Estimating infant age from skull X-ray images using deep learning.

This study constructed deep learning models using plain skull radiograph images to predict the accurate postnatal age of infants under 12 months. Utilizing the results of the trained deep learning models, it aimed to evaluate the feasibility of employing major changes visible in skull X-ray images for assessing postnatal cranial development through gradient-weighted class activation mapping. We developed DenseNet-121 and EfficientNet-v2-M convolutional neural network models to analyze 4933 skull X-ray images collected from 1343 infants. Notably, allowing for a ± 1 month error margin, DenseNet-121 reached a maximum corrected accuracy of 79.4% for anteroposterior (AP) views (average: 78.0 ± 1.5%) and 84.2% for lateral views (average: 81.1 ± 2.9%). EfficientNet-v2-M reached a maximum corrected accuracy 79.1% for AP views (average: 77.0 ± 2.3%) and 87.3% for lateral views (average: 85.1 ± 2.5%). Saliency maps identified critical discriminative areas in skull radiographs, including the coronal, sagittal, and metopic sutures in AP skull X-ray images, and the lambdoid suture and cortical bone density in lateral images, marking them as indicators for evaluating cranial development. These findings highlight the precision of deep learning in estimating infant age through non-invasive methods, offering the progress for clinical diagnostics and developmental assessment tools.

Preeminently Robust Neural PPG Denoiser.

Photoplethysmography (PPG) is a simple and cost-efficient technique that effectively measures cardiovascular response by detecting blood volume changes in a noninvasive manner. A practical challenge in the use of PPGs in real-world applications is noise reduction. PPG signals are likely to be compromised by various types of noise, such as scattering or motion artifacts, and removing such compounding noises using a monotonous method is not easy. To this end, this paper proposes a neural PPG denoiser that can robustly remove multiple types of noise from a PPG signal. By casting the noise reduction problem into a signal restoration approach, we aim to achieve a solid performance in the reduction of different noise types using a single neural denoiser built upon transformer-based deep generative models. Using this proposed method, we conducted the experiments on the noise reduction of a PPG signal synthetically contaminated with five types of noise. Following this, we performed a comparative study using six different noise reduction algorithms, each of which is known to be the best model for each noise. Evaluation results of the peak signal-to-noise ratio (PSNR) show that the neural PPG denoiser is superior in three out of five noise types to the performance of conventional noise reduction algorithms. The salt-and-pepper noise type showed the best performance, with the PSNR of the neural PPG denoiser being 36.6080, and the PSNRs of the other methods were 19.8160 and 32.8234. The Poisson noise type performed the worst, showing a PSNR of 33.0090; the PSNRs of other methods were 35.1822 and 33.4795, respectively. Thereafter, an experiment to recover a signal synthesized with two or more of the five noise types was conducted. When the number of mixed noises was two, three, four, and five, the PSNRs were 29.2759, 27.8759, 26.5608, and 25.9402, respectively. Finally, an experiment to recover motion artifacts was also conducted. The synthesized motion artifact signal was created by synthesizing only a certain ratio of the total signal length. As a result of the motion artifact signal restoration, the PSNRs were 25.2872, 22.8240, 21.2901, and 19.9577 at 30%, 50%, 70%, and 90% motion artifact ratios, respectively. In the three experiments conducted, the neural PPG denoiser showed that various types of noise were effectively removed. This proposal contributes to the universal denoising of continuous PPG signals and can be further expanded to denoise continuous signals in the general domain.

Restoration of Remote PPG Signal through Correspondence with Contact Sensor Signal.

Photoplethysmography (PPG) is an optical measurement technique that detects changes in blood volume in the microvascular layer caused by the pressure generated by the heartbeat. To solve the inconvenience of contact PPG measurement, a remote PPG technology that can measure PPG in a non-contact way using a camera was developed. However, the remote PPG signal has a smaller pulsation component than the contact PPG signal, and its shape is blurred, so only heart rate information can be obtained. In this study, we intend to restore the remote PPG to the level of the contact PPG, to not only measure heart rate, but to also obtain morphological information. Three models were used for training: support vector regression (SVR), a simple three-layer deep learning model, and SVR + deep learning model. Cosine similarity and Pearson correlation coefficients were used to evaluate the similarity of signals before and after restoration. The cosine similarity before restoration was 0.921, and after restoration, the SVR, deep learning model, and SVR + deep learning model were 0.975, 0.975, and 0.977, respectively. The Pearson correlation coefficient was 0.778 before restoration and 0.936, 0.933, and 0.939, respectively, after restoration.

Non-Contact Oxygen Saturation Measurement Using YCgCr Color Space with an RGB Camera.

Oxygen saturation (SPO2) is an important indicator of health, and is usually measured by placing a pulse oximeter in contact with a finger or earlobe. However, this method has a problem in that the skin and the sensor must be in contact, and an additional light source is required. To solve these problems, we propose a non-contact oxygen saturation measurement technique that uses a single RGB camera in an ambient light environment. Utilizing the fact that oxygenated and deoxygenated hemoglobin have opposite absorption coefficients at green and red wavelengths, the color space of photoplethysmographic (PPG) signals recorded from the faces of study participants were converted to the YCgCr color space. Substituting the peaks and valleys extracted from the converted Cg and Cr PPG signals into the Beer-Lambert law yields the SPO2 via a linear equation. When the non-contact SPO2 measurement value was evaluated based on the reference SPO2 measured with a pulse oximeter, the mean absolute error was 0.537, the root mean square error was 0.692, the Pearson correlation coefficient was 0.86, the cosine similarity was 0.99, and the intraclass correlation coefficient was 0.922. These results confirm the feasibility of non-contact SPO2 measurements.

Pulse Rate Variability Analysis Using Remote Photoplethysmography Signals.

Pulse rate variability (PRV) refers to the change in the interval between pulses in the blood volume pulse (BVP) signal acquired using photoplethysmography (PPG). PRV is an indicator of the health status of an individual's autonomic nervous system. A representative method for measuring BVP is contact PPG (CPPG). CPPG may cause discomfort to a user, because the sensor is attached to the finger for measurements. In contrast, noncontact remote PPG (RPPG) extracts BVP signals from face data using a camera without the need for a sensor. However, because the existing RPPG is a technology that extracts a single pulse rate rather than a continuous BVP signal, it is difficult to extract additional health status indicators. Therefore, in this study, PRV analysis is performed using lab-based RPPG technology that can yield continuous BVP signals. In addition, we intended to confirm that the analysis of PRV via RPPG can be performed with the same quality as analysis via CPPG. The experimental results confirmed that the temporal and frequency parameters of PRV extracted from RPPG and CPPG were similar. In terms of correlation, the PRVs of RPPG and CPPG yielded correlation coefficients between 0.98 and 1.0.

Lipoprotein-associated phospholipase A2 activity is associated with coronary artery disease and markers of oxidative stress: a case-control study.

BACKGROUND: Lipoprotein-associated phospholipase A(2) (Lp-PLA(2)) is a lipoprotein-bound enzyme that can release atherogenic isoprostanes from esterified phospholipids and that may be involved in inflammation and atherosclerosis. OBJECTIVE: This study investigates the association between Lp-PLA(2) activity and coronary artery disease (CAD) in relation to oxidative stress markers, in particular urinary 8-epi-prostaglandin F(2alpha) (8-epi-PGF(2alpha)). DESIGN: We conducted a case-control study in which the cross-sectional relation between Lp-PLA(2) activity, lipoproteins, and oxidative stress markers was determined in 799 patients with angiographically confirmed CAD and 925 healthy controls. RESULTS: Lp-PLA(2) activity was significantly (P < 0.001) higher in CAD cases than in controls (32.9 +/- 0.46 and 29.7 +/- 0.42 nmol . mL(-1) . min(-1), respectively). Both elevated Lp-PLA(2) activity and urinary excretion concentrations of 8-epi-PGF(2alpha) were associated with greater CAD risk (P for trend < 0.001). Odds ratios for the upper quartiles of Lp-PLA(2) activity and 8-epi-PGF(2alpha).excretion were 2.47 (95% CI: 1.79, 3.40) and 2.19 (1.52, 3.15), respectively, after adjustment for sex, age, BMI, blood pressure, smoking and alcohol consumption status, and LDL and HDL cholesterol. When we examined the additive effect of both markers for CAD risk, the relation between 8-epi-PGF(2alpha) and CAD was weakened above the second quartile of Lp-PLA(2) activity. Moreover, Lp-PLA(2) activity was positively correlated with urinary excretion concentrations of 8-epi-PGF(2alpha) in controls (r = 0.277, P < 0.001) and cases (r = 0.202, P < 0.001) and with the tail moment of lymphocyte DNA (r = 0.213, P < 0.001) in controls. CONCLUSION: This study shows an association of elevated Lp-PLA(2) activity with CAD risk in relation to oxidant stress and thus supports a proatherogenic role of Lp-PLA(2).