Search Results (135)

The early detection of Alzheimer’s disease (AD) is important for effective therapeutic interventions and optimized enrollment for clinical trials. Recent studies have shown high accuracy in identifying mild AD by applying visibility graph and machine learning methods to electroencephalographic (EEG) data. We present a novel analytical framework combining Weighted Visibility Graphs (WVG) and ensemble learning to detect individuals in the “preclinical” stage of AD (preAD) using a word repetition EEG paradigm, where WVG is an advanced variant of natural Visibility Graph (VG), incorporating weighted edges based on the visibility degree between corresponding data points. The EEG signals were recorded from 40 cognitively unimpaired elderly participants (20 preclinical AD and 20 normal old) during a word repetition task. Event-related potential (ERP) and oscillatory signals were extracted from each EEG channel and transformed into a WVG network, from which relevant topological features were extracted. The features were selected using t-tests to reduce noise. Subsequent statistical analysis reveals significant disparities in the structure of WVG networks between preAD and normal subjects. Furthermore, Principal Component Analysis (PCA) was applied to condense the input data into its principal features. Leveraging these PCA components as input features, several machine learning algorithms are used to classify preAD vs. normal subjects. To enhance classification accuracy and robustness, an ensemble method is employed alongside the classifiers. Our framework achieved an accuracy of up to 92% discriminating preAD from normal old using both linear and non-linear classifiers, signifying the efficacy of combining WVG and ensemble learning in identifying very early AD from EEG signals. The framework can also improve clinical efficiency by reducing the amount of data required for effective classification and thus saving valuable clinical time. Full article

Introduction: This study aimed to compare different scoring methods, such as phoneme, syllable, and word-based scoring, during word recognition in noise testing and their interaction with hearing loss severity. These scoring methods provided a structured framework for refining clinical audiological diagnosis by revealing underlying auditory processing at multiple linguistic levels. We highlight how scoring differences inform differential diagnosis and guide targeted audiological interventions. Methods: Pure tone audiometry and word-in-noise testing were conducted on 100 subjects with a wide range of hearing loss severity. Speech recognition was scored using phoneme, syllable, and word-based methods. All procedures were designed to reflect standard diagnostic protocols in clinical audiology. Discriminant function analysis examined how these scoring methods differentiate the degree of hearing loss. Results: Results showed that each method provides unique information about auditory processing. Phoneme-based scoring has pointed out basic auditory discrimination; syllable-based scoring can capture temporal and phonological processing, while word-based scoring reflects real-world listening conditions by incorporating contextual knowledge. These findings emphasize the diagnostic value of each scoring approach in clinical settings, aiding differential diagnosis and treatment planning. Conclusions: This study showed the effect of different scoring methods on hearing loss differentiation concerning severity. We recommend the integration of phoneme-based scoring into standard diagnostic batteries to enhance early detection and personalize rehabilitation strategies. Future research must involve studies about integration with other speech perception tests and applicability across different clinical settings. Full article

Introduction: Sleep disturbances are a common and often underestimated complication during intensive care unit (ICU) stays. These disturbances can significantly impact patients’ recovery and overall well-being. This study aimed to assess the sleep quality of ICU patients and investigate the environmental and clinical factors that affect sleep quality during their ICU stay. Methods: We conducted a six-month cross-sectional study involving patients who had stayed in the ICU for at least three nights and were oriented to time and place upon discharge. Sleep quality was assessed using the Arabic version of the Freedman Sleep Questionnaire. Both environmental factors (e.g., noise, light, and nursing interventions) and clinical variables (illness severity and pain) were examined. The differences across three time periods were analyzed using the Wilcoxon test and Spearman’s correlation. Multiple regression analysis identified the factors influencing sleep quality. Statistical analyses were performed using JAMOVI software (version 2.3.28). Results: The study enrolled 328 patients, with an average age of 49.74 ± 17.89 years. Of the participants, 75.3% were adults. The primary reasons for admission were circulatory distress (45.73%) and metabolic disorders (24.09%). Sleep quality was significantly lower in the ICU compared to patients’ sleep at home (Z = −14.870, p < 0.001). The EVA and APACHE II scores had a statistically significant effect on sleep quality (p < 0.001 and p = 0.015, respectively). In contrast, the Charlson and Quick SOFA scores did not show significant effects (p = 0.128 and p = 0.894). Environmental factors, including noise (p = 0.008), light (p = 0.009), and nursing interventions (p = 0.009), significantly impacted sleep quality. Conclusions: Patients in the ICU generally reported poor sleep quality. Our findings suggest that improving pain management, minimizing environmental noise, and reducing staff-related disturbances could significantly enhance sleep quality for patients in the intensive care unit (ICU). Full article

Hallux valgus is a common foot deformity. Traditional diagnosis mainly relies on X-ray images, which present radiation risks and require professional equipment, limiting their use in daily screening. In addition, in large-scale community screenings and resource-limited regions, where rapid processing of numerous patients is required, access to radiographic equipment or specialists may be constrained. Therefore, this study improves the MobileNetV3 model to automatically determine the presence of hallux valgus from digital foot photographs. In this study, we used 2934 foot photos from different organizations, combined with the segment anything model (SAM) to extract foot regions and replace the photo backgrounds to simulate different shooting scenarios, and used data enhancement techniques such as rotations and noise to extend the training set to more than 10,000 images to improve the diversity of the data and the model’s generalization ability. We evaluated several classification models and achieved over 95% accuracy, precision, recall, and F1 score by training the improved MobileNetV3. Our model offers a cost-effective, radiation-free solution to reduce clinical workload and enhance early diagnosis rates in underserved areas. Full article

Background: Knee Osteoarthritis (KOA) is a prevalent and debilitating joint disorder that significantly impacts quality of life, particularly in aging populations. Accurate and consistent classification of KOA severity, typically using the Kellgren-Lawrence (KL) grading system, is crucial for effective diagnosis, treatment planning, and monitoring disease progression. However, traditional KL grading is known for its inherent subjectivity and inter-rater variability, which underscores the pressing need for objective, automated, and reliable classification methods. Methods: This study investigates the performance of an EfficientNetB5 deep learning model, enhanced with transfer learning from the ImageNet dataset, for the task of classifying KOA severity into five distinct KL grades (0–4). We utilized a publicly available Kaggle dataset comprising 9786 knee X-ray images. A key aspect of our methodology was a comprehensive data-centric preprocessing pipeline, which involved an initial phase of outlier removal to reduce noise, followed by systematic label correction using the Cleanlab framework to identify and rectify potential inconsistencies within the original dataset labels. Results: The final EfficientNetB5 model, trained on the preprocessed and Cleanlab-remediated data, achieved an overall accuracy of 82.07% on the test set. This performance represents a significant improvement over previously reported benchmarks for five-class KOA classification on this dataset, such as ResNet-101 which achieved 69% accuracy. The substantial enhancement in model performance is primarily attributed to Cleanlab’s robust ability to detect and correct mislabeled instances, thereby improving the overall quality and reliability of the training data and enabling the model to better learn and capture complex radiographic patterns associated with KOA. Class-wise performance analysis indicated strong differentiation between healthy (KL Grade 0) and severe (KL Grade 4) cases. However, the “Doubtful” (KL Grade 1) class presented ongoing challenges, exhibiting lower recall and precision compared to other grades. When evaluated against other architectures like MobileNetV3 and Xception for multi-class tasks, our EfficientNetB5 demonstrated highly competitive results. Conclusions: The integration of an EfficientNetB5 model with a rigorous data-centric preprocessing approach, particularly Cleanlab-based label correction and outlier removal, provides a robust and significantly more accurate method for five-class KOA severity classification. While limitations in handling inherently ambiguous cases (such as KL Grade 1) and the small sample size for severe KOA warrant further investigation, this study demonstrates a promising pathway to enhance diagnostic precision. The developed pipeline shows considerable potential for future clinical applications, aiding in more objective and reliable KOA assessment. Full article

Pain assessment is a challenging task for clinicians due to its subjective nature, particularly in individuals with communication difficulties, cognitive impairments, or severe disabilities. Traditional methods such as the Visual Analogue Scale (VAS), Numerical Rating Scale (NRS), and Verbal Rating Scale (VRS) rely heavily on patient feedback, which can be inconsistent and subjective. To address these limitations, developing objective and reliable pain assessment tools that incorporate advanced technologies, such as multimodal data integration from video and fNIRS, is important for improving clinical outcomes. However, challenges such as noise susceptibility in fNIRS signals must be carefully addressed to realize their full potential. Recent studies have explored automatic pain assessment using machine learning and deep learning techniques, which require high-quality data that can accurately represent pain categories. In response to the introduction of a new dataset in the AI4Pain Challenge, we proposed a multimodal neural network model utilizing attention-based fusion to improve overall accuracy (MMAPA). Our model leverages video and fNIRS modalities as well as manually extracted statistical features. We also implemented fNIRS signal preprocessing and artifact noise filtering, which significantly improved performance on both the fNIRS and statistical feature branches. On the hidden test set, our model achieved an accuracy of 51.33%, outperforming the official baseline of 43.33%. To evaluate generalizability, we further tested our method on the BioVid Heat Pain Database, where our fusion model achieved the highest accuracy in the 10-fold cross-validation setting, outperforming PainAttNet and unimodal variants. These results highlight the effectiveness of our multimodal attention-based approach in improving pain classification performance across datasets. Full article

Background and Objectives: To evaluate the impact of impacted mandibular third molars on temporomandibular joint dysfunction using the Modified Helkimo Index, analyzing symptom severity across age groups. Materials and Methods: A cohort of 140 patients (70 with impacted molars, 70 without) was assessed using the Modified Helkimo Index. Patients were categorized by age (<25, 26–30, 31–35, >36 years), and statistical comparisons between Icdⁱ (with impacted molars) and Icd^a (without impacted molars) were performed. Key parameters included mandibular movement limitation, joint noises, and pain scores. Data were analyzed using descriptive statistics and statistical tests, with significance set at p < 0.05. Results: TMJ dysfunction was significantly more prevalent in patients under 25 years (Icdⁱ = 13.5, Icd^a = 11.0; p = 0.045), with a progressive decrease in severity in older groups (>36 years: Icdⁱ = 3.5, Icd^a = 4.5; p = 0.072). Women exhibited a higher prevalence across all age categories (female-to-male ratio: <25 years = 2.7, >36 years = 3.0). The most frequent symptoms were mandibular movement restriction (42.5%), joint noises (38.2%), and pain (35.7%). Conclusions: Impacted third molars may significantly exacerbate TMJ dysfunctions, particularly in younger individuals and females, with a strong association between impacted molars and increased Modified Helkimo Index scores. Early extraction might mitigate symptoms, emphasizing the need for proactive clinical management. Full article

Background/Objectives: Epilepsy is a common neurological disorder that not only severely impacts patients’ health but also imposes a significant burden on families and society. However, its pathogenesis remains unclear. Astrocytes play a crucial role in epileptic seizures and may serve as potential therapeutic targets. Establishing a network model of epileptic seizures based on the astrocyte–neuron cell coupling and the clinical electroencephalographic (EEG) characteristics of epilepsy can facilitate further research on refractory epilepsy and the development of treatment strategies. Methods: This study constructs a neuronal network dynamic model of epileptic seizures based on the Watts–Strogatz small-world network, with a particular emphasis on the biological mechanisms of astrocyte–neuron coupling. The phase-locking value (PLV) is used to quantify the degree of network synchronization and to identify the key nodes or connections influencing synchronous seizures, such that two epilepsy treatment strategies are proposed: seizure suppression through stimulation and surgical resection simulation therapy. The therapeutic effects are evaluated based on the PLV-quantified network synchronization. Results: The results indicate that the desynchronization effect of random noise and sinusoidal wave stimulation is limited, while square wave stimulation is the most effective. Among the four surgical resection strategies, the effectiveness is the highest when resecting nodes exhibiting epileptic discharges. These findings contribute to the development of rational seizure suppression strategies and provide insights into precise epileptic focus localization and personalized treatment approaches. Full article

Background: Single-sided deafness (SSD) and asymmetric hearing loss (AHL) impair spatial hearing and speech perception, often reducing quality of life. Cochlear implants (CIs) and bone conduction implants (BCIs) are rehabilitation options used in SSD and AHL to improve auditory perception and support functional integration in daily life. Objective: We aimed to evaluate hearing outcomes after auditory implantation in SSD and AHL patients, focusing on localization accuracy, speech-in-noise understanding, tinnitus relief, and perceived benefit. Methods: In this longitudinal observational study, 37 patients (adults and children) received a CI or a BCI according to clinical indications. Outcomes included localization and spatial speech-in-noise assessment, tinnitus ratings, and SSQ12 scores. Statistical analyses used parametric and non-parametric tests (p < 0.05). Results: In adult CI users, localization error significantly decreased from 81.9° ± 15.8° to 43.7° ± 13.5° (p < 0.001). In children, regardless of the implant type (CI or BCI), localization error improved from 74.3° to 44.8°, indicating a consistent spatial benefit. In adult BCI users, localization error decreased from 74.6° to 69.2°, but the improvement did not reach statistical significance. Tinnitus severity, measured on a 10-point VAS scale, decreased significantly in CI users (mean reduction: 2.8 ± 2.0, p < 0.001), while changes in BCI users were small and of limited clinical relevance. SSQ12B/C scores improved in all adult groups, with the largest gains observed in spatial hearing for CI users (2.1 ± 1.2) and in speech understanding for BCI users (1.6 ± 0.9); children reported high benefits across all domains. Head shadow yielded the most consistent benefit across all groups (up to 4.9 dB in adult CI users, 3.8 dB in adult BCI users, and 4.6 dB in children). Although binaural effects were smaller in BCI users, positive gains were observed, especially in pediatric cases. Correlation analysis showed that daily device use positively predicted SSQ12 improvement (r = 0.57) and tinnitus relief (r = 0.42), while longer deafness duration was associated with poorer localization outcomes (r = –0.48). Conclusions: CIs and BCIs provide measurable benefits in SSD and AHL rehabilitation. Outcomes vary with age, device, and deafness duration, underscoring the need for early intervention and consistent auditory input. Full article

Brain activity plays a fundamental role in science and technology, particularly in the advancement of cognitive process therapies. Gaining a deeper understanding of brain function can contribute to the development of more effective therapeutic strategies aimed at enhancing cognitive performance and mental well-being. Advances in technological innovation in the health sector have allowed the creation of portable wireless electroencephalogram (EEG) devices, which make recordings in contexts outside the laboratory or clinical area. This work aims to design, manufacture, and acquire data on the Chameleon-1 helmet used by young and adult people people in different health states. The data acquisition of the EEG signals is carried out using two electrodes positioned at points $F_3$ and $F_4$, which are placed with the international 10–20 system. Tests were performed on several university participants. The recorded results show reliable, precise, and stable data in each patient with an average concentration of 91%. Excellent results were obtained from patients with different health conditions. In these records, the efficiency and robustness of the Chameleon-1 helmet were verified in adapting to any skull and with good data precision without noise alteration. Full article

Thoracic electrical impedance tomography (EIT) provides real-time, bedside imaging of pulmonary function and has demonstrated significant clinical value in guiding treatment strategies for critically ill patients. However, the practical application of EIT remains challenging due to its susceptibility to measurement disturbances, such as electrode contact problems and patient movement. These disturbances often manifest as spike-like noise that can severely degrade EIT image quality. To address this issue, we propose a robust Principal Component Analysis (RPCA)-based approach that models EIT data as the sum of a low-rank matrix and a sparse matrix. The low-rank matrix captures the underlying physiological signals, while the sparse matrix contains spike-like noise components. In simulation studies considering different spike magnitudes, widths and channels, all the image correlation coefficients between RPCA-processed images and the ground truth exceeded 0.99, and the image error of the original fEIT image with spike-like noise was much larger than that after RPCA processing. In eight patient cases, RPCA significantly improved the image quality (image error: p < 0.001; image correlation coefficient: p < 0.001) and enhanced the clinical EIT-based indexes accuracy (p < 0.001). Therefore, we conclude that RPCA is a promising technique for reducing spike-like noise in clinical EIT data, thereby improving data quality and potentially facilitating broader clinical application of EIT. Full article

Optical imaging is an excellent non-invasive method for viewing visceral organs. Most importantly, it is safer as compared to ionizing radiation-based methods like X-rays. By making use of the properties of photons, this technique generates high-resolution images of cells, molecules, organs, and tissues using visible, ultraviolet, and infrared light. Moreover, optical imaging enables real-time evaluation of soft tissue properties, metabolic alterations, and early disease markers in real time by utilizing a variety of techniques, including fluorescence and bioluminescence. Innovative biocompatible fluorescent probes that may provide disease-specific optical signals are being used to improve diagnostic capabilities in a variety of clinical applications. However, despite these promising advancements, several challenges remain unresolved. The primary obstacle includes the difficulty of developing efficient fluorescent probes, and the tissue autofluorescence, which complicates signal detection. Furthermore, the depth penetration restrictions of several imaging modalities limit their use in imaging of deeper tissues. Additionally, enhancing biocompatibility, boosting fluorescent probe signal-to-noise ratios, and utilizing cutting-edge imaging technologies like machine learning for better image processing should be the main goals of future research. Overcoming these challenges and establishing optical imaging as a fundamental component of modern medical diagnoses and therapeutic treatments would require cooperation between scientists, physicians, and regulatory bodies. Full article

In our clinical setting, we have identified a novel pattern of Trans-Impedance Matrix (TIM) measurement that we call “scatter”, a measure characterised by a loss of definition in the heat and line maps. Objective: the aim of this study was to describe the basic characteristics of the anomaly pattern. The secondary purpose is to evaluate correlations between the “scatter” pattern and normal TIM by considering different parameters. Methods: the experimental sample, therefore, consisted of 565 patients (81.1% of people with a checked TIM at follow-up; M: 279, F: 286 and mean age: 27 years (sd 26). The scatter pattern was found in 55 devices (9.7%). We classified this pattern as severe (20 devices) or mild (35 devices) according to the visual extent of the abnormality. We considered the visual extension of the pattern, device lifetime, type of internal part of the CI, and auditory performance (speech audiometry in quiet at 65 dB and in noise—Ita Matrix Sentence Test). We also analysed two quantitive parameters: Shannon entropy and exponential decay. Results: a difference was found in these two quantitative parameters between the severe scatter, mild scatter, and normal TIM groups (p-value < 0.0001). The severe scatter group seems to be related to the type of device (CI24RE and CI512) and long device life (average 133 months); it did not show differences in audiology performances compared to the other groups. Conclusions: this result gives a numerical validation to the more subjective visual inspection approach. The scatter pattern is a novel, previously undescribed abnormality of TIM. It can vary from moderate to severe. A numerical basis to validate the inspection approach is described here. Full article

For the past few decades, brain tumors have had a substantial influence on human life, and pose severe health risks if not treated and diagnosed in the early stages. Brain tumor problems are highly diverse and vary extensively in terms of size, type, and location. This brain tumor diversity makes it challenging to progress an accurate and reliable diagnostic tool. In order to effectively segment and classify the tumor region, still several developments are required to make an accurate diagnosis. Thus, the purpose of this research is to accurately segment and classify brain tumor Magnetic Resonance Images (MRI) to enhance diagnosis. Primarily, the images are collected from BraTS 2019, 2020, and 2021 datasets, which are pre-processed using min–max normalization to eliminate noise. Then, the pre-processed images are given into the segmentation stage, where a Variational Spatial Attention with Graph Convolutional Neural Network (VSA-GCNN) is applied to handle the variations in tumor shape, size, and location. Then, the segmented outputs are processed into feature extraction, where an AlexNet model is used to reduce the dimensionality. Finally, in the classification stage, a Bidirectional Gated Recurrent Unit (Bi-GRU) is employed to classify the brain tumor regions as gliomas and meningiomas. From the results, it is evident that the proposed VSA-GCNN-BiGRU shows superior results on the BraTS 2019 dataset in terms of accuracy (99.98%), sensitivity (99.92%), and specificity (99.91%) when compared with existing models. By considering the BraTS 2020 dataset, the proposed VSA-GCNN-BiGRU shows superior results in terms of Dice similarity coefficient (0.4), sensitivity (97.7%), accuracy (98.2%), and specificity (97.4%). While evaluating with the BraTS 2021 dataset, the proposed VSA-GCNN-BiGRU achieved specificity of 97.6%, Dice similarity of 98.6%, sensitivity of 99.4%, and accuracy of 99.8%. From the overall observation, the proposed VSA-GCNN-BiGRU supports accurate brain tumor segmentation and classification, which provides clinical significance in MRI when compared to existing models. Full article

Cone-Beam Computed Tomography (CBCT) holds significant clinical value in image-guided radiotherapy (IGRT). However, CBCT images of low-density soft tissues are often plagued with artifacts and noise, which can lead to missed diagnoses and misdiagnoses. We propose a new unsupervised CBCT image artifact correction algorithm, named Spatial Convolution Diffusion (ScDiff), based on a conditional diffusion model, which combines the unsupervised learning ability of generative adaptive networks (GAN) with the stable training characteristics of diffusion models. This approach can efficiently and stably achieve CBCT image artifact correction, resulting in clear, realistic CBCT images with complete anatomical structures. The proposed model can effectively improve the image quality of CBCT. The obtained results can reduce artifacts while preserving the anatomical structure of CBCT images. We compared the proposed method with several GAN- and diffusion-based methods. Our method achieved the highest corrected image quality and the best evaluation metrics. Full article