Obtaining accurate distance or depth information in endoscopy is crucial for the effective utilization of navigation systems. However, due to space constraints, incorporating depth cameras into endoscopic systems is often impractical. Our goal is to estimate depth images directly from endoscopic images using deep learning. This study presents a three-step methodology for training a depth-estimation network model. Initially, simulated endoscopy images and corresponding depth maps are generated using Unity based on a colon surface model obtained from segmented computed tomography colonography data. Subsequently, a cycle generative adversarial network model is employed to enhance the realism of the simulated endoscopy images. Finally, a deep learning model is trained using the synthesized endoscopy images and depth maps to estimate depths accurately. The performance of the proposed approach is evaluated and compared against prior studies utilizing unsupervised training methods. The results demonstrate the superior precision of the proposed technique in estimating depth images within endoscopy. The proposed depth estimation method holds promise for advancing the field by enabling enhanced navigation, improved lesion marking capabilities, and ultimately leading to better clinical outcomes.

Background: Oral potentially malignant disorders (OPMDs) are associated with an increased risk of cancer of the oral cavity including the tongue. The early detection of oral cavity cancers and OPMDs is critical for reducing cancer-specific morbidity and mortality. Recently, there have been studies to apply the rapidly advancing technology of deep learning for diagnosing oral cavity cancer and OPMDs. However, several challenging issues such as class imbalance must be resolved to effectively train a deep learning model for medical imaging classification tasks. The aim of this study is to evaluate a new technique of artificial intelligence to improve the classification performance in an imbalanced tongue lesion dataset.Methods: A total of 1,810 tongue images were used for the classification. The class-imbalanced dataset consisted of 372 instances of cancer, 141 instances of OPMDs, and 1,297 instances of noncancerous lesions. The EfficientNet model was used as the feature extraction model for classification. Mosaic data augmentation, soft labeling, and curriculum learning (CL) were employed to improve the classification performance of the convolutional neural network.Results: Utilizing a mosaic-augmented dataset in conjunction with CL, the final model achieved an accuracy rate of 0.9444, surpassing conventional oversampling and weight balancing methods. The relative precision improvement rate for the minority class OPMD was 21.2%, while the relative $${F}_{1}$$F1 score improvement rate of OPMD was 4.9%.Conclusions: The present study demonstrates that the integration of mosaic-based soft labeling and curriculum learning improves the classification performance of tongue lesions compared to previous methods, establishing a foundation for future research on effectively learning from imbalanced data.

The aim of this study was to evaluate the accuracy of navigation according to the number of markers in terms of target registration errors (TREs) at each anatomical location during the registration process of the navigation system for the mandible. The TREs were measured in five different experiments, varying only in the number of registration reference markers, which ranged from three to seven. To measure the TREs according to the number of registration reference markers, two experimental navigation devices were used: 1) Cbyon navigation surgery equipment 2) Polaris optical tracker. Both experiments were conducted to obtain the TREs at the anatomical locations of the mandible according to the number of registration markers during the navigation process. Statistical analysis was performed using the SPSS 23.0 software. At all anatomical locations, errors were 2 mm or less. Further, significant differences in the target errors measured by the Cbyon system were found according to the number of registration markers. Significant differences in the target errors measured by the Polaris optical tracker were found according to the registration markers at the posterior border only. In both groups, the target errors did not decrease as the number of registration markers increased. This study demonstrates that an increase in the number of registration markers is not associated with a decrease in the TRE, and that a specific number of registration markers could reduce the TREs at each anatomical site. It is important to determine the minimum number of image registration markers at which the smallest TRE would be observed for different surgical sites.

This study was to investigate the changes in brain function due to lack of oxygen (O₂) caused by mouth breathing, and to suggest a method to alleviate the side effects of mouth breathing on brain function through an additional O₂ supply. For this purpose, we classified the breathing patterns according to EEG signals using a machine learning technique and proposed a method to reduce the side effects of mouth breathing on brain function. Twenty subjects participated in this study, and each subject performed three different breathings: nose and mouth breathing and mouth breathing with O₂ supply during a working memory task. The results showed that nose breathing guarantees normal O₂ supply to the brain, but mouth breathing interrupts the O₂ supply to the brain. Therefore, this comparative study of EEG signals using machine learning showed that one of the most important elements distinguishing the effects of mouth and nose breathing on brain function was the difference in O₂ supply. These findings have important implications for the workplace environment, suggesting that special care is required for employees who work long hours in confined spaces such as public transport, and that a sufficient O₂ supply is needed in the workplace for working efficiency.

Radio wave propagation of three types of birdcage transmit/receive radio‐frequency (RF) coils (lowpass filter, highpass filter, and bandpass filter configurations) was analysed for mouse body magnetic resonance imaging at main magnetic fields of 1.5, 3.0, 4.7, 7.0, 9.4, and 11.7 T (Larmor frequencies of 63.87, 127.74, 200, 300, 400, and 500 MHz) in terms of magnetic (| B ₁ |) field sensitivity and homogeneity. The observed radio wave propagation in the central axial | B ₁ | field, as influenced by the biological subject, was calculated numerically in the finite‐difference time‐domain method and compared with assessment criteria using the mean value and standard deviation. The results for different Larmor frequencies RF shielding cage configurations, and biological subject loading versus unloading were compared and are discussed in detail.

Serotonergic neurotransmission has been associated with aggression in several psychiatric disorders. Human aggression is a continuum of traits, ranging from normal to pathological phenomena. However, the individual differences in serotonergic neurotransmission and their relationships with aggression traits in healthy individuals remain unclear. In this study, we explored the relationship between 5-HT2A receptor availability in vivo and aggression traits in healthy participants. Thirty-three healthy participants underwent 3-Tesla magnetic resonance imaging and positron emission tomography (PET) with [¹¹C]MDL100907, a selective radioligand for 5-HT2A receptors. To quantify 5-HT2A receptor availability, the binding potential (BP_ND) was derived using the basis function implementation of the simplified reference tissue model, with the cerebellum as the reference region. The participants’ aggression levels were assessed using the Buss–Perry Aggression Questionnaire. The voxel-based correlation analysis with age and sex as covariates revealed that the total aggression score was significantly positively correlated with [¹¹C]MDL100907 BP_ND in the right middle temporal gyrus (MTG) pole, left fusiform gyrus (FUSI), right parahippocampal gyrus, and right hippocampus. The physical aggression subscale score had significant positive correlations with [¹¹C]MDL100907 BP_ND in the left olfactory cortex, left orbital superior frontal gyrus (SFG), right anterior cingulate and paracingulate gyri, left orbitomedial SFG, left gyrus rectus, left MTG, left inferior temporal gyrus, and left angular gyrus. The verbal aggression subscale score showed significant positive correlations with [¹¹C]MDL100907 BP_ND in the bilateral SFG, right medial SFG, left FUSI, and right MTG pole. Overall, our findings suggest the possibility of positive correlations between aggression traits and in vivo 5-HT2A receptor availability in healthy individuals. Future research should incorporate multimodal neuroimaging to investigate the downstream effects of 5-HT2A receptor-mediated signaling and integrate molecular and systems-level information in relation to aggression traits.