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Background/Objectives: Neuroinflammation is associated with the progression of various brain diseases, and the management of neuroinflammation-induced neural damage is a crucial aspect of treating neurological disorders. This study investigated the anti-inflammatory efficacy of photobiomodulation therapy (PBMT) using 660 nm phototherapy in a rat model with lipopolysaccharide (LPS)-induced neuroinflammation. Methods: We induced inflammation in rat brains via intraperitoneal injection of LPS and subjected the treatment group to 660 nm phototherapy to examine its protective effect against hippocampal damage based on pathological, histological, and immunohistochemical tissue analyses. Results: The 660 nm treated rats showed a significant decrease in hippocampal structural damage and cell death compared to the LPS-treated group. We observed reduced expression of the inflammation markers GFAP, TNF-α, and IL-1β in the hippocampus of the treatment group, and an increase in SIRT1 expression across all hippocampal regions. Conclusions: This study presents a promising method for controlling neuroinflammation and providing neuroprotection and inflammation relief. PBMT represents a non-invasive therapeutic approach with minimal side effects ensured through the proper control of light irradiation.

Photonic therapy is emerging as a promising method in neuroscience for addressing Alzheimer’s disease (AD). This study uses computational simulations to investigate the impact of specific wavelengths emitted by photodiodes on the light absorption rates in brain tissue for brain implant sensors. Additionally, it presents a novel methodology that enhances light absorption via multi-parameter optimization. By adjusting the angle and wavelength of the incident light, the absorption rate was significantly enhanced using four photodiodes, each emitting at 660 nm with a power input of 3 mW. Notably, an incident angle of 20 degrees optimized light absorption and minimized thermal effects on brain tissue. The findings indicate that photodiodes within the near-infrared spectrum are suitable for low-temperature therapeutic applications in brain tissues, affirming the viability of non-invasive and safe photonic therapy. This research contributes foundational data for advancing brain implant photonic sensor design and therapeutic strategies. Furthermore, it establishes conditions for achieving high light absorption rates with minimal heat generation, identifying optimal parameters for efficient energy transfer.

Temporal action detection (TAD) is one of the most active research areas in computer vision. TAD is the task of detecting actions in untrimmed videos and predicting the start and end times of the actions. TAD is a challenging task and requires a variety of temporal cues. In this paper, we present a one-stage transformer-based temporal action detection model using enhanced long- and short-term attention. Recognizing multiple actions in a video sequence requires an understanding of various temporal continuities. These temporal continuities encompass both long- and short-term temporal dependencies. To learn these long- and short-term temporal dependencies, our model leverages long- and short-term temporal attention based on transformers. In short-term temporal attention, we consider long-term memory to learn short-term temporal features and use compact long-term memory to efficiently learn long-term memory. Long-term temporal attention uses deformable attention to dynamically select the required features from long-term memory and efficiently learn the long-term features. Furthermore, our model offers interpretability for TAD by providing visualizations of class-specific probability changes for temporal action variations. This allows for a deeper understanding of the model’s decision-making process and facilitates further analysis of TAD. Based on the results of experiments conducted on the THUMOS14 and ActivityNet-1.3 datasets, our proposed model achieves an improved performance compared to previous state-of-the-art models. Our code is available at https://github.com/tommy-ahn/LSTA.

Our study develops a computer-aided diagnosis (CAD) system for breast ultrasound by presenting an innovative frequency domain technique for extracting mass irregularity features, thereby significantly boosting tumor classification accuracy. The experimental data consists of 5252 ultrasound breast tumor images, including 2745 benign tumors and 2507 malignant tumors. A Support Vector Machine was employed to classify the tumor as either benign or malignant, and the effectiveness of the proposed features set in distinguishing malignant masses from benign ones was validated. For the constructed CAD system, the performance indices’ accuracy, sensitivity, specificity, PPV, and NPV were 92.91%, 89.94%, 91.38%, 90.29%, and 91.45%, respectively, and the area index in the ROC analysis (AUC) was 0.924, demonstrating our method’s superiority over traditional spatial gray level dependence (SGLD), the ratio of depth to width, the count of depressions, and orientation features. Therefore, the constructed CAD system with the proposed features will be able to provide a precise and quick distinction between benign and malignant breast tumors with minimal training time in clinical settings.

Facial expressions are often considered the primary indicators of emotions. However, it is challenging to detect genuine emotions because they can be controlled. Many studies on emotion recognition have been conducted actively in recent years. In this study, we designed a convolutional neural network (CNN) model and proposed an algorithm that combines the analysis of bio-signals with facial expression templates to effectively predict emotional states. We utilized the EfficientNet-B0 architecture for network design and validation, known for achieving maximum performance with minimal parameters. The accuracy for emotion recognition using facial expression images alone was 74%, while the accuracy for emotion recognition combining biological signals reached 88.2%. These results demonstrate that integrating these two types of data leads to significantly improved accuracy. By combining the image and bio-signals captured in facial expressions, our model offers a more comprehensive and accurate understanding of emotional states.

Photobiomodulation (PBM) therapy on the brain employs red to near-infrared (NIR) light to treat various neurological and psychological disorders. The mechanism involves the activation of cytochrome c oxidase in the mitochondrial respiratory chain, thereby enhancing ATP synthesis. Additionally, light absorption by ion channels triggers the release of calcium ions, instigating the activation of transcription factors and subsequent gene expression. This cascade of events not only augments neuronal metabolic capacity but also orchestrates anti-oxidant, anti-inflammatory, and anti-apoptotic responses, fostering neurogenesis and synaptogenesis. It shows promise for treating conditions like dementia, stroke, brain trauma, Parkinson’s disease, and depression, even enhancing cognitive functions in healthy individuals and eliciting growing interest within the medical community. However, delivering sufficient light to the brain through transcranial approaches poses a significant challenge due to its limited penetration into tissue, prompting an exploration of alternative delivery methods such as intracranial and intranasal approaches. This comprehensive review aims to explore the mechanisms through which PBM exerts its effects on the brain and provide a summary of notable preclinical investigations and clinical trials conducted on various brain disorders, highlighting PBM’s potential as a therapeutic modality capable of effectively impeding disease progression within the organism—a task often elusive with conventional pharmacological interventions.

Facial expression recognition (FER) in the wild from various viewpoints, lighting conditions, face poses, scales, and occlusions is an extremely challenging field of research. In this study, we construct a face graph by selecting action units that play an important role in changing facial expressions, and we propose an algorithm for recognizing facial expressions using a graph convolutional network (GCN). We first generated an attention map that can highlight action units to extract important facial expression features from faces in the wild. After feature extraction, a face graph is constructed by combining the attention map with face patches, and changes in expression in the wild are recognized using a GCN. Through comparative experiments conducted using both lab-controlled and wild datasets, we prove that the proposed method is the most suitable FER approach for use with image datasets captured in the wild and those under well-controlled indoor conditions.

Abstract Background Long-term care facilities (LCFs) in South Korea have limited knowledge of and capability to care for patients with delirium. They also often lack an electronic medical record system. These barriers hinder systematic approaches to delirium monitoring and intervention. Therefore, this study aims to develop a web-based app for delirium prevention in LCFs and analyse its feasibility and usability. Methods The app was developed based on the validity of the AI prediction model algorithm. A total of 173 participants were selected from LCFs to participate in a study to determine the predictive risk factors for delerium. The app was developed in five phases: (1) the identification of risk factors and preventive intervention strategies from a review of evidence-based literature, (2) the iterative design of the app and components of delirium prevention, (3) the development of a delirium prediction algorithm and cloud platform, (4) a pilot test and validation conducted with 33 patients living in a LCF, and (5) an evaluation of the usability and feasibility of the app, completed by nurses (Main users). Results A web-based app was developed to predict high risk of delirium and apply preventive interventions accordingly. Moreover, its validity, usability, and feasibility were confirmed after app development. By employing machine learning, the app can predict the degree of delirium risk and issue a warning alarm. Therefore, it can be used to support clinical decision-making, help initiate the assessment of delirium, and assist in applying preventive interventions. Conclusions This web-based app is evidence-based and can be easily mobilised to support care for patients with delirium in LCFs. This app can improve the recognition of delirium and predict the degree of delirium risk, thereby helping develop initiatives for delirium prevention and providing interventions. Moreover, this app can be extended to predict various risk factors of LCF and apply preventive interventions. Its use can ultimately improve patient safety and quality of care.

There are some constraints such as external electrodes, a failure to capture most paroxysmal atrial fibrillation (AFib), low power transfer efficiency (PTE) for 24/7 charging technology, a short period of monitoring, and automatic detection of AFib in conventional electrocardiogram (ECG) sensors. To overcome these constraints, an implantable ECG sensor with a 2-coil inductive link with maximum power transfer efficiency (PTE) is designed to continuously monitor patients and efficiently detect AFib using global covering rule discovery and the minimum description length (MDL) algorithm. Among different combinations of ECG coils, the square spiral-square spiral coil demonstrates the maximum PTE, 56.23%, at the resonant frequency of 13.56 MHz and it is used in the implantable ECG sensor. The QRS complex from ECG signals of twenty-nine AFib patients is detected using different operation methods (DOM). The MDL algorithm is used to group 12 features of heart rate variability (HRV) parameters. The global covering rule discovery is proposed as a novel classification technique of AFib in ECG data. The average classification accuracy was 96.67 ± 7.03, and then the average recall, precision, F1-measures, and an average number of generated rules were 97.08 ± 6.23, 97.08 ± 6.23, 96.57 ± 7.23, and 7.9 ± 0.32, respectively. We found that the NN50, pNN50, and LF parameters can distinguish the AFib patient better than a healthy one. Among these parameters, pNN50 showed that it is greater than 34.75 in 41.38% of patients. The optimized implantable ECG sensor with a maximum PTE of 56.23% along with novel AFib detection and classification methods is suitable for its implementation in future implantable ECG sensors.

Pressure ulcer is a disease that has been increasing rapidly worldwide in recent years following the growing elderly population. However, its diagnoses and treatments still depend on the naked eye and traditional drug treatments, respectively. In this study, we examined the effects of the pressure ulcer-induced regions based on impedance measurements and skin electrical signals, and its treatment efficacy was verified using photobiomodulation therapy. The light used in this therapy was a light emitting diode light (660 nm), and it was verified to enhance antioxidant capacity in the enzyme-based antioxidation process. An examination of the angiogenesis efficacy based on wound healing activity and tube formation activation at the cell level confirmed that photobiomodulation therapy accelerated wound healing and angiogenesis. When the treatment effect of the therapy was evaluated with a rat animal model of pressure ulcer, the region affected by the pressure ulcer and the infiltration of inflammatory cells were significantly reduced by photobiomodulation therapy. The diagnosis using skin impedance measurement showed differences in the region affected by the pressure ulcer. The results of this study confirmed the diagnosis based on the impedance and treatment efficacy of photobiomodulation therapy and the treatment of pressure ulcer. The use of photobiomodulation therapy in conjunction with the existing treatment methods is expected to improve the efficiency of the pressure ulcer treatment.

In this study, we propose the direct diagnosis of thyroid cancer using a small probe. The probe can easily check the abnormalities of existing thyroid tissue without relying on experts, which reduces the cost of examining thyroid tissue and enables the initial self-examination of thyroid cancer with high accuracy. A multi-layer silicon-structured probe module is used to photograph light scattered by elastic changes in thyroid tissue under pressure to obtain a tactile image of the thyroid gland. In the thyroid tissue under pressure, light scatters to the outside depending on the presence of malignant and positive properties. A simple and easy-to-use tactile-sensation imaging system is developed by documenting the characteristics of the organization of tissues by using non-invasive technology for analyzing tactile images and judging the properties of abnormal tissues.

Visual diagnosis and rejuvenation are methods currently used to diagnose and treat pressure ulcers, respectively. However, the treatment process is difficult. We developed a biophotonic sensor to diagnose pressure ulcers and, subsequently, developed a pressure ulcer care device (PUCD.) We conducted animal and clinical trials to investigate the device’s effectiveness. We confirmed the accuracy of the pressure ulcer diagnosis algorithm to be 91% and we observed an 85% reduction in immune cells when using the PUCD to treat pressure ulcer-induced mice. Additionally, we compared the treatment group to the pressure ulcer induction group to assess the PUCD’s effectiveness in identifying immune cells through its nuclear shape. These results indicate a positive effect and suggest the use of PUCD as a recovery method for pressure ulcer diagnosis and treatment.

We thank Dr. Sadettin Ciftci for his comment on the key point issues in measuring the alpha and beta angle with Graf method. We appreciated his feedback [...]

It is hard to differentiate adolescent Major Depressive Disorder (MDD) patients from healthy adolescent controls based on structural MRI research findings, as the clinical characteristics of the patient group are heterogeneous, and the neuroimaging study results are ambiguous. We aimed to determine whether it is possible to reliably train a highly accurate predictive classification algorithm, even with the first onset of drug-naive adolescent MDD, solely using structural magnetic resonance imaging and without using any other clinical data from the patients. We also estimated the probability of the subject belonging to the predicted class to quantify the confidence of the prediction. Medication-naive adolescent patients in their first episode of MDD and healthy volunteers, matched for age, sex, and years of education, were prospectively recruited. Twenty-seven patients and 27 controls participated in the study. The two most significant variables were the standard deviations of intensity of the right ventral diencephalon and thickness of the superior segment of the circular sulcus of the insula. A participant is diagnosed as having MDD when the variation of either intensity in the right ventral diencephalon region or thickness of the superior segment of the circular sulcus of the insula increases. Structural brain changes can be used to build an accurate classification model for machine learning, even when the duration of illness is relatively short and the influence of MDD on the brain structure is minimal.

The deficiency and excess of vitamin D cause various diseases, necessitating continuous management; but it is not easy to accurately measure the serum vitamin D level in the body using a non-invasive method. The aim of this study is to investigate the correlation between vitamin D levels, body information obtained by an InBody scan, and blood parameters obtained during health checkups, to determine the optimum frequency of vitamin D quantification in the skin and to propose a vitamin D measurement method based on impedance. We assessed body composition, arm impedance, and blood vitamin D concentrations to determine the correlation between each element using multiple machine learning analyses and an algorithm which predicted the concentration of vitamin D in the body using the impedance value developed. Body fat percentage obtained from the InBody device and blood parameters albumin and lactate dehydrogenase correlated with vitamin D level. An impedance measurement frequency of 21.1 Hz was reflected in the blood vitamin D concentration at optimum levels, and a confidence level of about 75% for vitamin D in the body was confirmed. These data demonstrate that the concentration of vitamin D in the body can be predicted using impedance measurement values. This method can be used for predicting and monitoring vitamin D-related diseases and may be incorporated in wearable health measurement devices.

Malignant melanoma accounts for about 1–3% of all malignancies in the West, especially in the United States. More than 9000 people die each year. In general, it is difficult to characterize a skin lesion from a photograph. In this paper, we propose a deep learning-based computer-aided diagnostic algorithm for the classification of malignant melanoma and benign skin tumors from RGB channel skin images. The proposed deep learning model constitutes a tumor lesion segmentation model and a classification model of malignant melanoma. First, U-Net was used to classify skin lesions in dermoscopy images. We implement an algorithm to classify malignant melanoma and benign tumors using skin lesion images and expert labeling results from convolutional neural networks. The U-Net model achieved a dice similarity coefficient of 81.1% compared to the expert labeling results. The classification accuracy of malignant melanoma reached 80.06%. As a result, the proposed AI algorithm is expected to be utilized as a computer-aided diagnostic algorithm to help early detection of malignant melanoma.

Hip joint ultrasonographic (US) imaging is the golden standard for developmental dysplasia of the hip (DDH) screening. However, the effectiveness of this technique is subject to interoperator and intraobserver variability. Thus, a multi-detection deep learning artificial intelligence (AI)-based computer-aided diagnosis (CAD) system was developed and evaluated. The deep learning model used a two-stage training process to segment the four key anatomical structures and extract their respective key points. In addition, the check angle of the ilium body balancing level was set to evaluate the system’s cognitive ability. Hence, only images with visible key anatomical points and a check angle within ±5° were used in the analysis. Of the original 921 images, 320 (34.7%) were deemed appropriate for screening by both the system and human observer. Moderate agreement (80.9%) was seen in the check angles of the appropriate group (Cohen’s κ = 0.525). Similarly, there was excellent agreement in the intraclass correlation coefficient (ICC) value between the measurers of the alpha angle (ICC = 0.764) and a good agreement in beta angle (ICC = 0.743). The developed system performed similarly to experienced medical experts; thus, it could further aid the effectiveness and speed of DDH diagnosis.

The tissue inclusion parameter estimation method is proposed to measure the stiffness as well as geometric parameters. The estimation is performed based on the tactile data obtained at the surface of the tissue using an optical tactile sensation imaging system (TSIS). A forward algorithm is designed to comprehensively predict the tactile data based on the mechanical properties of tissue inclusion using finite element modeling (FEM). This forward information is used to develop an inversion algorithm that will be used to extract the size, depth, and Young's modulus of a tissue inclusion from the tactile data. We utilize the artificial neural network (ANN) for the inversion algorithm. The proposed estimation method was validated by a realistic tissue phantom with stiff inclusions. The experimental results showed that the proposed estimation method can measure the size, depth, and Young's modulus of a tissue inclusion with 0.58%, 3.82%, and 2.51% relative errors, respectively. The obtained results prove that the proposed method has potential to become a useful screening and diagnostic method for breast cancer.

Palpation techniques are widely used in medical procedures to detect the presence of lumps or tumors in the soft breast tissues. Since these procedures are very subjective and depend on the skills of the physician, it is imperative to perform detailed a scientific study in order to develop more efficient medical sensors to measure and generate palpation parameters. In this research, we propose an optical-based, artificial palpation sensor for lesion characterization. This has been developed using a multilayer polydimethylsiloxane optical waveguide. Light was generated at the critical angle to reflect totally within the flexible and transparent waveguide. When a waveguide was compressed by an external force, its contact area would deform and cause the light to scatter. The scattered light was captured by a high-resolution camera and saved as an image format. To test the performance of the proposed system, we used a realistic tissue phantom with embedded hard inclusions. The experimental results show that the proposed sensor can detect inclusions and provide the relative value of size, depth, and Young’s modulus of an inclusion.

Prolonged monitoring by cardiac electrocardiogram (ECG) sensors is useful for patients with emergency heart conditions. However, implant monitoring systems are limited by lack of tissue biocompatibility. Here, we developed an implantable ECG sensor for real-time monitoring of ventricular fibrillation and evaluated its biocompatibility using an animal model. The implantable sensor comprised transplant sensors with two electrodes, a wireless power transmission system, and a monitoring system. The sensor was inserted into the subcutaneous tissue of the abdominal area and operated for 1 h/day for 5 days using a wireless power system. Importantly, the sensor was encapsulated by subcutaneous tissue and induced angiogenesis, inflammation, and phagocytosis. In addition, we observed that the levels of inflammation-related markers increased with wireless-powered transmission via the ECG sensor; in particular, levels of the Th-1 cytokine interleukin-12 were significantly increased. The results showed that induced tissue damage was associated with the use of wireless-powered sensors. We also investigated research strategies for the prevention of adverse effects caused by lack of tissue biocompatibility of a wireless-powered ECG monitoring system and provided information on the clinical applications of inflammatory reactions in implant treatment using the wireless-powered transmission system.