Search Results (2,301)

In this paper, we propose noncontact heart rate (HR), oxygen saturation (SpO2), and respiratory rate (RR) detection methods using a smartphone camera. HR frequency is detected through filtering after obtaining a remote PPG (rPPG) signal and its power spectral density (PSD) is detected using color difference signal amplification and the plane-orthogonal-to-the-skin method. Additionally, the SpO2 is detected using the HR frequency and the absorption ratio of the G and B color channels based on oxyhemoglobin absorption and reflectance theory. After this, the respiratory frequency is detected using the PSD of rPPG through respiratory frequency band filtering. For the image sequences recorded under various imaging conditions, the proposed method demonstrated superior HR detection accuracy compared to existing methods. The confidence intervals for HR and SpO2 detection were analyzed using Bland–Altman plots. Furthermore, the proposed RR detection method was also verified to be reliable. Full article

Background and Objectives: Patients with hip fractures have a high mortality rate, highlighting the need for a reliable prognostic tool. Although the prognostic nutritional index (PNI) is a well-established predictor in patients with cancer, its utility has not been thoroughly investigated in patients with hip fractures. Therefore, this study aims to evaluate the association between PNI and mortality in patients undergoing hip fracture surgery. Materials and Methods: A retrospective review was conducted on all patients aged ≥65 years who underwent surgery for hip fracture between January 2014 and February 2018. Quartile stratification was chosen because no universally accepted clinical cut-off exists for PNI; this approach enables comparison of equally sized groups and exploration of potential non-linear risk patterns. The primary endpoints were 1-year and overall mortality in older adults undergoing hip fracture surgery. Multivariable Cox proportional-hazards models adjusted for age, sex, ASA class and comorbidities. Results: A total of 815 patients were analyzed. One-year and overall mortality rates were highest in the Q1 group (26.6%, 14.2%, 6.9%, 6.4% [p < 0.001] and 56.7%, 36.3%, 27.0%, 15.2% [p < 0.001], respectively). In Cox regression analysis, a lower preoperative PNI was significantly associated with an increased risk of overall mortality (Q1: HR 3.25, 95% confidence interval [CI] 2.11–5.01, p < 0.001; Q2: HR 1.85, 95% CI 1.19–2.86, p = 0.006; Q3: HR 1.52, 95% CI 0.97–2.38, p = 0.065; Q4 as reference), indicating a stepwise, dose–response increase in mortality risk as PNI decreases. Conclusions: The findings demonstrate that a lower preoperative PNI is significantly associated with higher 1-year and overall mortality in older adults undergoing hip fracture surgery. Although further prospective validation is needed, preoperative PNI may help predict mortality in frail patients undergoing hip fracture surgery and identify those who could benefit from nutritional assessment and optimization before surgery. Full article

The integration of vision and language processing into a cohesive system has already shown promise with the application of large language models (LLMs) in medical image analysis. Their capabilities encompass the generation of medical reports, disease classification, visual question answering, and segmentation, providing yet another approach to interpreting multimodal data. This survey aims to compile all known applications of LLMs in the medical image analysis field, spotlighting their promises alongside critical challenges and future avenues. We introduce the concept of X-stage tuning which serves as a framework for LLMs fine-tuning across multiple stages: zero stage, one stage, and multi-stage, wherein each stage corresponds to task complexity and available data. The survey describes issues like sparsity of data, hallucination in outputs, privacy issues, and the requirement for dynamic knowledge updating. Alongside these, we cover prospective features including integration of LLMs with decision support systems, multimodal learning, and federated learning for privacy-preserving model training. The goal of this work is to provide structured guidance to the targeted audience, demystifying the prospects of LLMs in medical image analysis. Full article

This paper proposes a low-latency and scalable architecture for Edge-Enabled Digital Twin networked control systems (E-DTNCS) aimed at multi-robot collision avoidance and remote control in dynamic and latency-sensitive environments. Traditional approaches, which rely on centralized cloud processing or direct sensor-to-controller communication, are inherently limited by excessive network latency, bandwidth bottlenecks, and a lack of predictive decision-making, thus constraining their effectiveness in real-time multi-agent systems. To overcome these limitations, we propose a novel framework that seamlessly integrates edge computing with digital twin (DT) technology. By performing localized preprocessing at the edge, the system extracts semantically rich features from raw sensor data streams, reducing the transmission overhead of the original data. This shift from raw data to feature-based communication significantly alleviates network congestion and enhances system responsiveness. The DT layer leverages these extracted features to maintain high-fidelity synchronization with physical robots and to execute predictive models for proactive collision avoidance. To empirically validate the framework, a real-world testbed was developed, and extensive experiments were conducted with multiple mobile robots. The results revealed a substantial reduction in collision rates when DT was deployed, and further improvements were observed with E-DTNCS integration due to significantly reduced latency. These findings confirm the system’s enhanced responsiveness and its effectiveness in handling real-time control tasks. The proposed framework demonstrates the potential of combining edge intelligence with DT-driven control in advancing the reliability, scalability, and real-time performance of multi-robot systems for industrial automation and mission-critical cyber-physical applications. Full article

Objective: To evaluate the outcomes of a modified infratemporal fossa approach (ITFA) that preserves the posterior external auditory canal (EAC) in patients with tumors in the infratemporal fossa and skull base, focusing on postoperative hearing and facial nerve function. Methods: This retrospective study included nine patients who underwent ITFA with posterior EAC preservation for tumor removal while minimizing facial nerve rerouting. All surgeries were performed by a single surgeon. Preoperative and postoperative hearing levels, facial nerve function, tumor characteristics, and surgical outcomes were analyzed. Air-bone gaps (ABG) were assessed using pure tone audiometry, and facial nerve function was assessed using the House–Brackmann grading system. Results: The cohort consisted of eight female patients and one male patient, with a mean tumor size of 3.0 cm. Surgical outcomes were promising, with no statistically significant increase in postoperative ABG and well-preserved facial nerve function. Only one patient developed postoperative grade II facial palsy. A residual tumor was identified in one case with extensive meningioma, which has remained stable, and no recurrence or regrowth was noted during the follow-up period (mean: 3.7 years). The modified approach minimized complications related to conductive hearing loss and facial nerve dysfunction. Conclusions: The modified ITFA with posterior EAC preservation provides a promising alternative to conventional ITFA for managing deep-seated tumors. It preserves both hearing and facial nerve function while ensuring adequate tumor resection. Full article

Visual field (VF) testing remains a cornerstone in assessing retinal function by measuring how well different parts of the retina detect light. It is essential for early detection, monitoring, and management of many retinal diseases. By mapping retinal sensitivity, VF exams can reveal functional loss before structural changes become visible. This review summarizes how VF testing is applied across key conditions: hydroxychloroquine (HCQ) retinopathy, age-related macular degeneration (AMD), diabetic retinopathy (DR) and macular edema (DME), and inherited disorders including inherited dystrophies such as retinitis pigmentosa (RP). Traditional methods like the Goldmann kinetic perimetry and simple tools such as the Amsler grid help identify large or central VF defects. Automated perimetry (e.g., Humphrey Field Analyzer) provides detailed, quantitative data critical for detecting subtle paracentral scotomas in HCQ retinopathy and central vision loss in AMD. Frequency-doubling technology (FDT) reveals early neural deficits in DR before blood vessel changes appear. Microperimetry offers precise, localized sensitivity maps for macular diseases. Despite its value, VF testing faces challenges including patient fatigue, variability in responses, and interpretation of unreliable results. Recent advances in artificial intelligence, virtual reality perimetry, and home-based perimetry systems are improving test accuracy, accessibility, and patient engagement. Integrating VF exams with these emerging technologies promises more personalized care, earlier intervention, and better long-term outcomes for patients with retinal disease. Full article

Equine lutropin hormone/choriogonadotropin receptor (LH/CGR) is a G protein-coupled receptor that binds to both luteinizing hormone and choriogonadotropin, with multiple potential N-linked glycosylation sites in the long extracellular domain region. The roles of these glycosylation sites in hormone binding have been widely studied; however, their relationships with cyclic adenosine monophosphate (cAMP) activation, loss of cell surface receptors, and phosphorylated extracellular signal-regulated kinases1/2 (pERK1/2) expression are unknown. We used site-directed mutagenesis with the substitution of Asn for Gln to alter the consensus sequences for N-linked glycosylation, and cAMP signaling was analyzed in the mutants. Specifically, the N174Q and N195Q mutants exhibited markedly reduced expression levels, reaching approximately 15.3% and 2.5%, respectively, of that observed for wild-type equine LH/CGR. Correspondingly, the cAMP EC₅₀ values were decreased by 7.6-fold and 5.6-fold, respectively. Notably, the N195Q mutant displayed an almost complete loss of cAMP activity, even at high concentrations of recombinant eCG, suggesting a critical role for this glycosylation site in receptor function. Despite these alterations, Western blot analysis revealed that pERK1/2 phosphorylation peaked at 5 min following agonist stimulation across all mutants, indicating that the ERK1/2 signaling pathway remains functionally intact. This study demonstrates that the specific N-linked glycosylation site, N195, in equine LH/CGR is indispensable for cAMP activity but is normally processed in pERK1/2 signaling. Thus, we suggest that in equine LH/CGR, agonist treatment induces biased signaling, differentially activating cAMP signaling and the pERK1/2 pathway. Full article

Deep Reinforcement Learning (DRL) algorithms often exhibit significant performance variability across different training runs, even with identical settings. This paper investigates the hypothesis that a key contributor to this variability is the divergence in the observation spaces explored by individual learning agents. We conducted an empirical study using Proximal Policy Optimization (PPO) agents trained on eight Atari environments. We analyzed the collected agent trajectories by qualitatively visualizing and quantitatively measuring the divergence in their explored observation spaces. Furthermore, we cross-evaluated the learned actor and value networks, measuring the average absolute TD-error, the RMSE of value estimates, and the KL divergence between policies to assess their functional similarity. We also conducted experiments where agents were trained from identical network initializations to isolate the source of this divergence. Our findings reveal a strong correlation: environments with low-performance variance (e.g., Freeway) showed high similarity in explored observation spaces and learned networks across agents. Conversely, environments with high-performance variability (e.g., Boxing, Qbert) demonstrated significant divergence in both explored states and network functionalities. This pattern persisted even when agents started with identical network weights. These results suggest that differences in experiential trajectories, driven by the stochasticity of agent–environment interactions, lead to specialized agent policies and value functions, thereby contributing substantially to the observed inconsistencies in DRL performance. Full article

Objectives: Spinal diseases are commonly encountered health problems with a wide spectrum. In addition to degenerative changes, other common spinal pathologies include metastases and compression fractures. Benign tumors like hemangiomas and infections such as spondylodiscitis are also frequently observed. Although magnetic resonance imaging (MRI) is considered the gold standard in diagnostic imaging, the morphological similarities of lesions can pose significant challenges in differential diagnoses. In recent years, the use of artificial intelligence applications in medical imaging has become increasingly widespread. In this study, we aim to detect and classify vertebral body lesions using the YOLO-v8 (You Only Look Once, version 8) deep learning architecture. Materials and Methods: This study included MRI data from 235 patients with vertebral body lesions. The dataset comprised sagittal T1- and T2-weighted sequences. The diagnostic categories consisted of acute compression fractures, metastases, hemangiomas, atypical hemangiomas, and spondylodiscitis. For automated detection and classification of vertebral lesions, the YOLOv8 deep learning model was employed. Following image standardization and data augmentation, a total of 4179 images were generated. The dataset was randomly split into training (80%) and validation (20%) subsets. Additionally, an independent test set was constructed using MRI images from 54 patients who were not included in the training or validation phases to evaluate the model’s performance. Results: In the test, the YOLOv8 model achieved classification accuracies of 0.84 and 0.85 for T1- and T2-weighted MRI sequences, respectively. Among the diagnostic categories, spondylodiscitis had the highest accuracy in the T1 dataset (0.94), while acute compression fractures were most accurately detected in the T2 dataset (0.93). Hemangiomas exhibited the lowest classification accuracy in both modalities (0.73). The F1 scores were calculated as 0.83 for T1-weighted and 0.82 for T2-weighted sequences at optimal confidence thresholds. The model’s mean average precision (mAP) 0.5 values were 0.82 for T1 and 0.86 for T2 datasets, indicating high precision in lesion detection. Conclusions: The YOLO-v8 deep learning model we used demonstrates effective performance in distinguishing vertebral body metastases from different groups of benign pathologies. Full article

Background and Objectives: Glucose instability has been established to be related to postoperative morbidity and mortality in liver transplantation. To date, the impact of maintaining optimal blood glucose (BG) levels on the incidence of acute kidney injury (AKI) following liver transplantation (LT) remains unclear. This study aimed to determine the impact of optimal BG level after reperfusion (REP BG) on the incidence of AKI after living donor LT (LDLT). Materials and Methods: This study retrospectively reviewed 3331 patients who underwent LDLT between January 2008 and December 2019. Patients were divided into optimal (110 mg/dL < BG < 180 mg/dL) and non-optimal (BG < 110 mg/dL or >180 mg/dL) REP BG groups. Multivariable logistic regression analysis was performed to assess factors associated with AKI. Propensity score matching (PSM) was used to compare the incidence of AKI, AKI severity, and progression to chronic kidney disease (CKD) between the groups. Results: The incidence of AKI was 66.7%. After PSM, patients in the optimal REP BG group showed a lower incidence of AKI (66.5% vs. 70.6%, p = 0.032). Multivariable logistic regression analysis showed that the non-optimal REP BG group was independently associated with a higher risk of AKI (odds ratio [OR], 1.21; 95% confidence interval [CI], 1.02–1.45; p = 0.037) compared to the optimal group. Similarly, the risks of severe AKI (OR, 1.32; 95% CI, 1.11–1.58; p = 0.002) and progression to CKD (OR, 1.19; 95% CI, 1.01–1.41; p = 0.039) were significantly higher in the non-optimal group after PSM. Conclusions: Maintenance of an optimal REP BG was associated with a significantly lower incidence of AKI and a reduced risk of progression to CKD within 1 year after LDLT. Full article

Background/Objectives: Heart failure (HF) is a growing global health burden, yet early detection remains challenging due to the limitations of traditional diagnostic tools such as electrocardiograms (ECGs). Recent advances in deep learning offer new opportunities to identify left ventricular systolic dysfunction (LVSD), a key indicator of HF, from ECG data. This study validates AiTiALVSD, our previously developed artificial intelligence (AI)-enabled ECG Software as a Medical Device, for its accuracy, transparency, and robustness in detecting LVSD. Methods: This retrospective single-center cohort study involved patients suspected of LVSD. The AiTiALVSD model, based on a deep learning algorithm, was evaluated against echocardiographic ejection fraction values. To enhance model transparency, the study employed Testing with Concept Activation Vectors (TCAV), clustering analysis, and robustness testing against ECG noise and lead reversals. Results: The study involved 688 participants and found AiTiALVSD to have a high diagnostic performance, with an AUROC of 0.919. There was a significant correlation between AiTiALVSD scores and left ventricular ejection fraction values, confirming the model’s predictive accuracy. TCAV analysis showed the model’s alignment with medical knowledge, establishing its clinical plausibility. Despite its robustness to ECG artifacts, there was a noted decrease in specificity in the presence of ECG noise. Conclusions: AiTiALVSD’s high diagnostic accuracy, transparency, and resilience to common ECG discrepancies underscore its potential for early LVSD detection in clinical settings. This study highlights the importance of transparency and robustness in AI-ECG, setting a new benchmark in cardiac care. Full article

Indium-based oxide semiconductors have been commercialized because of their excellent electrical properties, but the high cost, limited availability, and environmental toxicity of indium necessitate the development of alternative materials. Among the most promising candidates, zinc–tin oxide (ZTO) is an indium-free oxide semiconductor with considerable potential, but its relatively low carrier mobility and inherent limitations in thin-film quality demand further performance enhancements. This paper proposes a new approach to overcome these challenges by incorporating single-walled carbon nanotubes (SWNTs) as conductive fillers into the ZTO matrix and using a layer-by-layer multiple coating process to construct nanocomposite thin films. As a result, ZTO/SWNTs (0.07 wt.%) thin-film transistors (TFTs) fabricated with three coating cycles exhibited a high saturation mobility of 18.72 cm²/V·s, a threshold voltage of 0.84 V, and a subthreshold swing of 0.51 V/dec. These values represent an approximately four-fold improvement in mobility compared to ZTO TFT, showing that the multiple-coating-based nanocomposite strategy can effectively overcome the fundamental limitations. This study confirms the feasibility of achieving high-performance oxide semiconductor transistors without indium, providing a sustainable pathway for next-generation flexible electronics and display technologies. Full article

Background: This study was designed to evaluate and contrast the surgical outcomes between coaxial robotic single-site myomectomy (RSSM) performed using the da Vinci Xi system and da Vinci SP system. Methods: A retrospective review was conducted on 81 women who underwent coaxial RSSM and 108 women who underwent myomectomy with the da Vinci SP system between October 2020 and January 2024. Propensity score matching was performed based on myoma count, the dominant myoma’s maximum diameter, and the myoma type according to the International Federation of Gynecology and Obstetrics (FIGO) classification. Patient characteristics and surgical outcomes were evaluated and compared between the two groups. Results: Compared to the SP group, the coaxial RSSM group showed significantly lower estimated blood loss (102.33 ± 61.01 vs. 203.98 ± 163.15 mL, p < 0.001), shorter operative time (91.22 ± 18.25 vs. 148.69 ± 45.62 min, p < 0.001), and smaller hemoglobin decrement (1.69 ± 0.93 vs. 2.85 ± 1.30, p < 0.001). However, hospital stay was shorter in the SP group than in the coaxial group (2.06 ± 0.24 vs. 4.07 ± 0.76 days, p < 0.001). There were no statistically significant differences in postoperative complications, including ileus, fever, or wound dehiscence. Additional comparisons using cases performed by four different surgeons yielded results consistent with the one-to-one surgeon comparison. Conclusions: Coaxial RSSM was associated with a shorter operative time and lower blood loss compared to SP myomectomy. A prospective study is warranted to validate and further compare the surgical outcomes of the two techniques. Full article

Background: This study evaluated the association between bilirubin subtypes (total, indirect, and direct bilirubin) and thyroid cancer risk, with a particular focus on stratified analyses using the ALBI (Albumin-Bilirubin) and PALBI (Platelet-Albumin-Bilirubin) indices by sex, smoking and drinking status, and age under 50 years. Methods: Data were obtained from 133,596 participants in the Korean Cancer Prevention Study-II (KCPS-II) cohort. During a mean follow-up period of 13.55 years, 2314 cases of thyroid cancer (ICD-10: C73) were identified. Serum bilirubin levels and ALBI and PALBI indices were analyzed using Cox proportional hazards regression models stratified by age, sex, smoking, and alcohol consumption status to estimate hazard ratios (HRs) and 95% confidence intervals (CIs). Results: In women, indirect bilirubin showed the strongest inverse association with thyroid cancer risk. ALBI and PALBI indices based on indirect bilirubin also demonstrated significant associations. A 1 standard deviation (SD) increase in indirect bilirubin was associated with a decreased risk of thyroid cancer (HR: 0.92, 95% CI: 0.84–0.99), and the ALBI index similarly showed an inverse association (HR: 0.92, 95% CI: 0.87–0.99). In contrast, the PALBI index was positively associated with thyroid cancer risk (HR: 1.11, 95% CI: 1.03–1.20). Among women who had never smoked, significant associations were observed for indirect bilirubin (HR: 0.91, 95% CI: 0.83–1.00), ALBI (HR: 0.93, 95% CI: 0.86–1.00), and PALBI (HR: 1.14, 95% CI: 1.05–1.23). In analyses stratified by alcohol consumption, the PALBI index was associated with increased thyroid cancer risk in non-drinkers, former drinkers, and ever drinkers, with respective risk increases of 15%, 18%, and 9%. Conclusions: In women, indirect bilirubin was significantly and inversely associated with thyroid cancer risk, and the ALBI and PALBI indices incorporating indirect bilirubin showed consistent results. These findings suggest that indirect bilirubin may play a critical role in the metabolic pathways underlying thyroid cancer in women. Full article

Background/Objectives: Senior-Loken syndrome (SLS) is a rare autosomal recessive renal–retinal disease caused by mutations in 10 genes. This study aimed to review the ophthalmic findings, renal function, and genotypes of Korean SLS cases. Methods: We retrospectively reviewed 17 genetically confirmed SLS patients in Korea, including 9 newly identified cases and 8 previously reported. Comprehensive ophthalmologic evaluations and renal assessments were conducted. Genetic testing was performed using whole-genome sequencing (WGS), whole-exome sequencing (WES), or Sanger sequencing. Results: Among the 17 patients, patients with NPHP1 mutations were most common (35.3%), followed by those with NPHP4 (29.4%), IQCB1 (NPHP5, 29.4%), and SDCCAG8 (NPHP10, 5.9%) mutations. Patients with NPHP1 mutations showed retinitis pigmentosa (RP) sine pigmento and preserved central vision independent of renal deterioration. Patients with NPHP4 mutations showed early renal dysfunction. Two patients aged under 20 maintained relatively good visual function, but older individuals progressed to severe retinopathy. Patients with IQCB1 mutations were generally prone to early and severe retinal degeneration, typically manifesting as Leber congenital amaurosis (LCA) (three patients), while two patients exhibited milder RP sine pigmento with preserved central vision. Notably, two out of five (40.0%) maintained normal renal function at the time of diagnosis, and both had large deletions in IQCB1. The patient with SDCCAG8 mutation exhibited both end-stage renal disease and congenital blindness due to LCA. Wide-field fundus autofluorescence (AF) revealed perifoveal and peripapillary hypoAF with a perifoveal hyperAF in younger patients across genotypes. Patients under 20 years old showed relatively preserved central vision, regardless of the underlying genetic mutation. Conclusions: The clinical manifestation of renal and ocular impairment demonstrated heterogeneity among Korean SLS patients according to causative genes, and the severity of renal dysfunction and visual decline was not correlated. Therefore, simultaneous comprehensive evaluations of both renal and ocular function should be performed at the initial diagnosis to guide timely intervention and optimize long-term outcomes. Full article