Asteatosis is characterized by excessive moisture loss from the stratum corneum, leading to skin dryness, itching, cracking, and susceptibility to bacterial infections and allergic conditions. While subjective clinical assessments remain the standard in the diagnosis of asteatosis, recent studies have aimed to objectively evaluate skin moisture through physicochemical measurements. However, the adoption of existing research instruments for assessing skin moisture in clinical practice is limited due to their high cost, bulky design, and inconsistent measurement results. This study addresses the issue of measurement variability by developing a capacitance-based skin moisture meter with an improved load stabilizing mechanism to stabilize the contact force between the sensor and the skin, which is a primary source of measurement error. The interaction at the sensor-skin boundary area is modeled using an equivalent electrical circuit, and the optimal measurement frequency is determined to enhance accuracy and reproducibility. Based on these findings, we developed a compact capacitance-based skin moisture meter capable of measuring water content as electrostatic capacitance with low variability. Performance of the instrument was evaluated using pig skin, chosen for its physiological similarity to human skin, by calculating the correlation between capacitance readings and gravimetric water content. In addition, to assess the measurement variability, water sorption and desorption tests were conducted using filter paper and water. The water content measured by the device showed a strong linear correlation with pig dermis water content (r = 0.9296) and an inverse correlation with total water loss across whole pig skin (r = −0.9066). In water sorption and desorption tests with filter paper, the mean coefficients of variation were 1.0% and 1.9%, respectively, indicating measurement stability comparable to or higher than that of existing research instruments. Although the aforementioned investigations were limited to pig skin and filter paper, these results suggest that the novel device possesses the essential performance required for accurate, low-variability measurement of human skin moisture.

Reliable assessment of lifetime smoking exposure is essential for clinical risk stratification and public health interventions. However, current self-reported methods are subject to recall bias and misclassification. Here, we developed a new multimodal deep learning model for predicting cumulative smoking exposure based on fundus images, clinical features, and manually measured anatomic indicators. In a cohort of 8,299 subjects from the Japan Ocular Imaging Registry, pre-processing was performed. For each subject, smoking history was extracted, together with 31 anatomical features of fundus photographs and 13 clinical characteristics. The proposed model consists of three branches: (1) an image branch employing EfficientNet-B3 to extract image representations, (2) a clinical branch utilizing a residual multilayer perceptron to process clinical features, and (3) an anatomical branch using a fully connected encoder to obtain anatomical features; finally the fused multi-outputs are input to the classification-assisted multiexpert regressor to estimate Brinkman Index. Our multimodal model achieved significantly better results (MAE = 158.22, R² = 0.34) compared to unimodal models. The classifier accomplished a accuracy of 61% and an F1-score of 0.50 for smoking intervals. These observations were validated by performing ablation studies, which corroborated that the modalities complement one another and that handcrafted features improve the results. This multimodal, non-invasive, objective framework should be regarded as a methodological exploration that demonstrates the technical feasibility of estimating cumulative smoking exposure from fundus images and associated clinical data. Future studies with larger datasets and objective biomarkers are needed to further validate its clinical applicability.

Background: Accurate prediction of hemodynamic outcomes after percutaneous coronary intervention (PCI) is crucial for optimal treatment planning. We previously validated a simplified computational fluid dynamics (CFD) method for estimating fractional flow reserve (FFR) using a vascular volume ratio-based boundary condition. This study aimed to evaluate the feasibility of this method to predict post-PCI FFR based solely on pre-procedural coronary computed tomography (CT) data.

Methods: This prospective study enrolled four patients undergoing PCI. Three-dimensional coronary models were reconstructed from pre-PCI CT scans. A virtual stenting procedure was performed by expanding the stenosis according to the distal reference vessel diameter, mimicking clinical practice while not referencing the actual stent dimensions. Subsequently, post-PCI FFR_CFD was calculated using our previously established CFD method.

Results: The predicted post-PCI FFR_CFD values showed good agreement with invasively measured FFR. The diameters of the virtual stents closely approximated those of the deployed stents, with a maximum difference of 0.25 mm. However, prediction errors for post-PCI FFR were larger than those observed for pre-PCI FFR in some cases.

Conclusion: This pilot study suggests that our simplified CFD method is feasible and promising for predicting post-PCI hemodynamic outcomes using only pre-procedural anatomical information.

Larger-scale validation studies are warranted to confirm its potential as a clinical tool for non-invasive PCI planning.

Apheresis therapy requires advanced operational skills. Capabilities sufficient to respond rapidly and accurately to complications such as circuit clotting, pressure abnormalities, and air contamination are crucially important for patient safety. Nevertheless, existing training systems are often designed for centralized facilities. Moreover, they lack suitability for bedside or ICU environments, where individualized training is needed most. To address these issues, we developed a compact and portable apheresis training system that can reproduce circuit abnormalities using a magnetic particle based occlusion mechanism and an air intake unit. The 400 × 200 × 350 mm system weighs 7.6 kg, incorporating a 6% v/v magnetic particle suspension (10.7 ± 1.6 µm particle size, 5 g/cm³ density) stirred at 567.9 rpm, multiple occlusion mechanisms, a simulated arm, and a control unit. For evaluation, the device was connected to clinical blood purification equipment with blood circuits for plasma exchange (PE) and granulocyte and monocyte adsorption apheresis (GMA). Variable occlusion was achieved by adjusting the distance separating the circuit and an external magnet, yielding magnetic flux densities of 20.3 mT at 10 mm to 380.8 mT at 0 mm. Clogging rates showed high linearity (R² = 0.997, n = 10). Across 10 repeated trials for each condition, complete occlusion consistently triggered pressure alarms within a few seconds in PE and GMA systems, demonstrating high reproducibility. Characteristic pressure responses such as negative sampling pressure or inlet-outlet pressures exceeding 400 mmHg were consistent with clinically observed abnormalities. The air intake mechanism introduced air into the circuit reliably, as confirmed visually and detected using device bubble sensors. All abnormal states were reversible after deactivation, confirming system stability and durability. These findings demonstrate that this system can simulate clinically relevant circuit disturbances safely under realistic operating conditions, supporting its feasibility as a bedside training tool to enhance emergency response competencies in apheresis therapy.

In Japanese nursing homes, diapers are typically changed at scheduled times each day. However, unnecessary changes may occur when no urination has taken place or when the diaper retains sufficient absorptive capacity. These unnecessary changes increase caregiver workload and contribute to higher costs due to the disposal of unused diapers. Monitoring urination timing and urine absorption volume can help ensure that diapers are changed only when needed, thereby improving care efficiency and reducing costs. We developed a diaper sensor system capable of detecting urination timing and measuring urine absorption volume. The system consists of comb-shaped electrodes connected to a microcomputer, with the electrodes affixed to the outside of the diaper. Urine absorption volume is derived from changes in capacitance measured between the electrodes. The previous system did not consider postures when measuring urine absorption volume. This study demonstrates the necessity of posture detection for measuring urine absorption volume and proposes a new diaper sensor system that measures urine absorption volume using posture detected by an integrated accelerometer. In phantom experiments, the proposed system reduced the mean absolute percentage error (MAPE) from 30% (previous system) to 10%, and posture detection using the accelerometer achieved 100% accuracy, demonstrating the necessity of posture detection for measuring urine absorption volume. To evaluate the practicality of the proposed system and compare its accuracy in measuring urine absorption volume with that of the previous system, a healthy adult male volunteer wore the sensor-attached diaper and accelerometer. After resting for 1 h, the subject urinated in one of three postures-supine, sitting, or 90-degree lateral-each tested eight times. The proposed system reduced the MAPE from 35% (previous) to 23%, demonstrating improved measurement accuracy in actual use scenarios.

Medical device interoperability is critical for improving healthcare quality. However, widespread adoption has been hindered by the prevalence of legacy devices lacking native communication capabilities. Replacing or retrofitting these devices is often infeasible owing to high financial costs and regulatory challenges. This study aimed to solve this problem by developing a noninvasive system that enables real-time data integration from heterogeneous medical devices. We developed a system using optical character recognition (OCR) to read numerical data from the screens of multiple medical devices using a camera or capture card. For numerical recognition, we developed two AlexNet-based models trained from scratch: a general-purpose model for standard digits, and a font-specific model for superimposed digits that occur during screen refresh transitions. The training data for the 55 classes of the font-specific model include synthetically generated images of overlapping digits created using a logical OR operation. The accuracy of the system was evaluated against frame-by-frame visual confirmation. Furthermore, its ability to perform real-time analysis was validated by integrating it with the Cartor hemodynamic simulator during a surgical procedure on a canine model of induced myocardial infarction. The general-purpose model achieved an overall accuracy of 99.84%, while the font-specific model achieved perfect accuracy of 100%, successfully resolving all cases of digit overlap. In the animal experiment, the system succeeded to track changes in cardiac function in real time, calculated from six hemodynamic parameters, with an update frequency of once every 0.63 s. The system captured the dynamic response of the left ventricular ejection curve index (SL) to multiple coronary artery ligations performed to induce myocardial infarction, where the SL value initially remained stable before showing a clear decline. This study demonstrates that an OCR-based approach can serve as a practical and effective bridge technology that enables existing non-networked medical devices to connect with advanced analytical tools such as physiological simulators, facilitating real-time clinical decision support without requiring costly equipment replacement. This paper presents a feasible method for advancing data-driven medical care in diverse clinical settings.

Defecation support in elderly care presents a major burden for both caregivers and care recipients. This places additional strain related to preparation and post-processing of defecation, and stress caused by unexpected defecation. The ultimate goal of this study was to develop a system that notifies users in advance of defecation urges. Our previous studies identified changes in impedance and phase associated with defecation. In this study, we measured the abdominal impedance and phase before and after defecation to evaluate the feasibility of predicting defecation from abdominal impedance measurements. Measurements performed at several frequencies revealed increases in impedance at higher frequencies (≥ 40 kHz) and decreases in phase prior to defecation, both of which returned to baseline levels thereafter. A comparative analysis of the impedance and phase at rest and around the time of defecation confirmed that these changes were specific to the pre-defecation period. These findings suggest the potential for a non-invasive, wearable monitoring system that could alert caregivers in advance to imminent defecation, thereby reducing the burden of unexpected defecation events in elderly care.

Recent attention has focused on the post-market retraining of AI-based software as a medical device (SaMD) following the introduction of a new approval process in Japan, known as the “Improvement Design within Approval for Timely Evaluation Notice (IDATEN).” This process facilitates the post-market performance improvement of SaMD. However, repeated use of test data raises concerns about over-adaptation to the test dataset, potentially introducing performance bias. In this study, performance bias was evaluated using a deep model designed to support hotspot detection in bone scintigraphy by simulating the selection and integration of multiple post-market models with repeated use of identical test data. Performance bias was observed in both ensemble learning approaches employing bagging- and boosting-inspired sequential aggregation with pretrained models. Additionally, bias reduction was demonstrated using Thresholdout_AUC, based on differential privacy principles. These findings are expected to be useful for SaMD development, leading to continuous performance improvements.

In stroke-induced motor paralysis, impairment of finger individuation often persists over the long term. Traditional finger function assessments in clinical rehabilitation rely on ordinal scales and subjective evaluation, limiting the ability to capture detailed recovery processes. This study proposes a novel quantitative method to evaluate finger individuation by assessing how much a single finger can move individually without involuntary movements of the other fingers. Fifty healthy adults and ten stroke patients with hemiparesis were instructed to flex each finger, starting from all fingers in fully extended position. Data from healthy participants were used to define the ranges of involuntary movement in the four non-instructed fingers during each instructed finger movement. For patient evaluation, “individuation index” was used, which was defined as the maximum flexion angle achievable within the ranges obtained from healthy participants. This index was expressed as a percentage of the maximum range of motion for each finger. In a patient with minimal motor paralysis, all fingers achieved individuation index of 100%. In patients with mild and moderate paralysis, fewer fingers achieved an individuation index of 100%, reflecting the severity of motor impairment. This method accurately detected differences in finger individuation, providing a quantitative and objective measure that could serve as a useful index for assessing motor control in stroke patients.

An increasing number of individuals in aging populations are affected by urination disorders such as benign prostatic hyperplasia and overactive bladder. Bladder diaries are widely used in the diagnosis of these disorders. Patients record the time of urination and voided volume (VV) for 3 consecutive days in their bladder diaries. A urine collection container is required for VV measurement. Inappropriate disposal or cleaning of instruments after use can result in the transmission of infection. In addition, self-recording of bladder diaries often have problems such as inaccurate recording or missing entries of urinary output. To address these issues, we developed a water-soluble paper urine collection container that can be flushed down the toilet. This system was also developed to measure VV and urinary flow rate parameters. VV, maximum urinary flow rate (Qmax), and average flow rate (Qave) were estimated based on the increase in capacitance between the electrodes placed on the exterior of the container. In a basic experiment, 0.9% saline solution was used as pseudo-urine. The relationship between the volume of pseudo-urine and change in capacitance (Δx) exhibited a strong linear correlation (R² > 0.99). A correction factor (k ≈ 1.31) was applied to account for the difference in dielectric constant between urine and saline solution. The conversion from Δx to VV was determined based on the results of the basic experiment. In a urination measurement experiment, five young males (23 ± 1 years) without symptoms of urination disorders participated. Each subject urinated ten times. Simultaneous measurements were obtained using the proposed system and a medical uroflowmeter (PicoFlow2, MEDICA S.p.A., MO, Italy). The mean errors (mean ± SD) for VV, Qmax, and Qave were 14.9 ± 41.8 mL, 2.5 ± 6.0 mL/s, and 2.0 ± 2.6 mL/s, respectively. The mean absolute percent error for VV, Qmax, and Qave were 14.1%, 15.9% and 19.9%, respectively. Paired t-tests (significance level, p < 0.01) showed no significant differences between the two measurement methods for any of the parameters. Bland-Altman analysis revealed no systematic error between the proposed system and the medical uroflowmeter for any of the items. The VV, Qmax, Qave measured using the proposed system agree well with those measured using a medical uroflowmeter. This system can be expected to be a hygienic and portable uroflowmeter that can contribute to automated bladder diary recording.

The emergence of autonomous driving is becoming a reality worldwide. However, in emergency situations, human drivers must assume control of the vehicle, hence detecting driver vigilance is critical. Although many electroencephalographic studies have documented alterations in brain rhythms associated with fatigue, the results have been inconsistent. Furthermore, in high-level automated driving contexts, the driver does not operate the vehicle except in emergencies. In this study, magnetoencephalograms from eight healthy participants were recorded as they watched a 20-minute video from the driver’s perspective. The participants were instructed to press a button when a checkered flag appeared, simulating their response to an emergency. Statistical analysis demonstrated that response time to the checkered flag was prolonged during the End period (final third) of the experiment (p = 0.04). The increase in reaction time from the Beginning (first third) to the Middle (middle third) period correlated negatively with alpha-band activities in the occipital cortices during all three periods (Beginning, r = −0.72; p = 0.04; Middle, r = −0.73, p = 0.04; End r = −0.78, p = 0.02). In other words, participants with higher alpha-band activity in the Beginning period did not experience prolonged reaction time toward the Middle period. This result suggests that alpha-band activity in the occipital cortex may serve as an indicator of whether participants maintain vigilance during autonomous driving.