Multimodal Eye Imaging, Retina Characteristics, and Psychological Assessment Dataset.

The eyes provide insights into psychology, potentially offering a distinctive perspective for psychological health profiles. However, there exist a notable deficiency in datasets that simultaneously encompass eye features and psychological assessments. To address this gap, our study presents a dataset that included Fundus Photography, Psychological Assessment, Retina Characteristics, and Multimodal Imaging (FPRM). FPRM dataset comprise fundus images at different wavelengths (548 nm and 605 nm), image of oxygen saturation for the retina and 8 specific retinal vessels, videos of retinal blood flow and pupillary light reflex, along with 61 items of multimodal quantitative measurement from 384 participants. Additionally, it features psychological assessments across five dimensions (geriatric depression, generalized anxiety disorder, insomnia, activities of daily living, and deterioration), accompanied by fundus photographs and 6 items of retina characteristics from 1683 participants. FPRM dataset is the first to integrate multimodal ophthalmic data and psychological assessments, not only advancing the development of machine learning applications but also facilitating in-depth research into the relationship between eye health and psychological health profiles.

Off-pump Versus On-pump Coronary Artery Bypass Grafting in Diabetic patients: A Meta-analysis of Observational Studies with a Propensity-Score Analysis.

PURPOSE: The debate between off-pump coronary artery bypass grafting (OPCAB) and on-pump coronary artery bypass grafting (ONCAB) in diabetic patients remains. This meta-analysis aimed to investigate outcomes after OPCAB versus ONCAB for patients with diabetes. METHODS: Literature research was conducted up to December 2023 using Ovid Medline, EMBASE, and the Cochrane Library. Eligible studies were observational studies with a propensity-score analysis of OPCAB versus ONCAB. The primary outcomes were early mortality and mid-term survival. The secondary outcomes were cerebrovascular accidents, reoperation for bleeding, incomplete revascularization, myocardial infarction, low cardiac output, and renal replacement therapy. RESULTS: Our research identified seven observational studies with a propensity-score analysis enrolling 13,085 patients. There was no significant difference between OPCAB and ONCAB for early mortality, mid-term survival, myocardial infarction, low cardiac output, and renal replacement therapy. OPCAB was associated with a lower risk of cerebrovascular accidents (OR 0.43; 95% CI, 0.24-0.76, P = 0.004) and reoperation for bleeding (OR 0.60; 95% CI, 0.41-0.88, P = 0.009). However, OPCAB was associated with a higher risk of incomplete revascularization (OR 2.07; 95% CI, 1.60-2.68, P < 0.00001). CONCLUSION: Among patients with diabetes, no difference in early mortality and mid-term survival was observed. However, OPCAB was associated with a lower incidence of morbidity, including cerebrovascular accidents and reoperation for bleeding.

Design and characterization of a hybrid PET detector with DOI capability.

BACKGROUND: Monolithic or semi-monolithic detectors are attractive for positron emission tomography (PET) scanners with depth-of-interaction (DOI) capability. However, they often require complicated calibrations to determine the interaction positions of gamma photons. PURPOSE: We introduce a novel hybrid detector design that combines pixelated and semi-monolithic elements to achieve DOI capability while simplifying the calibrations for positioning. METHODS: A prototype detector with eight hybrid lutetium-yttrium oxyorthosilicate (LYSO) layers having dimensions of 25.8 × 12.9 × 15 mm3 was constructed. The energy-weighted and energy-squared weighted averages were used for estimating the x- (pixelated direction) and y-positions (non-pixelated direction). Pseudo-pixels were defined as discrete areas on the flood image based on the crystal look-up table (LUT). The intrinsic spatial resolutions in the pixelated and non-pixelated directions were measured. The ratio of the maximum to the sum of the multipixel photon counter (MPPC) signals was used to estimate the DOI positions. The coincidence timing resolution (CTR) was measured using the average and energy-weighted average of the earliest n time stamps. Two energy windows of 250-700 and 400-600 keV were applied for the measurements. RESULTS: The pattern of the flood images showed discrete event clusters, demonstrating that simple calibrations for determining the x- and y-positions of events could be achieved. Under 400-600 keV energy window, the average intrinsic spatial resolutions were 1.15 and 1.34 mm for the pixelated and non-pixelated directions; the average DOI resolution of the second row of pseudo-pixels was 5.1 mm in full width at half maximum (FWHM); when using the energy-weighted average of the earliest four-time stamps, the best CTR of 350 ps was achieved. Applying a broader energy window of 250-700 keV only slightly degrades the DOI resolution while maintaining the intrinsic resolution; the best CTR degrades to 410 ps. CONCLUSIONS: The proposed hybrid detector concept was verified, and a prototype detector showed high performance for 3D positioning and timing resolution. The novel detector concept shows promise for preclinical and clinical PET scanners with DOI capability.

Association between triglyceride glucose-waist height ratio index and cardiovascular disease in middle-aged and older Chinese individuals: a nationwide cohort study.

BACKGROUND: The triglyceride-glucose (TyG) index and its combination with obesity indicators can predict cardiovascular diseases (CVD). However, there is limited research on the relationship between changes in the triglyceride glucose-waist height ratio (TyG-WHtR) and CVD. Our study aims to investigate the relationship between the change in the TyG-WHtR and the risk of CVD. METHODS: Participants were from the China Health and Retirement Longitudinal Study (CHARLS). CVD was defined as self-reporting heart disease and stroke. Participants were divided into three groups based on changes in TyG-WHtR using K-means cluster analysis. Multivariable binary logistic regression analysis was used to examine the association between different groups (based on the change of TyG-WHtR) and CVD. A restricted cubic spline (RCS) regression model was used to explore the potential nonlinear association of the cumulative TyG-WHtR and CVD events. RESULTS: During follow-up between 2015 and 2020, 623 (18.8%) of 3312 participants developed CVD. After adjusting for various potential confounders, compared to the participants with consistently low and stable TyG-WHtR, the risk of CVD was significantly higher in participants with moderate and increasing TyG-WHtR (OR 1.28, 95%CI 1.01-1.63) and participants with high TyG-WHtR with a slowly increasing trend (OR 1.58, 95%CI 1.16-2.15). Higher levels of cumulative TyG-WHtR were independently associated with a higher risk of CVD events (per SD, OR 1.27, 95%CI 1.12-1.43). CONCLUSIONS: For middle-aged and older adults, changes in the TyG-WHtR are independently associated with the risk of CVD. Maintaining a favorable TyG index, effective weight management, and a reasonable waist circumference contribute to preventing CVD.

A novel relative-equilibrium graphical plot for rapid reversible tracer studies in dynamic PET imaging.

Objective.This study aims to address the issue of long scan durations required by traditional graphical analysis methods, such as the Logan plot and its variant, the reversible equilibrium (RE) Logan plot, for dynamic PET imaging of tracer kinetics.Approach.We propose a relative RE Logan model that builds on the principles of the Logan plot and its variant to significantly reduce scan time without compromising the accuracy of tracer kinetics analysis. The model is supported by theoretical evidence and experimental validations, including two computer simulations and one clinical data analysis.Main results.The proposed model demonstrates a significant linear relationship between the variablexand the slopeDVTof the RE Logan plot, and the variablex' and the slopeDVT'of the relative RE Logan plot. The Pearson correlation coefficients (r) of the linear fitting of thex' to thexequal 0.9849 in the simulated data and 0.9912 in the clinical data. Similarly, thervalues for the linear fitting ofDVT'toDVTequal 0.9989 and 0.9988 in the simulated data, and 0.9954 in the clinical data.Significance.These results demonstrate the model's capability to maintain strong linear relationships and produce parametric images comparable to the traditional RE Logan plot, but with the considerable advantage of shorter scan durations. This innovation holds significant potential for enhancing the efficiency and feasibility of PET imaging in clinical settings.

Boosting Weakly Supervised Object Localization and Segmentation With Domain Adaption.

Weakly supervised object localization (WSOL), adopting only image-level annotations to learn the pixel-level localization model, can release human resources in the annotation process. Most one-stage WSOL methods learn the localization model with multi-instance learning, making them only activate discriminative object parts rather than the whole object. In our work, we attribute this problem to the domain shift between the training and test process of WSOL and provide a novel perspective that views WSOL as a domain adaption (DA) task. Under this perspective, a DA-WSOL pipeline is elaborated to better assist WSOL with DA approaches by considering the specificities for the adaption of WSOL. Our DA-WSOL pipeline can discern the source-related and the Universum samples from other target samples based on a proposed target sampling strategy and then utilize them to solve the sample unbalancing and label unmatching between the source and target domain of WSOL. Experiments show that our pipeline outperforms SOTA methods on three WSOL benchmarks and can improve the performance of downstream weakly supervised semantic segmentation tasks. Codes are available at https://github.com/zh460045050/dawsol.

Megahertz multi-parametric ophthalmic OCT system for whole eye imaging.

An ultrahigh-speed, wide-field OCT system for the imaging of anterior, posterior, and ocular biometers is crucial for obtaining comprehensive ocular parameters and quantifying ocular pathology size. Here, we demonstrate a multi-parametric ophthalmic OCT system with a speed of up to 1 MHz for wide-field imaging of the retina and 50 kHz for anterior chamber and ocular biometric measurement. A spectrum correction algorithm is proposed to ensure the accurate pairing of adjacent A-lines and elevate the A-scan speed from 500 kHz to 1 MHz for retinal imaging. A registration method employing position feedback signals was introduced, reducing pixel offsets between forward and reverse galvanometer scanning by 2.3 times. Experimental validation on glass sheets and the human eye confirms feasibility and efficacy. Meanwhile, we propose a revised formula to determine the "true" fundus size using all-axial length parameters from different fields of view. The efficient algorithms and compact design enhance system compatibility with clinical requirements, showing promise for widespread commercialization.

Predicting the cognitive impairment with multimodal ophthalmic imaging and artificial neural network for community screening.

BACKGROUND/AIMS: To investigate the comprehensive prediction ability for cognitive impairment in a general elder population using the combination of the multimodal ophthalmic imaging and artificial neural networks. METHODS: Patients with cognitive impairment and cognitively healthy individuals were recruited. All subjects underwent medical history, blood pressure measurement, the Montreal Cognitive Assessment, medical optometry, intraocular pressure and custom-built multimodal ophthalmic imaging, which integrated pupillary light reaction, multispectral imaging, laser speckle contrast imaging and retinal oximetry. Multidimensional parameters were analysed by Student's t-test. Logistic regression analysis and back-propagation neural network (BPNN) were used to identify the predictive capability for cognitive impairment. RESULTS: This study included 104 cognitive impairment patients (61.5% female; mean (SD) age, 68.3 (9.4) years), and 94 cognitively healthy age-matched and sex-matched subjects (56.4% female; mean (SD) age, 65.9 (7.6) years). The variation of most parameters including decreased pupil constriction amplitude (CA), relative CA, average constriction velocity, venous diameter, venous blood flow and increased centred retinal reflectance in 548 nm (RC548) in cognitive impairment was consistent with previous studies while the reduced flow acceleration index and oxygen metabolism were reported for the first time. Compared with the logistic regression model, BPNN had better predictive performance (accuracy: 0.91 vs 0.69; sensitivity: 93.3% vs 61.70%; specificity: 90.0% vs 68.66%). CONCLUSIONS: This study demonstrates retinal spectral signature alteration, neurodegeneration and angiopathy occur concurrently in cognitive impairment. The combination of multimodal ophthalmic imaging and BPNN can be a useful tool for predicting cognitive impairment with high performance for community screening.

Suppressing label noise in medical image classification using mixup attention and self-supervised learning.

Deep neural networks (DNNs) have been widely applied in medical image classification and achieve remarkable classification performance. These achievements heavily depend on large-scale accurately annotated training data. However, label noise is inevitably introduced in the medical image annotation, as the labeling process heavily relies on the expertise and experience of annotators. Meanwhile, DNNs suffer from overfitting noisy labels, degrading the performance of models. Therefore, in this work, we innovatively devise a noise-robust training approach to mitigate the adverse effects of noisy labels in medical image classification. Specifically, we incorporate contrastive learning and intra-group mixup attention strategies into vanilla supervised learning. The contrastive learning for feature extractor helps to enhance visual representation of DNNs. The intra-group mixup attention module constructs groups and assigns self-attention weights for group-wise samples, and subsequently interpolates massive noisy-suppressed samples through weighted mixup operation. We conduct comparative experiments on both synthetic and real-world noisy medical datasets under various noise levels. Rigorous experiments validate that our noise-robust method with contrastive learning and mixup attention can effectively handle with label noise, and is superior to state-of-the-art methods. An ablation study also shows that both components contribute to boost model performance. The proposed method demonstrates its capability of curb label noise and has certain potential toward real-world clinic applications.

The retinal oxygen metabolism and hemodynamics as a substitute for biochemical tests to predict nonproliferative diabetic retinopathy.

Predicting the occurrence of nonproliferative diabetic retinopathy (NPDR) using biochemical parameters is invasive, which limits large-scale clinical application. Noninvasive retinal oxygen metabolism and hemodynamics of 215 eyes from 73 age-matched healthy subjects, 90 diabetic patients without DR, 40 NPDR, and 12 DR with postpanretinal photocoagulation were measured with a custom-built multimodal retinal imaging device. Diabetic patients underwent biochemical examinations. Two logistic regression models were developed to predict NPDR using retinal and biochemical metrics, respectively. The predictive model 1 using retinal metrics incorporated male gender, insulin treatment condition, diastolic duration, resistance index, and oxygen extraction fraction presented a similar predictive power with model 2 using biochemical metrics incorporated diabetic duration, diastolic blood pressure, and glycated hemoglobin A1c (area under curve: 0.73 vs. 0.70; sensitivity: 76% vs. 68%; specificity: 64% vs. 62%). These results suggest that retinal oxygen metabolic and hemodynamic biomarkers may replace biochemical parameters to predict the occurrence of NPDR .

A multi-resolution TOF-DOI detector for human brain dedicated PET scanner.

Objective. We propose a single-ended readout, multi-resolution detector design that can achieve high spatial, depth-of-interaction (DOI), and time-of-flight (TOF) resolutions, as well as high sensitivity for human brain-dedicated positron emission tomography (PET) scanners.Approach. The detector comprised two layers of LYSO crystal arrays and a lightguide in between. The top (gamma ray entrance) layer consisted of a 16 × 16 array of 1.53 × 1.53 × 6 mm3LYSO crystals for providing high spatial resolution. The bottom layer consisted of an 8 × 8 array of 3.0 × 3.0 × 15 mm3LYSO crystals that were one-to-one coupled to an 8 × 8 multipixel photon counter (MPPC) array for providing high TOF resolution. The 2 mm thick lightguide introduces inter-crystal light sharing that causes variations of the light distribution patterns for high DOI resolution. The detector was read out by a PETsys TOFPET2 application-specific integrated circuit.Main result. The top and bottom layers were distinguished by a convolutional neural network with 97% accuracy. All crystals in the top and bottom layers were resolved. The inter-crystal scatter (ICS) events in the bottom layer were identified, and the measured average DOI resolution of the bottom layer was 4.1 mm. The coincidence time resolution (CTR) for the top-top, top-bottom, and bottom-bottom coincidences was 476 ps, 405 ps, and 298 ps, respectively. When ICS events were excluded from the bottom layer, the CTR of the bottom-bottom coincidence was 277 ps.Significance. The top layer of the proposed two-layer detector achieved a high spatial resolution and the bottom layer achieved a high TOF resolution. Together with its high DOI resolution and detection efficiency, the proposed detector is well suited for next-generation high-performance brain-dedicated PET scanners.