in the second round, it was 0.939 (0.905-0.964). The PFMI was extremely reliable, with an intraobserver ICC of 0.888. In the first round, the interobserver ICC reliability for the PFMI was 0.967 (0.948-0.981), while in the second round, it was 0.973 (0.957-0.984).

Conclusions

The HHOS and PFMI classifications had excellent reliability. These two methods are beneficial to reduce additional exposure to radiation and expense for AIS. There are advantages and disadvantages to each classification. Clinicians should choose a personalized and reasonable method to assess skeletal maturity, which will assist in the management of adolescent scoliosis patients.

Convolutional neural network-based automatic cervical vertebral maturation classification method

OBJECTIVES

This study aimed to develop a fully automated artificial intelligence-aided cervical vertebral maturation (CVM) classification method based on convolutional neural networks (CNNs) to provide an auxiliary diagnosis for orthodontists.

METHODS

This study consisted of cephalometric images from patients aged between 5 and 18 years. After grouping them into six cervical stages (CSs) by orthodontists, a data set was constructed for analyzing CVM using CNNs. The data set was divided into training, validation, and test sets in the ratio of 70, 15, and 15%. Four CNN models namely, VGG16, GoogLeNet, DenseNet161, and ResNet152 were selected as the candidate models. After training and validation, the models were evaluated to determine which of them is most suitable for CVM analysis. Heat maps were analyzed for a deeper understanding of what the CNNs had learned.

RESULTS

The final classification accuracy ranking was ResNet152>DenseNet161>GoogLeNet>VGG16, as evaluated on the test set. ResNet152 proved to be the best model among the four models for CVM classification with a weighted κ of 0.826, an average AUC of 0.933 and total accuracy of 67.06%. The F1 score rank for each subgroup was: CS6>CS1>CS4>CS5>CS3>CS2. The area of the third (C3) and fourth (C4) cervical vertebrae were activated when CNNs were assessing the images.

CONCLUSION

CNN models proved to be a convenient, fast and reliable method for CVM analysis. CNN models have the potential to provide automatic auxiliary diagnostic tools in the future.

Evaluation of the Efficacy of Artificial Neural Network-Based Music Therapy for Depression

In order to evaluate the therapeutic effect of music therapy on patients with depression, this paper proposes a CNN-based noise detection method with the combination of HHT and FastICA for noise removal, with good data support from the DBN model. DBN-based feature extraction and classification are completed. As the training process of DBN itself requires a large number of training samples, there are also disadvantages such as slow convergence speed and easy to fall into local minima, which lead to a large amount of effort and time, and the learning efficiency is relatively low. A DBN optimization algorithm based on artificial neural network was proposed to evaluate the efficacy of music therapy. First of all, through the comparison of music therapy experimental group and control group, to verify that music therapy is effective for the treatment of depressed patients. Secondly, we propose to optimize the selection of features based on the frequency band energy ratio and the sliding average sample entropy, respectively, and then to classify the EEG of depressed patients under different music perceptions by training the DBN model and continuously adjusting the parameters, combined with the surtax classifier, and the classification accuracy is high. In particular, it can detect the different effects of different music styles, which is of great significance for the selection of appropriate music for the treatment of depressed patients.

Sex determination from lateral cephalometric radiographs using an automated deep learning convolutional neural network

Purpose

Despite the proliferation of numerous morphometric and anthropometric methods for sex identification based on linear, angular, and regional measurements of various parts of the body, these methods are subject to error due to the observer’s knowledge and expertise. This study aimed to explore the possibility of automated sex determination using convolutional neural networks (CNNs) based on lateral cephalometric radiographs.

Materials and Methods

Lateral cephalometric radiographs of 1,476 Iranian subjects (794 women and 682 men) from 18 to 49 years of age were included. Lateral cephalometric radiographs were considered as a network input and output layer including 2 classes (male and female). Eighty percent of the data was used as a training set and the rest as a test set. Hyperparameter tuning of each network was done after preprocessing and data augmentation steps. The predictive performance of different architectures (DenseNet, ResNet, and VGG) was evaluated based on their accuracy in test sets.

Results

The CNN based on the DenseNet121 architecture, with an overall accuracy of 90%, had the best predictive power in sex determination. The prediction accuracy of this model was almost equal for men and women. Furthermore, with all architectures, the use of transfer learning improved predictive performance.

Conclusion

The results confirmed that a CNN could predict a person’s sex with high accuracy. This prediction was independent of human bias because feature extraction was done automatically. However, for more accurate sex determination on a wider scale, further studies with larger sample sizes are desirable.

Artificial intelligence in oral and maxillofacial radiology: what is currently possible?

Artificial intelligence, which has been actively applied in a broad range of industries in recent years, is an active area of interest for many researchers. Dentistry is no exception to this trend, and the applications of artificial intelligence are particularly promising in the field of oral and maxillofacial (OMF) radiology. Recent researches on artificial intelligence in OMF radiology have mainly used convolutional neural networks, which can perform image classification, detection, segmentation, registration, generation, and refinement. Artificial intelligence systems in this field have been developed for the purposes of radiographic diagnosis, image analysis, forensic dentistry, and image quality improvement. Tremendous amounts of data are needed to achieve good results, and involvement of OMF radiologist is essential for making accurate and consistent data sets, which is a time-consuming task. In order to widely use artificial intelligence in actual clinical practice in the future, there are lots of problems to be solved, such as building up a huge amount of fine-labeled open data set, understanding of the judgment criteria of artificial intelligence, and DICOM hacking threats using artificial intelligence. If solutions to these problems are presented with the development of artificial intelligence, artificial intelligence will develop further in the future and is expected to play an important role in the development of automatic diagnosis systems, the establishment of treatment plans, and the fabrication of treatment tools. OMF radiologists, as professionals who thoroughly understand the characteristics of radiographic images, will play a very important role in the development of artificial intelligence applications in this field.