Abstract: Wind energy is an important renewable clean energy resource. However, the stochastic and volatile nature of wind power brings significant challenges to the power system’s reliable and secure operation. Accurate and reliable wind power prediction is critical for the integration of wind power into the grid. The existing wind power forecasting (WPF) methods lack an assessment of the reliability of the predicted results, which may result in a financial penalty for the wind energy producers. An accurate prediction with reliability measurement is urgently needed to encounter the intricate nature of the problem. In this paper, a Bayesian framework-based bidirectional gated logic unit (BiGRU) method was proposed for ultra-short-term wind power forecasting. First, an encoder-decoder (ED)

Abstract: Retinal arteriovenous nicking (AVN) manifests as a reduced venular caliber of an arteriovenous crossing. AVNs are signs of many systemic, particularly cardiovascular diseases. Studies have shown that people with AVN are twice as likely to have a stroke. However, AVN classification faces two challenges. One is the lack of data, especially AVNs compared to the normal arteriovenous (AV) crossings. The other is the significant intra-class variations and minute inter-class differences. AVNs may look different in shape, scale, pose, and color. On the other hand, the AVN could be different from the normal AV crossing only by slight thinning of the vein. To address these challenges, first, we develop a data synthesis method to generate AV crossings, including normal and AVNs. Second, to

Abstract: Limitations in the current capability of monitoring PM2.5 adversely impact air quality management and health risk assessment of PM2.5 exposure. Commonly, ground-based monitoring networks are established to measure the PM2.5 concentrations in highly populated regions and protected areas such as national parks, yet large gaps exist in spatial coverage. Satellite-derived aerosol optical properties serve to complement the missing spatial information of ground-based monitoring networks. However, satellite remote sensing AODs are hampered under cloudy/hazy conditions or during nighttime. Here we strive to overcome the long-standing restriction that surface PM2.5 cannot be obtained with satellite remote sensing under cloudy/hazy conditions or during nighttime. In this work, we introduce

Abstract: PURPOSE: The purpose of this study was to investigate the quantitative retinal vascular morphological characteristics of Retinopathy of Prematurity (ROP) and Familial Exudative Vitreoretinopathy (FEVR) in the newborn by the application of a deep learning network with artificial intelligence. METHODS: Standard 130-degree fundus photographs centered on the optic disc were taken in the newborns. The deep learning network provided segmentation of the retinal vessels and the optic disc (OD). Based on the vessel segmentation, the vascular morphological characteristics, including avascular area, vessel angle, vessel density, fractal dimension (FD), and tortuosity, were automatically evaluated. RESULTS: 201 eyes of FEVR, 289 eyes of ROP, and 195 eyes of healthy individuals were included

Abstract: PURPOSE: The reported threshold of a near-infrared fluorescence detection probe (FDP) for judging parathyroid glands (PGs) is based on the autofluorescence intensity relative to other non-PG tissues, making it unreliable when not enough reference tissues are measured. We aim to convert FDP into a more convenient tool for identifying accidentally resected PGs by quantitative measurements of autofluorescence in resected tissues. METHODS: It was a prospective study approved by the Institutional Review Board. The research was divided into two stages: (1) In order to calibrate the novel FDP system, autofluorescence intensity of different in / ex vivo tissues was measured and the optimal threshold was obtained using receiver operating characteristic (ROC) curve. (2) To further validate

Abstract: PURPOSE: To achieve an accurate diagnosis of idiopathic epiretinal membrane (iERM) through analyzing retinal blood vessel oxygen saturation (SO2) and vascular morphological features in fundus images. METHODS: Dual-modal fundus camera was used to obtain color fundus image, 570-nm, and 610-nm images. As iERM affects the macular area, a macular-centered semicircle area as region of interest (MROI) was selected and analyzed SO2 and vascular morphologies in it. Eventually, random forest (RF) and support vector machine (SVM) were as classifiers to diagnose iERM patients. RESULTS: The arterial and venous SO2 levels of the iERM group were significantly higher than that of the control group. There were significant differences in the vessel density and fractal dimension on the artery for v

Abstract: Early and accurate diagnosis of Alzheimer's disease (AD) and its prodromal period mild cognitive impairment (MCI) is essential for the delayed disease progression and the improved quality of patients' life. The emerging computer-aided diagnostic methods that combine deep learning with structural magnetic resonance imaging (sMRI) have achieved encouraging results, but some of them are limit of issues such as data leakage, overfitting, and unexplainable diagnosis. In this research, we propose a novel end-to-end deep learning approach for automated diagnosis of AD. This approach has the following differences from the current approaches: (1) Convolutional Neural Network (CNN) models of different structures and capacities are evaluated systemically and the most suitable model is adopt

Abstract: Telemedicine has the potential to overcome the unequal distribution of medical resources worldwide. In this study, we report the second-generation co-axial projective imaging (CPI-2) system featured with orthotopic image projection for augmented reality surgical telementoring. The CPI-2 system can acquire surgical scene images from the local site, transmit them wirelessly to the remote site, and project the virtual annotations drawn by a remote expert with great accuracy to the surgical field. The performance characteristics of the CPI-2 system are quantitatively verified in benchtop experiments. The ex vivo study that compares the CPI-2 system and a monitor-based telementoring system shows that the CPI-2 system can reduce the focus shift and avoid subjective mapping of the instr

Abstract: Deep convolutional neural network (DCNN) models have been widely explored for skin disease diagnosis and some of them have achieved the diagnostic outcomes comparable or even superior to those of dermatologists. However, broad implementation of DCNN in skin disease detection is hindered by small size and data imbalance of the publically accessible skin lesion datasets. This paper proposes a novel single-model based strategy for classification of skin lesions on small and imbalanced datasets. First, various DCNNs are trained on different small and imbalanced datasets to verify that the models with moderate complexity outperform the larger models. Second, regularization DropOut and DropBlock are added to reduce overfitting and a Modified RandAugment augmentation strategy is propose

Abstract: BACKGROUND: Lack of intuitiveness and poor hand-eye coordination present a major technical challenge in neurosurgical navigation. METHODS: We developed an integrated dexterous stereotactic co-axial projection imaging (sCPI) system featuring orthotopic image projection for augmented reality (AR) neurosurgical navigation. The performance characteristics of the sCPI system, including projection resolution and navigation accuracy, were quantitatively verified. The resolution of the sCPI was tested with a USAF1951 resolution test chart. The stereotactic navigation accuracy of the sCPI was measured using a calibration panel with a 7x7 circle array pattern. In benchtop validation, the navigation accuracy of the sCPI and the BrainLab Kick Navigation Station was compared using a skull pha

Abstract: Objective and Impact Statement. There is a need to develop high-performance and low-cost data augmentation strategies for intelligent skin cancer screening devices that can be deployed in rural or underdeveloped communities. The proposed strategy can not only improve the classification performance of skin lesions but also highlight the potential regions of interest for clinicians' attention. This strategy can also be implemented in a broad range of clinical disciplines for early screening and automatic diagnosis of many other diseases in low resource settings. Methods. We propose a high-performance data augmentation strategy of search space 10(1), which can be combined with any model through a plug-and-play mode and search for the best argumentation method for a medical database

Abstract: PURPOSE: The purpose of this study is to investigate the retinal vascular morphological characteristics in high myopia patients of different severity. METHODS: 317 eyes of high myopia patients and 104 eyes of healthy control subjects were included in this study. The severity of high myopia patients is classified into C0-C4 according to the Meta Analysis of the Pathologic Myopia (META-PM) classification and their vascular morphological characteristics in ultra-wide field imaging were analyzed using transfer learning methods and RU-net. Correlation with axial length (AL), best corrected visual acuity (BCVA) and age was analyzed. In addition, the vascular morphological characteristics of myopic choroidal neovascularization (mCNV) patients and their matched high myopia patients were