Blinding assessment in clinical trials of traditional Chinese medicine:Exploratory principles and protocol

As one of the key components of clinical trials, blinding, if successfully implemented, can help to mitigate the risks of implementation bias and measurement bias, consequently improving the validity and reliability of the trial results. However, successful blinding in clinical trials of traditional Chinese medicine(TCM) is hard to achieve, and the evaluation of blinding success through blinding assessment lacks established guidelines. Taking into account the challenges associated with blinding in the TCM field, here we present a framework for assessing blinding. Further, this study proposes a blinding assessment protocol for TCM clinical trials, building upon the framework and the existing methods. An assessment report checklist and an approach for evaluating the assessment results are presented based on the proposed protocol. It is anticipated that these improvements to blinding assessment will generate greater awareness among researchers, facilitate the standardization of blinding, and augment the blinding effectiveness. The use of this blinding assessment may further advance the quality and precision of TCM clinical trials and improve the accuracy of the trial results. The blinding assessment protocol will undergo continued optimization and refinement, drawing upon expert consensus and experience derived from clinical trials.

Blind Image Quality Assessment Based on Wavelet Power Spectrum in Perceptual Domain

Blind image quality assessment(BIQA) can assess the perceptual quality of a distorted image without a prior knowledge of its reference image or distortion type. In this paper, a novel BIQA model is developed in wavelet domain. Considering the multi-resolution and band-passing characteristics of discrete wavelet transform(DWT), an improvement over the power spectrum is put forward, i.e., dubbed wavelet power spectrum(WPS)estimation. Then, the concept of directional WPS is applied to simplify the calculation. Moreover, a rotationally symmetric modulation transfer function(MTF) of human visual system(HVS) is integrated as a filter, which makes the metric to be consistent with the human vision perception and more discriminative. Experiments are conducted on the LIVE databases and three other databases, and the results show that the proposed metric is highly correlated with subjective evaluations and it competes well with other state-of-the-art metrics in terms of effectiveness and robustness.

Relationship between diabetes mellitus and cataract in Hungary

AIM:To examine the coexistence of diabetes mellitus(DM)and cataract in Hungary.The effects of DM on the cataract surgical results were also in the focus of analysis.METHODS:Statistical data analysis of the results of the Rapid Assessment of Avoidable Blindness with Diabetic Retinopathy(RAAB+DR)module conducted in Hungary in 2015.This cross-sectional,population-based,national survey included 3523 people aged 50 years and over.Participants of the survey were examined on-site.Visual acuity,main cause for visual impairment(using direct and indirect ophthalmoscopes),in case of best corrected visual acuity(BCVA)≤0.5 and blood glucose level(random test with glucometer)were examined.RESULTS:The prevalence of cataract was 23.4%,and DM was 20.0%.The occurrence of cataract steadily increased with age.Among the examined participants with DM,the prevalence of cataract was significantly(P=0.012)higher(+35%)than that in non-diabetic subjects(29.5%vs 21.8%).Following aging(OR=15.2%,P<0.001),DM proved to be the most independent influencing risk factor(OR=49.9%,P<0.001).The presence of DM was neither an influencing factor for complications of cataract surgery,nor for postoperative visual acuity.CONCLUSION:DM appears to be one of the main risk factors for developing cataract.Other risk factors,such as age,sex and environment also play an influencing role.Diabetes does not seem to affect the occurrence of cataract surgical complications.

Cataract blindness in Hungary

AIM:To estimate the prevalence of blindness and visual impairment resulting from cataract in the population aged≥50 y in Hungary,and to assess the cataract surgical services.METHODS:A rapid assessment of avoidable blindness(RAAB)was conducted.A total of 3523 eligible people were randomly selected and examined.Each participant underwent surgery for cataract was interviewed with regard to the year,place,and costs of the surgery.Participants with obvious cataract were asked why they had not yet undergone surgery(barriers to surgery).RESULTS:An estimated 12514 people were bilaterally blind;the visual acuity(VA)in 19293 people was<6/60,and the VA in 73962 people was<6/18 in the better eye due to cataract.An estimated 77933 eyes are blind;98067 eyes had a VA of<6/60,and an estimated 277493 eyes had a VA of<6/18 due to cataract.Almost all cataract surgeries were conducted in government hospitals.The age-and sexadjusted cataract surgical coverage with VA<3/60 in eyes was 90.0%.The rate of good visual outcome after surgery was 79.5%.Ocular comorbidity was the main cause of poor outcome(78.1%),followed by late complications(such as posterior capsule opacification)(17.2%),inadequate optical correction(3.1%),and surgical complications(1.6%).The main barrier to surgery in people with bilateral cataract and VA of<6/60 was‘need not felt’.CONCLUSION:The prevalence of visual impairment resulting from cataract is slightly higher than expected.The quality of the cataract surgical service seems adequate in Hungary.However,the number of cataract operations per year should continue to increase due to the increasing patient demands and the aging population.

A multimodal dense convolution network for blind image quality assessment

Technological advancements continue to expand the communications industry’s potential.Images,which are an important component in strengthening communication,are widely available.Therefore,image quality assessment(IQA)is critical in improving content delivered to end users.Convolutional neural networks(CNNs)used in IQA face two common challenges.One issue is that these methods fail to provide the best representation of the image.The other issue is that the models have a large number of parameters,which easily leads to overfitting.To address these issues,the dense convolution network(DSC-Net),a deep learning model with fewer parameters,is proposed for no-reference image quality assessment(NR-IQA).Moreover,it is obvious that the use of multimodal data for deep learning has improved the performance of applications.As a result,multimodal dense convolution network(MDSC-Net)fuses the texture features extracted using the gray-level co-occurrence matrix(GLCM)method and spatial features extracted using DSC-Net and predicts the image quality.The performance of the proposed framework on the benchmark synthetic datasets LIVE,TID2013,and KADID-10k demonstrates that the MDSC-Net approach achieves good performance over state-of-the-art methods for the NR-IQA task.