Search Results (1,671)

Background/Objectives: In recent years, 3D shape models of the human body have been used for various purposes. In principle, CT and MRI tomographic images are necessary to create such models. However, CT imaging and MRI generally impose heavy physical and financial burdens on the person being imaged, the model creator, and the hospital where the imaging facility is located. To reduce these burdens, the purpose of this study was to propose a method of creating individually adapted models by using simple X-ray images, which provide relatively little information and can therefore be easily acquired, and by transforming an existing base model. Methods: From medical images, anatomical feature values and scanning feature values that use the points that compose the contour line that can represent the shape of the femoral knee joint area were acquired, and deformed by free-form deformation. Free-form deformations were automatically performed to match the feature values using optimization calculations based on the confidence region method. The accuracy of the deformed model was evaluated by the distance between surfaces of the deformed model and the node points of the reference model. Results: Deformation and evaluation were performed for 13 cases, with a mean error of 1.54 mm and a maximum error of 12.88 mm. In addition, the deformation using scanning feature points was more accurate than the deformation using anatomical feature points. Conclusions: This method is useful because it requires only the acquisition of feature points from two medical images to create the model, and overall average accuracy is considered acceptable for applications in biomechanical modeling and motion analysis. Full article

Social facilities play a crucial role in urban development. However, there are currently few studies on the rationality of the spatial layout of social facilities in inland coastal cross-river cities. In view of this, we choose Nanjing City, China as an example, based on the point of interest (POI) data of social facility, and use the techniques including kernel density analysis, standard error ellipses, and spatial correlation analysis to systematically investigate the spatial distribution characteristics and patterns of social facilities in Nanjing. The research results show that there are significant differences in the spatial distribution of different types of social facilities in Nanjing, and the overall layout presents a pattern of denser distribution in the central urban area and more dispersed distribution in the peripheral areas. Shopping and transportation facilities are mostly concentrated in the core area of the main urban district, medical facilities are relatively concentrated, and cultural and educational facilities are located in all regions. The expert weighting analysis based on the Delphi method indicates that the influence weights of shopping consumption and transportation facilities on urban facilities are relatively greater than those of other factors. Overall, the social service facilities in the central urban area of Nanjing are well developed and well arranged, whereas the construction of facilities in several new districts and suburbs still needs to be further strengthened. The findings offer a scientific foundation for improving the layout of social facilities and urban planning in Nanjing, while also serving as a valuable reference for the development of other inland coastal cities spanning rivers. Full article

Background/Objectives: Bones are essential components of the human body, providing structural support, enabling mobility, storing minerals, and protecting internal organs. Bone fractures (BFs) are common injuries that result from excessive physical force and can lead to serious complications, including bleeding, infection, impaired oxygenation, and long-term disability. Early and accurate identification of fractures through radiographic imaging is critical for effective treatment and improved patient outcomes. However, manual evaluation of X-rays is often time-consuming and prone to diagnostic errors due to human limitations. To address this, artificial intelligence (AI), particularly deep learning (DL), has emerged as a powerful tool for enhancing diagnostic precision in medical imaging. Methods: This research introduces a novel convolutional neural network (CNN) model, the Multi-Level Feature Fusion Network (MLFNet), designed to capture and integrate both low-level and high-level image features. The model was evaluated using the Bone Fracture Multi-Region X-ray (BFMRX) dataset. Preprocessing steps included image normalization, resizing, and contrast enhancement to ensure stable convergence, reduce sensitivity to lighting variations in radiographic images, and maintain consistency. Ablation studies were conducted to assess architectural variations, confirming the model’s robustness and generalizability across data distributions. MLFNet’s high accuracy, interpretability, and efficiency make it a promising solution for clinical deployment. Results: MLFNet achieved an impressive accuracy of 99.60% as a standalone model and 98.81% when integrated into hybrid ensemble architectures with five leading pre-trained DL models. Conclusions: The proposed approach supports timely and precise fracture detection, optimizing the diagnostic process and reducing healthcare costs. This approach offers significant potential to aid clinicians in fields such as orthopedics and radiology, contributing to more equitable and effective patient care. Full article

Sensor arrays serve a crucial role in various fields such as environmental monitoring, industrial process control, and medical diagnostics, yet their reliability remains challenged by sensor drift and noise contamination. This study presents a novel framework for real-time data error correction and long-term drift compensation utilizing an iterative random forest-based error correction algorithm paired with an Incremental Domain-Adversarial Network (IDAN). The iterative random forest algorithm leverages the collective data from multiple sensor channels to identify and rectify abnormal sensor responses in real time. The IDAN integrates domain-adversarial learning principles with an incremental adaptation mechanism to effectively manage temporal variations in sensor data. Experiments utilizing the metal oxide semiconductor gas sensor array drift dataset demonstrate that the combination of these approaches significantly enhances data integrity and operational efficiency, achieving a robust and good accuracy even in the presence of severe drift. This study underscores the efficacy of integrating advanced artificial intelligence techniques for the ongoing evolution of sensor array technology, paving the way for enhanced monitoring systems capable of sustaining high levels of performance over extended time periods. Full article

Electroencephalography (EEG) communication technology with ultra-low power consumption, high transmission data rates, and low latency plays a significant role in mHealth, telemedicine, and Internet of Medical Things (IoMT). In this paper, generalized frequency division multiplexing (GFDM)-based direct mapping (DM) multi-input—multi-output (MIMO) mobile EEG communication technology (MECT) is proposed for implementation with the above-mentioned applications. The (2000, 1000) low-density parity-check (LDPC) code, four-quadrature amplitude modulation (4-QAM), a power assignment mechanism, and the 3rd Generation Partnership Project (3GPP) cluster delay line (CDL) channel model D were integrated into the proposed EEGCT. The transmission bit error rates (BERs), mean square errors (MSEs), and Pearson-correlation coefficients (PCCs) of the original and received EEG signals were evaluated. Simulation results show that, with a signal to noise ratio (SNR) of 14.51 dB, with a channel estimation error (CEE) of 5%, the BER, MSE, and PCC of the original and received EEG signals were 9.9777 × 10⁻⁸, 1.440 × 10⁻⁵ and 0.999999998, respectively, whereas, with an SNR of 15.0004 dB and a CEE of 10%, they were 9.9777 × 10⁻⁸, 1.4368 × 10⁻⁵, and 0.999999997622151, respectively. As the BER value, and PS saving are 9.9777 × 10⁻⁸, and 40%, respectively. With the CEE changes from 0% to 5%, and 5% to 10%, the N₀ values of the proposed MECT decrease by approximately 0.0022 and 0.002, respectively. The MECT has excellent EEG signal transmission performance. Full article

Introduction: Prescribing errors are the most common cause of preventable patient harm. In recent years, interprofessional education (IPE) has been increasingly utilised to improve knowledge and skills through promoting interprofessional collaboration. This scoping review aimed to evaluate the effectiveness of IPE interventions for pharmacological knowledge and prescribing skills in medical students and doctors-in-training. Methods: MEDLINE, EMBASE, CENTRAL, CINAHL, PsycINFO, ERIC and Scopus were searched on 18 February 2025 for studies published since 2020. Keywords included interprofessional education, medical student, medical trainee, pharmacology and prescribing. Results: Of the 2254 citations identified, 42 studies were included. There were four main types of IPE interventions: case-based learning, work-integrated-learning, didactic, and simulation and role-plays. Outcomes were spread across pharmacological knowledge, prescribing skills and interprofessional attitudes, and all studies reported one or more positive findings at Kirkpatrick IPE level 1, 2a, 2b, 3 or 4b. No study reported outcomes at Kirkpatrick IPE 4a. Conclusions: IPE interventions targeting pharmacology and prescribing are positively viewed by medical learners. IPE is effective in improving interprofessional attitudes and collaboration, as well as pharmacological knowledge and prescribing competency. Logistical challenges can be barriers to larger-group IPE implementation; nonetheless, IPE work-integrated learning in authentic clinical settings may overcome these challenges. Full article

Hemolysis, or the breakdown of red blood cells, observed in medical devices has been a significant concern for many years, particularly when mechanical stress on the cells is considered. This study focuses on evaluating extensional stresses in two configurations of the U.S. Food and Drug Administration (FDA) nozzle: the Gradual Cone (GC) and Sudden Contraction (SC) models. The nozzle geometries were created as 3D models using Ansys Fluent 18.2 and its pre-processing software ICEM CFD. The mesh was constructed with hexahedral elements with O-grid topologies. Effects of varying flow conditions were observed by modeling five experimental cases of the FDA nozzles, including throat Reynolds numbers of 500, 2000, 3500, 5000, and 6500. Hemolysis potentials of FDA nozzle configurations were examined by analyzing the whole domains. Turbulent modeling was used by applying the shear stress transport k-ω (SST k-ω) model. A threshold of 2.8 Pa for extensional stress was observed. Moreover, the most commonly used power law models were applied to the FDA nozzle to see the effect of extensional stress on power law models. Zhang’s power law models gave the lowest standard error, while Giersiepen’s model gave the highest error on hemolysis predictions. Full article

Deep learning has excelled in image classification largely due to large, professionally labeled datasets. However, in the field of medical images data annotation often relies on experienced experts, especially in tasks such as white blood cell classification where the staining methods for different cells vary greatly and the number of samples in certain categories is relatively small. To evaluate leukocyte classification performance with limited labeled samples, a few-shot learning method based on Feature Reconstruction Network with Improved EfficientNetV2 (FRNE) is proposed. Firstly, this paper presents a feature extractor based on the improved EfficientNetv2 architecture. To enhance the receptive field and extract multi-scale features effectively, the network incorporates an ASPP module with dilated convolutions at different dilation rates. This enhancement improves the model’s spatial reconstruction capability during feature extraction. Subsequently, the support set and query set are processed by the feature extractor to obtain the respective feature maps. A feature reconstruction-based classification method is then applied. Specifically, ridge regression reconstructs the query feature map using features from the support set. By analyzing the reconstruction error, the model determines the likelihood of the query sample belonging to a particular class, without requiring additional modules or extensive parameter tuning. Evaluated on the LDWBC and Raabin datasets, the proposed method achieves accuracy improvements of 3.67% and 1.27%, respectively, compared to the method that demonstrated strong OA performance on both datasets among all compared approaches. Full article

Background/Objectives: Later-stage melanoma at diagnosis is associated with increased mortality. Health care access, socioeconomic status, and neighborhood-level factors likely influence stage at presentation. This study aimed to examine whether neighborhood disadvantage, as measured by the Area Deprivation Index (ADI), is associated with later-stage melanoma diagnosis. Methods: We conducted a cross-sectional analysis of a retrospective cohort of 941 patients diagnosed with melanoma at a large academic medical center between 2010 and 2019. Residential addresses were geocoded and linked to ADI and rurality data. Covariates included race, ethnicity, age, gender, and insurance status. Multivariable logistic regression models with robust standard errors clustered at the census tract level were used to assess associations with melanoma stage at diagnosis. Results: Of 941 patients (63% male, 92.8% non-Hispanic White, mean age 64 years), 432 (46%) were diagnosed with late-stage melanoma. Mean ADI was higher among late-stage cases (5.4) compared to early-stage cases (3.3) (p < 0.001), even after adjustment for covariates. Non-Hispanic White race, private insurance, older age, and urban residences were associated with earlier stage at diagnosis. Racial disparities were attenuated after adjusting for ADI, with no significant interaction between race and ADI. Conclusions: Neighborhood disadvantage is significantly associated with later-stage melanoma diagnosis and contributes to observed racial and socioeconomic disparities. These findings highlight the need for targeted educational interventions and health policy initiatives to reduce late-stage melanoma diagnoses in vulnerable populations. Full article

Designing fair and sustainable pricing mechanisms for health insurance requires accurate risk assessment and the formulation of incentive-compatible strategies among stakeholders. This study proposes a hybrid framework that integrates machine learning with game theory to determine optimal, risk-based premium rates. Using a comprehensive dataset of insured individuals, the XGBoost algorithm is employed to predict medical claim costs and calculate corresponding premiums. To enhance transparency and explainability, SHAP analysis is conducted across four risk-based groups, revealing key drivers, including healthcare utilization and demographic features. The strategic interactions among the insurer, insured, and employer are modeled as a repeated game. Using the Folk Theorem, the conditions under which long-term cooperation becomes a sustainable Nash equilibrium are explored. The results demonstrate that XGBoost achieves high predictive accuracy (R² ≈ 0.787) along with strong performance in error measures (RMSE ≈ 1.64 × 10⁷ IRR, MAE ≈ 1.08 × 10⁶ IRR), while SHAP analysis offers interpretable insights into the most influential predictors. Game-theoretic analysis further reveals that under appropriate discount rates, stable cooperation between stakeholders is achievable. These findings support the development of equitable, transparent, and data-driven health insurance systems that effectively align the incentives of all stakeholders. Full article

The small (N = 102) prospective study by Bar-Sela and colleagues at Emek Medical Center in Israel) regarding diminished efficacy of immunotherapy in the setting of cannabis use would be an important discovery which could theoretically benefit the outcomes of oncology patients if verified by independent research teams, including by basic scientists. However, if this finding was spurious, clinical practice guidelines could recommend that oncology patients receiving immunotherapies be erroneously denied an evidence-based treatment for pain and chemotherapy-induced nausea and vomiting. Our full-length manuscript identified dozens of instances of unverifiable statistical information and even errors in arithmetic in this report. More briefly, our concerns regarding this well-cited (123 times) paper are as follows: (1) non-verifiable non-parametric statistics, including some that would change the statistical inferences; (2) difficulties with determining percentages; (3) switching from two-tailed tests in the Methods to one-tailed in the Results; (4) engaging in the unusual practice of floor rounding but not reporting this in the Methods; and (5) not reporting smoking, which could be a key confound. These concerns are serious errors that undermine the validity of the results and invalidate the conclusions that can be drawn from this prospective study about cannabis and immunotherapy. Full article

The localization and pose estimation of clinicians in the operating room is a critical component for building intelligent perception systems, playing a vital role in enhancing surgical standardization and safety. Multi-view, multi-person 3D pose estimation is a highly challenging task—especially in the operating room, where the presence of sterile clothing, occlusion from surgical instruments, and limited data availability due to privacy concerns exacerbate the difficulty. While voxel-based 3D pose estimation methods have shown promising results in general scenarios, their performance is significantly challenged in surgical environments with limited camera views and severe occlusions. To address these issues, this paper proposes a fine-grained voxel feature reconstruction method enhanced with depth information, effectively mitigating projection errors caused by reduced viewpoints. Additionally, an attention mechanism is integrated into the encoder–decoder architecture to improve the network’s capacity for global information modeling and enhance the accuracy of keypoint regression. Experiments conducted in real-world operating room scenarios, using the Multi-View Operating Room (MVOR) dataset, demonstrate that the proposed method maintains high accuracy even under limited camera views and outperforms existing state-of-the-art multi-view 3D pose estimation approaches. This work provides a novel and efficient solution for human pose estimation (HPE) in complex medical environments. Full article

Background/Objectives. Intracranial hemorrhages (ICHs) require immediate diagnosis for optimal clinical outcomes. Artificial intelligence (AI) is considered a potential solution for optimizing neuroimaging under conditions of radiologist shortage and increasing workload. This study aimed to directly compare diagnostic effectiveness between standalone AI services and AI-assisted radiologists in detecting ICHs on brain CT. Methods. A prospective, multicenter comparative study was conducted in 67 medical organizations in Moscow over 15+ months (April 2022–December 2024). We analyzed 3409 brain CT studies containing 1101 ICH cases (32.3%). Three commercial AI services with state registration were compared with radiologist conclusions formulated with access to AI results as auxiliary tools. Statistical analysis included McNemar’s test for paired data and Cohen’s h effect size analysis. Results. Radiologists with AI assistance statistically significantly outperformed AI services across all diagnostic metrics (p < 0.001): sensitivity 98.91% vs. 95.91%, specificity 99.83% vs. 87.35%, and accuracy 99.53% vs. 90.11%. The radiologists’ diagnostic odds ratio exceeded that of AI by 323-fold. The critical difference was in false-positive rates: 293 cases for AI vs. 4 for radiologists (73-fold increase). Complete complementarity of ICH misses was observed: all 12 cases undetected by radiologists were identified by AI, while all 45 cases missed by AI were diagnosed by radiologists. Agreement between methods was 89.6% (Cohen’s kappa 0.776). Conclusions. Radiologists maintain their role as the gold standard in ICH diagnosis, significantly outperforming AI services. Error complementarity indicates potential for improvement through systematic integration of AI as a “second reader” rather than a primary diagnostic tool. However, the high false-positive rate of standalone AI requires substantial algorithm refinement. The optimal implementation strategy involves using AI as an auxiliary tool within radiologist workflows rather than as an autonomous diagnostic system, with potential for delayed verification protocols to maximize diagnostic sensitivity while managing the false-positive burden. Full article

Background: Face masks are used in healthcare for the prevention of the spread of disease; however, the recent COVID-19 pandemic raised awareness of the challenges of typical opaque masks that obscure nonverbal cues. In addition, various masks have been shown to attenuate speech above 1000 Hz, and lack of nonverbal cues exacerbates speech understanding in the presence of background noise. Transparent masks can help to overcome the loss of nonverbal cues, but they have greater attenuative effects on higher speech frequencies. This study evaluated a newer prototype transparent face mask redesigned from a version evaluated in a previous study. Methods: Thirty participants (10 with normal hearing, 10 with moderate hearing loss, and 10 with severe-to-profound hearing loss) were recruited. Selected lists from the Connected Speech Test (CST) were digitally recorded using male and female talkers and presented to listeners at 65 dB HL in 12 conditions against a background of 4-talker babble (+5 dB SNR): without a mask (auditory only and audiovisual), with an opaque mask (auditory only and audiovisual), and with a transparent mask (auditory only and audiovisual). Results: Listeners with normal hearing performed consistently well across all conditions. For listeners with hearing loss, speech was generally easier to understand with the male talker. Audiovisual conditions were better than auditory-only conditions, and No Mask and Transparent Mask conditions were better than Opaque Mask conditions. Conclusions: These findings continue to support the use of transparent masks to improve communication, minimize medical errors, and increase patient satisfaction. Full article

Introduction: Decision-making regarding radiotherapy techniques for patients with nasopharyngeal cancer requires a comparison of photon and proton plans generated using planning software, which requires time and expertise. We developed a fully automated decision tool to select patients for proton therapy that predicts proton therapy (XT) and photon therapy (PT) dose distributions using only patient CT image data, predicts xerostomia and dysphagia probability using predicted critical organ mean doses, and makes decisions based on the Netherlands’ National Indication Protocol Proton therapy (NIPP) to select patients likely to benefit from proton therapy. Methods: This study used 48 nasopharyngeal patients treated at the Cancer Hospital of the Chinese Academy of Medical Sciences. We manually generated a photon plan and a proton plan for each patient. Based on this dose distribution, photon and proton dose prediction models were trained using deep learning (DL) models. We used the NIPP model to measure xerostomia levels 2 and 3, dysphagia levels 2 and 3, and decisions were made according to the thresholds given by this protocol. Results: The predicted doses for both photon and proton groups were comparable to those for manual plan (MP). The Mean Absolute Error (MAE) for each organ at risk in the photon and proton plans did not exceed 5% and showed a good performance of the dose prediction model. For proton, the normal tissue complication probability (NTCP) of xerostomia and dysphagia performed well, p > 0.05. There was no statistically significant difference. For photon, the NTCP of dysphagia performed well, p > 0.05. For xerostomia p < 0.05 but the absolute deviation was 0.85% and 0.75%, which would not have a great impact on the prediction result. Among the 48 patients’ decisions, 3 were wrong, and the correct rate was 93.8%. The area under curve (AUC) of operating characteristic curve (ROC) was 0.86, showing the good performance of the decision-making tool in this study. Conclusions: The decision tool based on DL and NTCP models can accurately select nasopharyngeal cancer patients who will benefit from proton therapy. The time spent generating comparison plans is reduced and the diagnostic efficiency of doctors is improved, and the tool can be shared with centers that do not have proton expertise. Trial registration: This study was a retrospective study, so it was exempt from registration. Full article