OPG-based dental age estimation using a data-technical exploration of deep learning techniques.

Dental age estimation, a cornerstone in forensic age assessment, has been extensively tried and tested, yet manual methods are impeded by tedium and interobserver variability. Automated approaches using deep transfer learning encounter challenges like data scarcity, suboptimal training, and fine-tuning complexities, necessitating robust training methods. This study explores the impact of convolutional neural network hyperparameters, model complexity, training batch size, and sample quantity on age estimation. EfficientNet-B4, DenseNet-201, and MobileNet V3 models underwent cross-validation on a dataset of 3896 orthopantomograms (OPGs) with batch sizes escalating from 10 to 160 in a doubling progression, as well as random subsets of this training dataset. Results demonstrate the EfficientNet-B4 model, trained on the complete dataset with a batch size of 160, as the top performer with a mean absolute error of 0.562 years on the test set, notably surpassing the MAE of 1.01 at a batch size of 10. Increasing batch size consistently improved performance for EfficientNet-B4 and DenseNet-201, whereas MobileNet V3 performance peaked at batch size 40. Similar trends emerged in training with reduced sample sizes, though they were outperformed by the complete models. This underscores the critical role of hyperparameter optimization in adopting deep learning for age estimation from complete OPGs. The findings not only highlight the nuanced interplay of hyperparameters and performance but also underscore the potential for accurate age estimation models through optimization. This study contributes to advancing the application of deep learning in forensic age estimation, emphasizing the significance of tailored training methodologies for optimal outcomes.

Phantom study of augmented reality framework to assist epicardial punctures.

Purpose: The objective of this study is to evaluate the accuracy of an augmented reality (AR) system in improving guidance, accuracy, and visualization during the subxiphoidal approach for epicardial ablation. Approach: An AR application was developed to project real-time needle trajectories and patient-specific 3D organs using the Hololens 2. Additionally, needle tracking was implemented to offer real-time feedback to the operator, facilitating needle navigation. The AR application was evaluated through three different experiments: examining overlay accuracy, assessing puncture accuracy, and performing pre-clinical evaluations on a phantom. Results: The results of the overlay accuracy assessment for the AR system yielded 2.36±2.04 mm. Additionally, the puncture accuracy utilizing the AR system yielded 1.02±2.41 mm. During the pre-clinical evaluation on the phantom, needle puncture with AR guidance showed 7.43±2.73 mm, whereas needle puncture without AR guidance showed 22.62±9.37 mm. Conclusions: Overall, the AR platform has the potential to enhance the accuracy of percutaneous epicardial access for mapping and ablation of cardiac arrhythmias, thereby reducing complications and improving patient outcomes. The significance of this study lies in the potential of AR guidance to enhance the accuracy and safety of percutaneous epicardial access.