Ultrasound-guided regional anesthesia (UGRA) offers substantial benefits in perioperative pain management; however, it remains underutilized because of technical complexity and training demands. Assistive artificial intelligence (AI) has emerged as a promising solution to support UGRA by enhancing anatomical recognition, procedural accuracy, and user confidence. This narrative review outlines the AI development pipeline for nerve visualization, describes available commercial tools, and summarizes clinical evidence. Although these technologies have the potential to democratize UGRA and reduce interoperator variability, limitations remain, including data bias, narrow anatomical coverage, and lack of outcome-based validation. Future efforts should focus on standardized evaluation, clinician-centered design, and rigorous clinical trials to ensure safe and effective integration of AI into UGRA practice.

Background: Time-of-flight (TOF) magnetic resonance angiography (MRA) is widely used to identify aneurysm in human brain. Various deep learning models have been developed to help TOF-MRA reading in the field. The performance of those TOF-MRA analysis tools, however, faces several limitations in cerebral aneurysm detection. These challenges primarily come from the fact that cerebral aneurysms occupy less than 0.1% of the total TOF-MRA voxel size. This study aims to improve the efficiency of cerebral aneurysm detection by developing a landmark-based semi-supervised deep learning method, a technology that automatically generates landmark boxes in areas with a high probability of cerebral aneurysm occurrence. Methods: We used data from a total of 500 aneurysm-positive and 50 aneurysm-negative subjects. The aneurysm detection model was developed using clustering and a dilated residual network. Results: When the number of landmarks was ten and their size was 36 mm3, the best performance was achieved in our experiment. Although landmark occupies a small portion of the entire image, up to 98.2% of landmarks were cerebral aneurysms. The sensitivity of the model for cerebral aneurysm detection was 83.0%, with a false positive rate of 3.4%. Conclusions This study developed a deep learning model using TOF-MRA image. This model generates the most suitable landmarks for each individual, excluding unnecessary areas for cerebral aneurysm detection, which makes it possible to focus on areas with a high probability of occurrence. This model is expected to enhance the efficiency and accuracy of cerebral aneurysm detection in the field.

Background: Time-of-flight (TOF) magnetic resonance angiography (MRA) is widely used to identify aneurysm in human brain. Various deep learning models have been developed to help TOF-MRA reading in the field. The performance of those TOF-MRA analysis tools, however, faces several limitations in cerebral aneurysm detection. These challenges primarily come from the fact that cerebral aneurysms occupy less than 0.1% of the total TOF-MRA voxel size. This study aims to improve the efficiency of cerebral aneurysm detection by developing a landmark-based semi-supervised deep learning method, a technology that automatically generates landmark boxes in areas with a high probability of cerebral aneurysm occurrence. Methods: We used data from a total of 500 aneurysm-positive and 50 aneurysm-negative subjects. The aneurysm detection model was developed using clustering and a dilated residual network. Results: When the number of landmarks was ten and their size was 36 mm3, the best performance was achieved in our experiment. Although landmark occupies a small portion of the entire image, up to 98.2% of landmarks were cerebral aneurysms. The sensitivity of the model for cerebral aneurysm detection was 83.0%, with a false positive rate of 3.4%. Conclusions This study developed a deep learning model using TOF-MRA image. This model generates the most suitable landmarks for each individual, excluding unnecessary areas for cerebral aneurysm detection, which makes it possible to focus on areas with a high probability of occurrence. This model is expected to enhance the efficiency and accuracy of cerebral aneurysm detection in the field.

Background: Time-of-flight (TOF) magnetic resonance angiography (MRA) is widely used to identify aneurysm in human brain. Various deep learning models have been developed to help TOF-MRA reading in the field. The performance of those TOF-MRA analysis tools, however, faces several limitations in cerebral aneurysm detection. These challenges primarily come from the fact that cerebral aneurysms occupy less than 0.1% of the total TOF-MRA voxel size. This study aims to improve the efficiency of cerebral aneurysm detection by developing a landmark-based semi-supervised deep learning method, a technology that automatically generates landmark boxes in areas with a high probability of cerebral aneurysm occurrence. Methods: We used data from a total of 500 aneurysm-positive and 50 aneurysm-negative subjects. The aneurysm detection model was developed using clustering and a dilated residual network. Results: When the number of landmarks was ten and their size was 36 mm3, the best performance was achieved in our experiment. Although landmark occupies a small portion of the entire image, up to 98.2% of landmarks were cerebral aneurysms. The sensitivity of the model for cerebral aneurysm detection was 83.0%, with a false positive rate of 3.4%. Conclusions This study developed a deep learning model using TOF-MRA image. This model generates the most suitable landmarks for each individual, excluding unnecessary areas for cerebral aneurysm detection, which makes it possible to focus on areas with a high probability of occurrence. This model is expected to enhance the efficiency and accuracy of cerebral aneurysm detection in the field.

Objective: Cognitive reserve has emerged as a concept to explain the variable expression of clinical symptoms in the pathology of Alzheimer’s disease (AD). The association between years of education, a proxy of cognitive reserve, and resting-state functional connectivity (rFC), a representative intermediate phenotype, has not been explored in the preclinical phase, considering risk factors for AD. We aimed to evaluate whether the relationship between years of education and rFC in cognitively preserved older adults differs depending on amyloid-beta deposition and APOE ε4 carrier status as effect modifiers. Methods: A total of 121 participants underwent functional magnetic resonance imaging, [ 18F] flutemetamol positron emission tomography-computed tomography, APOE genotyping, and a neuropsychological battery. Potential interactions between years of education and AD risk factors for rFC of AD-vulnerable neural networks were assessed with wholebrain voxel-wise analysis. Results: We found a significant education years-by-APOE ε4 carrier status interaction for the rFC from the seed region of the central executive (CEN) and dorsal attention networks. Moreover, there was a significant interaction of rFC between right superior occipital gyrus and the CEN seed region by APOE ε4 carrier status for memory performances and overall cognitive function. Conclusion In preclinical APOE ε4 carriers, higher years of education were associated with higher rFC of the AD vulnerable network, but this contributed to lower cognitive function. These results contribute to a deeper understanding of the impact of cognitive reserve on sensitive functional intermediate phenotypic markers in the preclinical phase of AD.

Purpose: Various vitrectomy probes are currently being used commercially, and there are ongoing efforts toward developing probes with higher cutting rates and smaller gauges. This study aimed to compare the efficiency and safety of various commercially available small gauge ultrahigh-speed dual pneumatic vitrectomy probes. Methods: We retrospectively analyzed the medical records of patients and recorded intraoperative videos while they underwent microincision three-port vitrectomy surgery for idiopathic epiretinal membrane at Soonchunhyang University Seoul Hospital. The patients were categorized into four groups based on the vitrectomy probe used during surgery: 23-7500 (UltraVit 23-gauge 7,500 cuts per minute [CPM]), 23-7500 (UltraVit 25-gauge 7,500 CPM), 25-10K (Advanced UltraVit 25-gauge 10,000 CPM), and 27-10K (Advanced UltraVit 27-gauge 10,000 CPM). Results: In total, 82 eyes from 82 patients were included in this work, with 16, 11, 26, and 29 eyes in groups 23-7500, 25-7500, 25-10K, and 27-10K, respectively. The corresponding vitrectomy times were 295.56 ± 53.55, 293.09 ± 50.28, 299.92 ± 59.42, and 349.38 ± 67.23 seconds, respectively. There was a significant difference in the vitrectomy time between the groups (p = 0.004). The mean number of sutures was 3, 3, 2.96, and 0.83, respectively. In the 23-7500 group, there was one case of iatrogenic retinal break, while in the 27-10K group, there was one case of postoperative hypotony. Conclusions Although advancements have been made in the 27-gauge vitrectomy probe, it still takes more vitrectomy time than it does when using the 23- and 25-gauge probes. However, the delay was within an average of 1 minute, and considering the significantly reduced need for sutures, there is a substantial benefit in terms of postoperative discomfort. Therefore, when choosing a probe for epiretinal membrane surgery among the four options, it is reasonable to select the 27-gauge probe according to the surgeon’s preference.

Objective: We aimed to create an efficient and valid predicting model which can estimate individuals’ brain age by quantifying their regional brain volumes. Methods: A total of 2,560 structural brain magnetic resonance imaging (MRI) scans, along with demographic and clinical data, were obtained. Pretrained deep-learning models were employed to automatically segment the MRI data, which enabled fast calculation of regional brain volumes. Brain age gaps for each subject were estimated using volumetric values from predefined 12 regions of interest (ROIs): bilateral frontal, parietal, occipital, and temporal lobes, as well as bilateral hippocampus and lateral ventricles. A larger weight was given to the ROIs having a larger mean volumetric difference between the cognitively unimpaired (CU) and cognitively impaired group including mild cognitive impairment (MCI), and dementia groups. The brain age was predicted by adding or subtracting the brain age gap to the chronological age according to the presence or absence of the atrophy region. Results: The study showed significant differences in brain age gaps among CU, MCI, and dementia groups. Furthermore, the brain age gaps exhibited significant correlations with education level and measures of cognitive function, including the clinical dementia rating sum-of-boxes and the Korean version of the Mini-Mental State Examination. Conclusion The brain age that we developed enabled fast and efficient brain age calculations, and it also reflected individual’s cognitive function and cognitive reserve. Thus, our study suggested that the brain age might be an important marker of brain health that can be used effectively in real clinical settings.