Evaluation of inter- and intra-observer variations in prostate gland delineation using CT-alone versus CT/TPUS.

Background: This study aims to explore the role of four-dimensional (4D) transperineal ultrasound (TPUS) in the contouring of prostate gland with planning computed tomography (CT) images, in the absence of magnetic resonance imaging (MRI). Materials and methods: Five radiation oncologists (ROs) performed two rounds of prostate gland contouring (single-blinded) on CT-alone and CT/TPUS datasets obtained from 10 patients who underwent TPUS-guided external beam radiotherapy. Parameters include prostate volume, DICE similarity coefficient (DSC) and centroid position. Wilcoxon signed-rank test assessed the significance of inter-modality differences, and the intraclass correlation coefficient (ICC ) reflected inter- and intra-observer reliability of parameters. Results: Inter-modality analysis revealed high agreement (based on DSC and centroid position) of prostate gland contours between CT-alone and CT/TPUS. Statistical significant difference was observed in the superior-inferior direction of the prostate centroid position (p = 0.011). All modalities yielded excellent inter-observer reliability of delineated prostate volume with ICC > 0.9, mean DSC > 0.8 and centroid position: CT-alone (ICC = 1.000) and CT/TPUS (ICC = 0.999) left-right (L/R); CT-alone (ICC = 0.999) and CT/TPUS (ICC = 0.998) anterior-posterior (A/P); CT-alone (ICC = 0.999) and CT/TPUS (ICC = 1.000) superior-inferior (S/I). Similarly, all modalities yielded excellent intra-observer reliability of delineated prostate volume, ICC > 0.9 and mean DSC > 0.8. Lastly, intra-observer reliability was excellent on both imaging modalities for the prostate centroid position, ICC > 0.9. Conclusion: TPUS does not add significantly to the amount of anatomical information provided by CT images. However, TPUS can supplement planning CT to achieve a higher positional accuracy in the S/I direction if access to CT/MRI fusion is limited.

Extensive diversity in circadian regulation of plasma lipids and evidence for different circadian metabolic phenotypes in humans.

The circadian system regulates daily rhythms in lipid metabolism and adipose tissue function. Although disruption of circadian clock function is associated with negative cardiometabolic end points, very little is known about interindividual variation in circadian-regulated metabolic pathways. Here, we used targeted lipidomics-based approaches to profile the time course of 263 lipids in blood plasma in 20 healthy individuals. Over a span of 28 h, blood was collected every 4 h and plasma lipids were analyzed by HPLC/MS. Across subjects, about 13% of lipid metabolites showed circadian variation. Rhythmicity spanned all metabolite classes examined, suggesting widespread circadian control of lipid-mediated energy storage, transport, and signaling. Intersubject agreement for lipids identified as rhythmic was only about 20%, however, and the timing of lipid rhythms ranged up to 12 h apart between individuals. Healthy subjects therefore showed substantial variation in the timing and strength of rhythms across different lipid species. Strong interindividual differences were also observed for rhythms of blood glucose and insulin, but not cortisol. Using consensus clustering with iterative feature selection, subjects clustered into different groups based on strength of rhythmicity for a subset of triglycerides and phosphatidylcholines, suggesting that there are different circadian metabolic phenotypes in the general population. These results have potential implications for lipid metabolism disorders linked to circadian clock disruption.

Nonlinear Dynamics Measures for Automated EEG-Based Sleep Stage Detection.

BACKGROUND: The brain's continuous neural activity during sleep can be monitored by electroencephalogram (EEG) signals. The EEG wave pattern and frequency vary during five stages of sleep. These subtle variations in sleep EEG signals cannot be easily detected through visual inspection. SUMMARY: A range of time, frequency, time-frequency and nonlinear analysis methods can be applied to understand the complex physiological signals and their chaotic behavior. This paper presents a comprehensive comparative review and analysis of 29 nonlinear dynamics measures for EEG-based sleep stage detection. KEY MESSAGES: The characteristic ranges of these features are reported for the five different sleep stages. All nonlinear measures produce clinically significant results, that is, they can discriminate the individual sleep stages. Feature ranking based on the statistical F-value, however, shows that the third order cumulant of higher order spectra yields the most discriminative result. The distinct value ranges for each sleep stage and the discriminative power of the features can be used for sleep disorder diagnosis, treatment monitoring, and drug efficacy assessment.

Melanopsin and rod-cone photoreceptors play different roles in mediating pupillary light responses during exposure to continuous light in humans.

In mammals, the pupillary light reflex is mediated by intrinsically photosensitive melanopsin-containing retinal ganglion cells that also receive input from rod-cone photoreceptors. To assess the relative contribution of melanopsin and rod-cone photoreceptors to the pupillary light reflex in humans, we compared pupillary light responses in normally sighted individuals (n = 24) with a blind individual lacking rod-cone function. Here, we show that visual photoreceptors are required for normal pupillary responses to continuous light exposure at low irradiance levels, and for sustained pupillary constriction during exposure to light in the long-wavelength portion of the visual spectrum. In the absence of rod-cone function, pupillomotor responses are slow and sustained, and cannot track intermittent light stimuli, suggesting that rods/cones are required for encoding fast modulations in light intensity. In sighted individuals, pupillary constriction decreased monotonically for at least 30 min during exposure to continuous low-irradiance light, indicating that steady-state pupillary responses are an order of magnitude slower than previously reported. Exposure to low-irradiance intermittent green light (543 nm; 0.1-4 Hz) for 30 min, which was given to activate cone photoreceptors repeatedly, elicited sustained pupillary constriction responses that were more than twice as great compared with exposure to continuous green light. Our findings demonstrate nonredundant roles for rod-cone photoreceptors and melanopsin in mediating pupillary responses to continuous light. Moreover, our results suggest that it might be possible to enhance nonvisual light responses to low-irradiance exposures by using intermittent light to activate cone photoreceptors repeatedly in humans.

Singapore radiographers' perceptions and expectations of artificial intelligence - A qualitative study.

INTRODUCTION: With the emergence of artificial intelligence (AI) in medical imaging, radiographers are likely to be at the forefront of this technological advancement. Studies have therefore been conducted recently to understand radiographers' opinions on AI adoption. This study extends that work by using a qualitative approach to further explore radiographers' knowledge, perceptions, and expectations of AI. METHOD: Six online focus groups were conducted with 22 radiographers from the three public healthcare clusters in Singapore. They were purposively sampled, and participants were recruited from a broad demographic background with varying years of working experience and designations. The focus group sessions were transcribed verbatim and thematic analysis was performed on their responses. RESULTS: Participants demonstrated limited knowledge of AI. Their perceptions of AI were mixed, recognising its benefits in increasing efficiency and improving patient care, but also aware of its limitations in accuracy and bias. On how patients may perceive AI, participants felt that patients would accept AI if they felt it improves their care but may reject it once they lose trust in it. Expectations wise, participants envisioned several applications in pre-, peri­, and post-procedural workflows including order vetting, patient positioning, language translation, and artefact removal. On radiographers' role and career opportunities, some participants see an opportunity for radiographers to specialise in AI, becoming involved in algorithm development and its clinical implementation. DISCUSSION: Our findings suggest that widespread implementation of AI would require limited knowledge amongst radiographers and current AI limitations to be addressed. While radiographers are positively anticipating the integration of AI into their practices, they should also become actively involved in the development of AI tools such that those they envisioned. This would help align optimal use of AI tools and radiographer role changes. Patients' acceptance and reactions to AI also warrant further research.

Classifying attentional vulnerability to total sleep deprivation using baseline features of Psychomotor Vigilance Test performance.

There are strong individual differences in performance during sleep deprivation. We assessed whether baseline features of Psychomotor Vigilance Test (PVT) performance can be used for classifying participants' relative attentional vulnerability to total sleep deprivation. In a laboratory, healthy adults (n = 160, aged 18-30 years) completed a 10-min PVT every 2 h while being kept awake for ≥24 hours. Participants were categorized as vulnerable (n = 40), intermediate (n = 80), or resilient (n = 40) based on their number of PVT lapses during one night of sleep deprivation. For each baseline PVT (taken 4-14 h after wake-up time), a linear discriminant model with wrapper-based feature selection was used to classify participants' vulnerability to subsequent sleep deprivation. Across models, classification accuracy was about 70% (range 65-76%) using stratified 5-fold cross validation. The models provided about 78% sensitivity and 86% specificity for classifying resilient participants, and about 70% sensitivity and 89% specificity for classifying vulnerable participants. These results suggest features derived from a single 10-min PVT at baseline can provide substantial, but incomplete information about a person's relative attentional vulnerability to total sleep deprivation. In the long term, modeling approaches that incorporate baseline performance characteristics can potentially improve personalized predictions of attentional performance when sleep deprivation cannot be avoided.

Effects of total sleep deprivation on divided attention performance.

Dividing attention across two tasks performed simultaneously usually results in impaired performance on one or both tasks. Most studies have found no difference in the dual-task cost of dividing attention in rested and sleep-deprived states. We hypothesized that, for a divided attention task that is highly cognitively-demanding, performance would show greater impairment during exposure to sleep deprivation. A group of 30 healthy males aged 21-30 years was exposed to 40 h of continuous wakefulness in a laboratory setting. Every 2 h, subjects completed a divided attention task comprising 3 blocks in which an auditory Go/No-Go task was 1) performed alone (single task); 2) performed simultaneously with a visual Go/No-Go task (dual task); and 3) performed simultaneously with both a visual Go/No-Go task and a visually-guided motor tracking task (triple task). Performance on all tasks showed substantial deterioration during exposure to sleep deprivation. A significant interaction was observed between task load and time since wake on auditory Go/No-Go task performance, with greater impairment in response times and accuracy during extended wakefulness. Our results suggest that the ability to divide attention between multiple tasks is impaired during exposure to sleep deprivation. These findings have potential implications for occupations that require multi-tasking combined with long work hours and exposure to sleep loss.

Changes in Plasma Lipids during Exposure to Total Sleep Deprivation.

STUDY OBJECTIVES: The effects of sleep loss on plasma lipids, which play an important role in energy homeostasis and signaling, have not been systematically examined. Our aim was to identify lipid species in plasma that increase or decrease reliably during exposure to total sleep deprivation. DESIGN: Twenty individuals underwent sleep deprivation in a laboratory setting. Blood was drawn every 4 h and mass spectrometry techniques were used to analyze concentrations of 263 lipid species in plasma, including glycerolipids, glycerophospholipids, sphingolipids, and sterols. SETTING: Chronobiology and Sleep Laboratory, Duke-NUS Graduate Medical School. PARTICIPANTS: Healthy ethnic-Chinese males aged 21-28 y (n = 20). INTERVENTIONS: Subjects were kept awake for 40 consecutive hours. MEASUREMENTS AND RESULTS: Each metabolite time series was modeled as a sum of sinusoidal (circadian) and linear components, and we assessed whether the slope of the linear component differed from zero. More than a third of all individually analyzed lipid profiles exhibited a circadian rhythm and/or a linear change in concentration during sleep deprivation. Twenty-five lipid species showed a linear and predominantly unidirectional trend in concentration levels that was consistent across participants. Choline plasmalogen levels decreased, whereas several phosphatidylcholine (PC) species and triacylglycerides (TAG) carrying polyunsaturated fatty acids increased. CONCLUSIONS: The decrease in choline plasmalogen levels during sleep deprivation is consistent with prior work demonstrating that these lipids are susceptible to degradation by oxidative stress. The increase in phosphatidylcholines and triacylglycerides suggests that sleep loss might modulate lipid metabolism, which has potential implications for metabolic health in individuals who do not achieve adequate sleep.

Diurnal regulation of lipid metabolism and applications of circadian lipidomics.

The circadian timing system plays a key role in orchestrating lipid metabolism. In concert with the solar cycle, the circadian system ensures that daily rhythms in lipid absorption, storage, and transport are temporally coordinated with rest-activity and feeding cycles. At the cellular level, genes involved in lipid synthesis and fatty acid oxidation are rhythmically activated and repressed by core clock proteins in a tissue-specific manner. Consequently, loss of clock gene function or misalignment of circadian rhythms with feeding cycles (e.g., in shift work) results in impaired lipid homeostasis. Herein, we review recent progress in circadian rhythms research using lipidomics, i.e., large-scale profiling of lipid metabolites, to characterize circadian-regulated lipid pathways in mammals. In mice, novel regulatory circuits involved in fatty acid metabolism have been identified in adipose tissue, liver, and muscle. Extensive diversity in circadian regulation of plasma lipids has also been revealed in humans using lipidomics and other metabolomics approaches. In future studies, lipidomics platforms will be increasingly used to better understand the effects of genetic variation, shift work, food intake, and drugs on circadian-regulated lipid pathways and metabolic health.

Risk scoring for prediction of acute cardiac complications from imbalanced clinical data.

Fast and accurate risk stratification is essential in the emergency department (ED) as it allows clinicians to identify chest pain patients who are at high risk of cardiac complications and require intensive monitoring and early intervention. In this paper, we present a novel intelligent scoring system using heart rate variability, 12-lead electrocardiogram (ECG), and vital signs where a hybrid sampling-based ensemble learning strategy is proposed to handle data imbalance. The experiments were conducted on a dataset consisting of 564 chest pain patients recruited at the ED of a tertiary hospital. The proposed ensemble-based scoring system was compared with established scoring methods such as the modified early warning score and the thrombolysis in myocardial infarction score, and showed its effectiveness in predicting acute cardiac complications within 72 h in terms of the receiver operation characteristic analysis.

Individual differences in physiologic measures are stable across repeated exposures to total sleep deprivation.

Some individuals show severe cognitive impairment when sleep deprived, whereas others are able to maintain a high level of performance. Such differences are stable and trait-like, but it is not clear whether these findings generalize to physiologic responses to sleep loss. Here, we analyzed individual differences in behavioral and physiologic measures in healthy ethnic-Chinese male volunteers (n = 12; aged 22-30 years) who were kept awake for at least 26 h in a controlled laboratory environment on two separate occasions. Every 2 h, sustained attention performance was assessed using a 10-min psychomotor vigilance task (PVT), and sleepiness was estimated objectively by determining percentage eyelid closure over the pupil over time (PERCLOS) and blink rate. Between-subject differences in heart rate and its variability, and electroencephalogram (EEG) spectral power were also analyzed during each PVT. To assess stability of individual differences, intraclass correlation coefficients (ICC) were determined using variance components analysis. Consistent with previous work, individual differences in PVT performance were reproducible across study visits, as were baseline sleep measures prior to sleep deprivation. In addition, stable individual differences were observed during sleep deprivation for PERCLOS, blink rate, heart rate and its variability, and EEG spectral power in the alpha frequency band, even after adjusting for baseline differences in these measures (range, ICC = 0.67-0.91). These findings establish that changes in ocular, ECG, and EEG signals are highly reproducible across a night of sleep deprivation, hence raising the possibility that, similar to behavioral measures, physiologic responses to sleep loss are trait-like.

Sustained attention performance during sleep deprivation associates with instability in behavior and physiologic measures at baseline.

STUDY OBJECTIVES: To identify baseline behavioral and physiologic markers that associate with individual differences in sustained attention during sleep deprivation. DESIGN: In a retrospective study, ocular, electrocardiogram, and electroencephalogram (EEG) measures were compared in subjects who were characterized as resilient (n = 15) or vulnerable (n = 15) to the effects of total sleep deprivation on sustained attention. SETTING: Chronobiology and Sleep Laboratory, Duke-NUS Graduate Medical School Singapore. PARTICIPANTS: Healthy volunteers aged 22-32 years from the general population. INTERVENTIONS: Subjects were kept awake for at least 26 hours under constant environmental conditions. Every 2 hours, sustained attention was assessed using a 10-minute psychomotor vigilance task (PVT). MEASUREMENTS AND RESULTS: During baseline sleep and recovery sleep, EEG slow wave activity was similar in resilient versus vulnerable subjects, suggesting that individual differences in vulnerability to sleep loss were not related to differences in homeostatic sleep regulation. Rather, irrespective of time elapsed since wake, subjects who were vulnerable to sleep deprivation exhibited slower and more variable PVT response times, lower and more variable heart rate, and higher and more variable EEG spectral power in the theta frequency band (6.0-7.5 Hz). CONCLUSIONS: Performance decrements in sustained attention during sleep deprivation associate with instability in behavioral and physiologic measures at baseline. Small individual differences in sustained attention that are present at baseline are amplified during prolonged wakefulness, thus contributing to large between-subjects differences in performance and sleepiness.

Effects of exposure to intermittent versus continuous red light on human circadian rhythms, melatonin suppression, and pupillary constriction.

Exposure to light is a major determinant of sleep timing and hormonal rhythms. The role of retinal cones in regulating circadian physiology remains unclear, however, as most studies have used light exposures that also activate the photopigment melanopsin. Here, we tested the hypothesis that exposure to alternating red light and darkness can enhance circadian resetting responses in humans by repeatedly activating cone photoreceptors. In a between-subjects study, healthy volunteers (n = 24, 21-28 yr) lived individually in a laboratory for 6 consecutive days. Circadian rhythms of melatonin, cortisol, body temperature, and heart rate were assessed before and after exposure to 6 h of continuous red light (631 nm, 13 log photons cm(-2) s(-1)), intermittent red light (1 min on/off), or bright white light (2,500 lux) near the onset of nocturnal melatonin secretion (n = 8 in each group). Melatonin suppression and pupillary constriction were also assessed during light exposure. We found that circadian resetting responses were similar for exposure to continuous versus intermittent red light (P = 0.69), with an average phase delay shift of almost an hour. Surprisingly, 2 subjects who were exposed to red light exhibited circadian responses similar in magnitude to those who were exposed to bright white light. Red light also elicited prolonged pupillary constriction, but did not suppress melatonin levels. These findings suggest that, for red light stimuli outside the range of sensitivity for melanopsin, cone photoreceptors can mediate circadian phase resetting of physiologic rhythms in some individuals. Our results also show that sensitivity thresholds differ across non-visual light responses, suggesting that cones may contribute differentially to circadian resetting, melatonin suppression, and the pupillary light reflex during exposure to continuous light.

Heart rate variability can be used to estimate sleepiness-related decrements in psychomotor vigilance during total sleep deprivation.

STUDY OBJECTIVES: To assess whether changes in psychomotor vigilance during sleep deprivation can be estimated using heart rate variability (HRV). DESIGN: HRV, ocular, and electroencephalogram (EEG) measures were compared for their ability to predict lapses on the Psychomotor Vigilance Task (PVT). SETTING: Chronobiology and Sleep Laboratory, Duke-NUS Graduate Medical School Singapore. PARTICIPANTS: Twenty-four healthy Chinese men (mean age ± SD = 25.9 ± 2.8 years). INTERVENTIONS: Subjects were kept awake continuously for 40 hours under constant environmental conditions. Every 2 hours, subjects completed a 10-minute PVT to assess their ability to sustain visual attention. MEASUREMENTS AND RESULTS: During each PVT, we examined the electrocardiogram (ECG), EEG, and percentage of time that the eyes were closed (PERCLOS). Similar to EEG power density and PERCLOS measures, the time course of ECG RR-interval power density in the 0.02-0.08-Hz range correlated with the 40-hour profile of PVT lapses. Based on receiver operating characteristic curves, RR-interval power density performed as well as EEG power density at identifying a sleepiness-related increase in PVT lapses above threshold. RR-interval power density (0.02-0.08 Hz) also classified subject performance with sensitivity and specificity similar to that of PERCLOS. CONCLUSIONS: The ECG carries information about a person's vigilance state. Hence, HRV measures could potentially be used to predict when an individual is at increased risk of attentional failure. Our results suggest that HRV monitoring, either alone or in combination with other physiologic measures, could be incorporated into safety devices to warn drowsy operators when their performance is impaired.

Automated detection of sleep apnea from electrocardiogram signals using nonlinear parameters.

Sleep apnoea is a very common sleep disorder which can cause symptoms such as daytime sleepiness, irritability and poor concentration. To monitor patients with this sleeping disorder we measured the electrical activity of the heart. The resulting electrocardiography (ECG) signals are both non-stationary and nonlinear. Therefore, we used nonlinear parameters such as approximate entropy, fractal dimension, correlation dimension, largest Lyapunov exponent and Hurst exponent to extract physiological information. This information was used to train an artificial neural network (ANN) classifier to categorize ECG signal segments into one of the following groups: apnoea, hypopnoea and normal breathing. ANN classification tests produced an average classification accuracy of 90%; specificity and sensitivity were 100% and 95%, respectively. We have also proposed unique recurrence plots for the normal, hypopnea and apnea classes. Detecting sleep apnea with this level of accuracy can potentially reduce the need of polysomnography (PSG). This brings advantages to patients, because the proposed system is less cumbersome when compared to PSG.

Towards using photo-plethysmogram amplitude to measure blood pressure during sleep.

There is considerable interest in non-intrusive and reliable continuous ambulatory blood pressure measurement systems. Pulse amplitude is the peak to trough amplitude of the photo-plethysmogram signal. We compared pulse amplitude with a currently popular parameter, the pulse arrival time (PAT), for estimating continuous systolic blood pressure (SBP). Overnight sleep data from 18 young, healthy subjects (14 M 4 F, age 24+/-5 years, BMI 23.8+/-4.0 kg/m2) was analyzed. We found that pulse amplitude was more effective than PAT for estimating SBP during sleep. Mean coherence between pulse amplitude and SBP was significantly stronger than that for PAT [p<0.001, 95% CI: 0.21-0.25 (finger), 0.11-0.14 (wrist)]. Correlation between pulse amplitude and SBP was significantly stronger than that for PAT [p<0.001, 95% CI: 0.46-0.53 (finger), 0.13-0.20 (wrist)]. SBP estimation errors were significantly lower using pulse amplitude [p<0.001, 95% CI: -1.55 to -1.29 mmHg (finger), -0.53 to -0.36 mmHg (wrist)]. We also found that while pulse amplitude was closely related to SBP, the relationship weakened during and around REM sleep (ANOVA of REM, transitional Wake-REM and transitional REM-Sleep versus other sleep states: F=24.7, p<0.001). These results suggest that pulse amplitude is potentially a more suitable measure than pulse arrival time for estimating continuous blood pressure.

Analysis and automatic identification of sleep stages using higher order spectra.

Electroencephalogram (EEG) signals are widely used to study the activity of the brain, such as to determine sleep stages. These EEG signals are nonlinear and non-stationary in nature. It is difficult to perform sleep staging by visual interpretation and linear techniques. Thus, we use a nonlinear technique, higher order spectra (HOS), to extract hidden information in the sleep EEG signal. In this study, unique bispectrum and bicoherence plots for various sleep stages were proposed. These can be used as visual aid for various diagnostics application. A number of HOS based features were extracted from these plots during the various sleep stages (Wakefulness, Rapid Eye Movement (REM), Stage 1-4 Non-REM) and they were found to be statistically significant with p-value lower than 0.001 using ANOVA test. These features were fed to a Gaussian mixture model (GMM) classifier for automatic identification. Our results indicate that the proposed system is able to identify sleep stages with an accuracy of 88.7%.