Objective Quality Score Research Articles

Clinical Feasibility of 5.0 T MRI/MRCP in Characterizing Pancreatic Cystic Lesions: Comparison with 3.0 T and MDCT

Objectives: To assess the feasibility of 5.0 T magnetic resonance imaging (MRI) in characterizing pancreatic cystic lesions (PCLs), compared with 3.0 T MRI and multidetector computed tomography (MDCT). Methods: Thirty-five patients with PCLs underwent 5.0 T MR alongside 3.0 T MR or MDCT. Two observers measured subjective and objective image quality scores. The consistency of two observers between 5.0 T and 3.0 T was calculated by intraclass correlation coefficients. The characteristics of PCLs and their specific diagnosis, as well as benignity/malignancy, were evaluated across MDCT, 3.0 T, and 5.0 T MRI. Results: The 5.0 T MR demonstrated significantly higher subjective image quality and SNR on T1WI compared to that in 3.0 T MR (p < 0.05). The 5.0 T MRI identified more cyst lesions than the 3.0 T MRI (40 and 32) and MDCT (82 and 56). The sensitivity, specificity, and accuracy for differentiating benign from malignant lesions with 5.0 T MRI (75%, 100%, and 91.4%, respectively) surpassed those of 3.0 T MRI and MDCT. The accuracy of the specific diagnosis of PCLs at 5.0 T MRI (80%) was superior to 3.0 T MRI and MDCT. Conclusions: 5.0 T MRI exhibits certain superiority in delineating details of PCLs and in clinical diagnostic accuracy, outperforming MDCT and 3.0 T MRI while maintaining sufficient image quality.

Image quality and detection efficacy of zero echo time magnetic resonance imaging on Lung-RADS 2 pulmonary ground-glass nodules in comparison to thin-slice fat-saturated T2-weighted imaging.

Widely used computed tomography (CT) screening increases the detection of pulmonary pure ground-glass nodules (pGGNs), often classified as the second category of Lung Imaging Reporting and Data System (Lung-RADS 2). Despite their low malignancy risk, these nodules pose significant challenges and necessitate accurate assessment to minimize the risk of long-term follow-ups. This study investigated the detection efficacy of zero echo time (ZTE) magnetic resonance imaging (MRI) and thin-slice fat-saturated T2-weighted imaging (T2WI-FS) on 3.0 T MRI on the predictive accuracy of invasiveness for Lung-RADS 2 pGGNs. This prospective study enrolled 83 consecutive patients with 110 pGGNs who underwent preoperative CT and MRI scans. All CT images were assessed by artificial intelligence (AI) software and confirmed by a thoracic radiologist. Another two radiologists blind to pathology results assessed MRI for image quality (objective and subjective evaluations) and detection of pGGNs. Differences in nodule diameter, CT density and detection rate were compared within different pathological groups. The objective and subjective image quality scores were compared using the Wilcoxon signed rank test between ZTE and T2WI-FS. Interobserver agreement was calculated using the kappa coefficient. Receiver operating characteristic (ROC) curve analysis evaluated the diagnostic accuracy for distinguishing invasiveness. Among the 110 pGGNs evaluated, T2WI-FS demonstrated a higher detection rate (80.0%) compared to ZTE (51.8%). ZTE showed a superior signal-to-noise ratio (SNR) in the lung parenchyma, aorta, and peripheral lung structures, whereas T2WI-FS more effectively delineated tracheal walls and pulmonary nodules. Both observers rated ZTE higher for vascular and bronchial visibility, while T2WI-FS was better in terms of lower noise and fewer artifacts. Notably, ZTE visibility varied with pathological results, exhibiting a range from 0% in atypical adenomatous hyperplasia (AAH) to 94.1% in invasive adenocarcinoma (IAC). The key indicators for distinguishing invasive pGGNs from non-invasive ones were nodule diameter [area under the curve (AUC) =0.874], ZTE visibility (AUC =0.740), followed by CT values (AUC =0.682) and T2WI-FS visibility (AUC =0.678). MRI has the potential to detect and predict the invasiveness of pGGN. Both T2WI-FS and ZTE demonstrate reliable image quality in pulmonary imaging, each displaying strengths in visualizing pGGN. Thin-slice T2WI-FS has a superior detection rate, while ZTE better predicts histological invasiveness.

Research on a Multidimensional Digital Printing Image Quality Evaluation Method Based on MLP Neural Network Regression

High-quality printing is a longstanding objective in the printing and replication industry. However, the methods used to evaluate print quality suffer from subjectivity and multidimensionality, relying on personal preferences and subjective perceptions to assess the quality of printed images, which poses significant limitations. To address these issues, a set of evaluation metrics aimed at assessing the quality of digital printing products is proposed to achieve evaluation results consistent with human visual perception. Given the differing imaging principles of pre-press digital images and post-scan images, these images are first preprocessed to standardize them for comparison. Next, features are extracted in both spatial and frequency domains, and similarity metrics are used to quantify the differences in features between pre-press digital images and post-scan images. Finally, a multilayer perceptron (MLP) neural network regression model is trained to predict the final objective quality scores. Experimental results on two standard databases demonstrate that this metric exhibits high consistency in both subjective and objective quality evaluation metrics for printed image quality assessment and outperforms other metrics in terms of accuracy.

Don't judge a book or health app by its cover: User ratings and downloads are not linked to quality.

To analyse the relationship between health app quality with user ratings and the number of downloads of corresponding health apps. Utilising a dataset of 881 Android-based health apps, assessed via the 300-point objective Organisation for the Review of Care and Health Applications (ORCHA) assessment tool, we explored whether subjective user-level indicators of quality (user ratings and downloads) correlate with objective quality scores in the domains of user experience, data privacy and professional/clinical assurance. For this purpose, we applied spearman correlation and multiple linear regression models. For user experience, professional/clinical assurance and data privacy scores, all models had very low adjusted R squared values (< .02). Suggesting that there is no meaningful link between subjective user ratings or the number of health app downloads and objective quality measures. Spearman correlations suggested that prior downloads only had a very weak positive correlation with user experience scores (Spearman = .084, p = .012) and data privacy scores (Spearman = .088, p = .009). There was a very weak negative correlation between downloads and professional/clinical assurance score (Spearman = -.081, p = .016). Additionally, user ratings demonstrated a very weak correlation with no statistically significant correlations observed between user ratings and the scores (all p > 0.05). For ORCHA scores multiple linear regression had adjusted R-squared = -.002. This study highlights that widely available proxies which users may perceive to signify the quality of health apps, namely user ratings and downloads, are inaccurate predictors for estimating quality. This indicates the need for wider use of quality assurance methodologies which can accurately determine the quality, safety, and compliance of health apps. Findings suggest more should be done to enable users to recognise high-quality health apps, including digital health literacy training and the provision of nationally endorsed "libraries".

Feasibility of an Integrated Digital and Pharmacological Approach Targeting Blood Lipids in Atherosclerotic Cardiovascular Disease Management

Objectives: Strong evidence supports the importance of lipid-lowering and exercise therapies in the long-term therapy of atherosclerotic cardiovascular disease (ASCVD). Establishing efficient comprehensive intervention programs that combine pharmacological and exercise prescription is crucial to investigate with the aid of digital technologies. Methods: A convenience sample of 25 ASCVD patients (57.8 ± 9.5 years, 76% males) was gathered. All participants were prescribed with 12-week home exercise program supervised by an app and bimonthly 75mg alirocumab subcutaneous injections. Follow up visits were scheduled at the end of 4th and 12th week. Results: Nineteen participants completed the program with a retention rate of 76%. Sixteen (84.2%) participants received all six doses of alirocumab. The total management time of the 12-week program added up to 65.47 minutes per patient. Satisfaction score was 4.2 ± 0.6 and the User Version of the Mobile Application Rating Scale (uMARS) overall objective quality score on the app was 3.4 ± 0.7. At the 4-week and 12-week follow-ups, LDL-C levels reduced compared to baseline (−1.5 ± 0.8 mmol/L, P &lt; 0.001, −1.6 ± 0.8 mmol/L, P &lt; 0.001, respectively), along with TC (−1.8 ± 1.2 mmol/L, P &lt; 0.001, −1.6 ± 1.3 mmol/L, P &lt; 0.001, respectively), but not TG, HDL-C, GAD-7 and PHQ-9. Conclusion: The integrated pharmaceutical and digital ERx intervention program was feasible and well accepted in ASCVD patients.

Kompresija JPEG i BPG bez gubitka vizuelnih informacija na primeru baze KonJND-1k

Introduction/purpose: This paper presents the results of the research on visually lossless image compression which is of particular interest because it achieves a high degree of compression, while the visual quality of the image is not impaired, i.e., end users are very satisfied with the image quality. The analysis was carried out using the publicly available large-scale picture-wise KonJND-1k database which contains the results of subjective tests on JPEG and BPG compressed images. Methods: Thanks to the availability of images from the KonJND-1k database, the dependence of objective assessments of image quality on parameters that control the degree of compression of source signals (quality factor for JPEG and quantization parameter for BPG) is analyzed. The results of the visually lossless subjective tests are used for a deep analysis of the boundary and typical values of the parameters that control these two types of compression, as well as for the analysis of the corresponding values of the objective quality scores. Furthermore, reliable features for predicting the boundary between visually lossless and visually lossy compression have been identified. For that purpose, the degree of agreement between the predictions and the ground truth values of the peak signal-to-noise ratio (PSNR) and image representation in bits per pixel (bpp) is used. The visually lossless compression ratio is used to compare JPEG and BPG techniques. Results: It is shown that the boundary between visually lossless and visually lossy image compression is found in a wide range of PSNR values (about 20 dB for JPEG and 15 dB for BPG). The corresponding JPEG image compression quality factor values at this threshold also range widely from 31 to 79, with concentration between 40 and 45. For the BPG encoder, the values of the quantization parameter are grouped around 30, and the boundary values are 25 and 34. Furthermore, it is shown that this boundary can be reliably determined based on simple features derived from the original uncompressed image. Gradient-based features known as spatial frequency and spatial information proved to be the best predictors. The degree of agreement between the predictions obtained from these features with the ground truth values of PSNR and bpp in both types of compression is greater than 85%. A comparative analysis has showed that, using BPG compression, it is possible, on the average, to achieve a twice larger compression ratio of visually lossless compression than for JPEG (80 versus 40). Conclusion: Although a high degree of agreement is achieved between the predictions and the ground truth values of PSNR and bpp of the boundary between visually lossless and visually lossy compression, there is a need for the development of new prediction approaches, especially with the BPG technique, which through the compression ratio proved to be superior to the JPEG technique. The existing databases used for the analysis of visually lossless compression contain color images from the visible part of the electromagnetic spectrum. Considering the increasing use of images from the infrared part of the spectrum, there is a need to conduct similar tests in this spectral range.

Utilization of patient portals: a cross-sectional study investigating associations with mobile app quality

BackgroundMobile apps facilitate patients’ access to portals and interaction with their healthcare providers. The COVID-19 pandemic accelerated this trend globally, but little evidence exists on patient portal usage in the Middle East, where internet access and digital literacy are limited. Our study aimed to explore how users utilize a patient portal through its related mobile app (MyChart by EPIC).MethodsWe conducted a cross-sectional survey of MyChart users, recruited from a tertiary care center in Lebanon. We collected MyChart usage patterns, perceived outcomes, and app quality, based on the Mobile Application Rating Scale (user version, uMARS), and sociodemographic factors. We examined associations between app usage, app quality, and sociodemographic factors using Pearson’s correlations, Chi-square, ANOVA, and t-tests.Results428 users completed the survey; they were primarily female (63%), aged 41.3 ± 15.6 years, with a higher education level (87%) and a relatively high crowding index of 1.4 ± 0.6. Most of the sample was in good and very good health (78%) and had no chronic illnesses (67%), and accessed the portal through MyChart once a month or less (76%). The most frequently used features were accessing health records (98%), scheduling appointments (67%), and messaging physicians (56%). According to uMARS completers (n = 200), the objective quality score was 3.8 ± 0.5, and the subjective quality was 3.6 ± 0.7. No significant association was found between overall app usage and the mobile app quality measured via uMARS. Moreover, app use frequency was negatively associated with education, socioeconomic status, and perceived health status. On the other hand, app use was positively related to having chronic conditions, the number of physician visits and subjective app quality.ConclusionThe patient portal usage was not associated with app quality but with some of the participants’ demographic factors. The app offers a user-friendly, good-quality interface to patient health records and physicians, appreciated chiefly by users with relatively low socioeconomic status and education. While this is encouraging, more research is needed to capture the usage patterns and perceptions of male patients and those with even lower education and socioeconomic status, to make patient portals more inclusive.

A no-reference panoramic image quality assessment with hierarchical perception and color features

The ultimate goal of no-reference omnidirectional image quality assessment (NR-OIQA) is to design a comprehensive perception method that can accurately assess the quality of damaged omnidirectional images without prior knowledge. However, most existing studies cannot attain credible accuracy because of lacking a non-neuroscience-based or non-biology-based model. Inspired by this, an original visual perception-based and neuroscience-based OIQA model by considering the hierarchical perception features of the HVS, which includes specific information, local saliency information, global information, and color information which is often ignored by researchers is proposed in this work. According to the hierarchical process in neuroscience, the high-frequency co-occurrence matrix (HFCM)-based and variance-based specific features are applied to perceive details that are first distorted in the frequency domain. The entropy-based combination of the paranormal saliency map(PSM) and superpixel segmentation with the simple linear iterative clustering (SLIC) algorithm is employed to emphasize the rich quality-aware local saliency information. The global panoramic statistical (GPS) model is utilized to express global semantics distortion as the high-level feature. Visual-aware color texture descriptor with the cross-channel local binary pattern(CCLBP) which effectively reflects the correlation and dependency of pixels between different color channels is employed to map color information. Finally, all above features have been extracted and combined with subjective scores to assess the objective quality scores by support vector regression (SVR). Experiments express that our method has more accuracy and stronger stability on CVIQD2018 and OIQA databases.

Subjective and objective quality assessment of gastrointestinal endoscopy images: From manual operation to artificial intelligence.

Gastrointestinal endoscopy has been identified as an important tool for cancer diagnosis and therapy, particularly for treating patients with early gastric cancer (EGC). It is well known that the quality of gastroscope images is a prerequisite for achieving a high detection rate of gastrointestinal lesions. Owing to manual operation of gastroscope detection, in practice, it possibly introduces motion blur and produces low-quality gastroscope images during the imaging process. Hence, the quality assessment of gastroscope images is the key process in the detection of gastrointestinal endoscopy. In this study, we first present a novel gastroscope image motion blur (GIMB) database that includes 1,050 images generated by imposing 15 distortion levels of motion blur on 70 lossless images and the associated subjective scores produced with the manual operation of 15 viewers. Then, we design a new artificial intelligence (AI)-based gastroscope image quality evaluator (GIQE) that leverages the newly proposed semi-full combination subspace to learn multiple kinds of human visual system (HVS) inspired features for providing objective quality scores. The results of experiments conducted on the GIMB database confirm that the proposed GIQE showed more effective performance compared with its state-of-the-art peers.

Reduced-Reference Quality Assessment of Point Clouds via Content-Oriented Saliency Projection

Many dense 3D point clouds have been exploited to represent visual objects instead of traditional images or videos. To evaluate the perceptual quality of various point clouds, in this letter, we propose a novel and efficient Reduced-Reference quality metric for point clouds, which is based on Content-oriented sAliency Projection (RR-CAP). Specifically, we make the first attempt to simplify reference and distorted point clouds into projected saliency maps with a downsampling operation. Through this process, we tackle the issue of transmitting large-volume original point clouds to user-ends for quality assessment. Then, motivated by the characteristics of the human visual system (HVS), the objective quality scores of distorted point clouds are produced by combining content-oriented similarity and statistical correlation measurements. Finally, extensive experiments are conducted on SJTU-PCQA and WPC databases. The experimental results demonstrate that our proposed algorithm outperforms existing reduced-reference and no-reference quality metrics, and significantly reduces the performance gap between state-of-the-art full-reference quality assessment methods. In addition, we show the performance variation of each proposed technical component by ablation tests.

The 50 most cited studies on posterior tibial slope in joint preserving knee surgery

PurposeTo determine the 50 most cited studies on posterior tibial slope (PTS) in joint preserving knee surgery and assess their level of evidence, objective study quality scores as well as to examine whether the study quality correlated with the citation count and citation density in the top 50 list.MethodsA literature search on Web of Science was performed to determine the 50 most cited studies on the topic of PTS in joint preserving knee surgery between 1990 and 2022. The studies were evaluated for their bibliographic parameters, level of evidence rating (LOE), citation counts, the Modified Coleman Methodological Score (MCMS), the Methodological Index for Non-Randomized Studies (MINORS), and the Radiologic Methodology and Quality Scale (MQCSRE).ResultsOf the top 50 list, 16 studies were published in the American Journal of Sports Medicine. A total of 23 studies were produced in the United States (46%). Of 10 different study types, case control studies (n = 16, 32%) and cadaveric studies (n = 10, 20%) were most common. 15 studies (30%) were purely radiological studies. 6 studies were level II (12%), 23 level III (46%), 15 level IV (30%), and 6 level V studies (12%), respectively. The number of citations ranged from 42 to 447 (mean 105.6 ± 79.2 citations) and showed a mean citation density of 10.3 ± 5.2, composed of the decades 1994 – 2000 (8.3 ± 4.1), 2001 – 2010 (11.1 ± 5.9), 2011 – 2019 (10.1 ± 5.1). Mean quality scores were 55.9 ± 13.0 for MCMS (n = 18), 14.5 ± 3.2 for MINORS (n = 18) and 18.1 ± 3.7 for MQCSRE (n = 20), respectively. High citation counts did not correlate with higher study quality scores (p > 0.05). Radiological studies were not significantly cited more often than non-radiological studies (mean 116.9 ± 88.3 vs. 100.8 ± 75.8 citations; p > 0.05).ConclusionIn joint preserving knee surgery, the 50 most cited studies on PTS did not represent a ranking of the highest methodological quality scores. Citation counts and citation density over the past three decades did not significantly differ, even though the number of articles in the presented list multiplied over the same period. This list can serve as a reference tool for orthopedic surgeons aiming to review PTS literature.

Usability of the TCI Mobile app as a complement to the Constraint-Induced Therapy protocol

<h3>Research Objectives</h3> To investigate the usability of the TCI Mobile app by therapists and individuals with stroke. <h3>Design</h3> Cross-sectional study. <h3>Setting</h3> Functionality and Technological Innovation in Neurorehabilitation Group lab, Brazil. <h3>Participants</h3> This preliminary study included patients with stroke and physical therapists (PTs). Inclusion criteria for patients were ≥6 months of chronicity, access to a mobile device and the internet, and a score < 24 on the mini mental state examination and excluded if they had a neurological, orthopedic, and/or respiratory disorders unrelated to stroke or severe visual impairment. PTs who had experience with constraint-induced therapy (CIT), a mobile device, and internet access were included. <h3>Interventions</h3> TCI Mobile is an application used during CIT application. It encourages engagement with homework through push notifications, enables self-monitoring of daily activities, provides performance feedback, and compares assessment results. Patients and PTs used the application for 3 consecutive days during application of the CIT protocol, and then completed an interview. <h3>Main Outcome Measures</h3> The User Mobile Application Rating Scale (uMARS) includes three subscales: objective and subjective quality and perceived impact, with scores ranging from 1 (inadequate) to 5 (excellent). It was previously validated to assess mobile health applications and had excellent internal consistency (alpha = 0.90) and good test-retest reliability. <h3>Results</h3> Five participants (two women) with stroke were included. They had an average age of 48 years and approximately 67% had dominant hand hemiparesis. Their average chronicity time was 130 months (range 56-264 months). The two PT participants were women (average age, 41.5 years). uMARS data were analyzed using means and standard deviation. Patients' average objective and subjective quality scores and perceived impact were 3.92±0.73, 4.58±0.72, and 4.78±0.38, respectively. PTs' average objective and subjective quality scores and perceived impact were 4.20±0.11, 4.75±0.00, 3.92±1.06, respectively. <h3>Conclusions</h3> Therapists and people with stroke reported good objective and subjective quality and good perceived impact of the TCI mobile application, which showed good usability as an addition to the CIT protocol in both clinical and research settings. <h3>Author(s) Disclosures</h3> The authors have no conflict of interest to declare.

The development and usability of a web-based mobile application as a dairy intake screener for South African adults.

Paper-based dietary assessment tools such as food frequency questionnaires (FFQ) and especially dietary screeners are making way for versions that use technology. Amidst low intakes of dairy and dairy-related nutrients in South Africa, and to increase public awareness thereof, we aimed to develop and evaluate the usability of an application (app) to screen for dairy intake in higher income South African adults. In a consultative process, a dairy intake screener ('Dairy Diary') was developed as an eight-item quantitative FFQ with four types of commonly consumed local dairy products: milk, maas (fermented milk), yoghurt, and cheese. For each dairy product, usual frequency of consumption and portion size per eating occasion were scored resulting in three risk classes: <1 serving daily; 1≤2 servings daily; ≥2 servings daily. Digitalisation included product- and portion-specific graphics with linkage to risk class-relevant preliminary dairy-related guidance as part of a web-based mobile app. For the evaluation of the usability, the 26-item end-user version of the Mobile Application Rating Scale (uMARS) was used in an online cross-sectional survey (Qualtrics; April 2020). Items were scored on a 5-point Likert-type scale, resulting in three final app scores. From a conveniently recruited sample of 1102, 703 (64%; 81% female; mean age 29.8 ± 11.0 years) were retained for analysis. uMARS-informed descriptive statistics summarise the findings. The uMARS app mean objective quality score (3.9 ± 0.85), app subjective quality score (3.5 ± 0.77), app-specific score (3.6 ± 0.94), and additional question on e-portion (4.3 ± 0.78) met the minimum acceptability score of ≥3.0. For the subscales, the mean score for aesthetics was the highest (4.4 ± 0.82), followed by information (4.3 ± 0.90) and functionality (4.0 ± 1.33). Engagement scored lowest (3.0 ± 1.55). The 'Dairy Diary' is a user-friendly screener for dairy intake.

O030 Image-analysis algorithm to determine quality of cold perfusion in kidney transplantation

Abstract Introduction Surgeon assessment of visual ‘quality of perfusion’ (QOP) influences kidney discard and predicts transplant outcome. However, this assessment is subjective and bias-prone. We aimed to design an application utilising a smartphone camera to make this assessment objective and enhance decision making. Methods The QOP in photographs of backbench kidneys was graded from 1 (ideal) to 5 (very poor) by three independent surgeons. A training cohort was used to develop an image-analysis algorithm, which was validated in a separate cohort. Results Analysing surgeon scores of 174 kidney images revealed that inter-rater agreement was good for kidneys displaying the best (rated 1) and worst (rated 4 or 5) QOP. However, for intermediate scores inter-rater agreement was poor. Inter-rater agreement between surgeons decreased as they graded more images; as surgeons fatigued, their ability to classify images worsened. A training cohort (n=174 kidneys) was used for algorithm development. First, small regions within each image were mapped within the CEILAB colour-space, where well-perfused and poorly perfused areas show clear separation. To generate a score for each kidney these regions are compared with ideally flushed kidney tissue. Testing our algorithm (validation cohort - n=29 kidneys) revealed strong correlation between image-analysis QOP score and surgeon assessment, r=0.789 (0.587–0.899), P&amp;lt;0.001. Conclusion Surgeon inter-rater agreement on kidney QOP is low for kidneys with borderline QOP and worsens with fatigue. We provide a QOP score utilising an image-analysis algorithm, which correlates with surgeon scoring. With additional images and training this could provide an objective, numerical, point-of-care assessment of organ quality. Take-home message Current visual assessment of transplant organ quality is subjective and bias-prone. This body of work attempts to create a point-of-care image-analysis application to provide an objective numeric organ quality score.

Unsupervised blind image quality assessment based on joint structure and natural scene statistics features

Compared with the widely used supervised blind image quality assessment (BIQA) models, unsupervised BIQA models require little prior knowledge for calculating the objective quality scores of distorted images. In this paper, we propose an unsupervised BIQA method that aims to achieve both good performance and generalization capability with low computational complexity. Carefully selected and extensive structure and natural scene statistics (NSS) features can better represent image quality. First, we employ phase congruency (PC) and finely selected gradient magnitude map and Laplacian of Gaussian response (GM-LOG) features to represent image structure information. Second, we calculate the local mean-subtracted and contrast-normalized (MSCN) coefficients and the Karhunen–Loéve transform (KLT) coefficients to represent the naturalness of the distorted images. Last, multivariate Gaussian (MVG) model with joint features extracted from both the pristine images and the distorted images is adopted to calculate the objective image quality. Extensive experiments conducted on nine IQA databases demonstrate that the proposed method achieves better performance than the state-of-the-art BIQA methods.

Empowerment through technology: A systematic evaluation of the content and quality of mobile applications to empower individuals with cancer

ObjectiveGreater emphasis on patient empowerment has led to a plethora of mobile health applications aimed at empowering patients with cancer. However, the rigor and evidence of these apps are rarely acknowledged. This systematic review of patient empowerment apps describes the characteristics, quality, heuristics, and evidence supporting these apps. Materials and MethodsWe identified commercially available apps through the Apple and Google Play stores using patient- and research-derived conceptualizations of patient empowerment. Three authors used the Mobile App Rating Scale, heuristics, readability, user ratings, and evidence to evaluate the apps’ foci, features, and quality. App characteristics were summarized with descriptive analyses. ResultsTwelve apps met the eligibility criteria and were analyzed. Apps’ content focused on enhancing communication skills (n = 10, 83.3%), social support (n = 8, 66.7%), information about cancer and treatment (n = 8, 66.7%), and peer-to-peer support (n = 5, 41.7%). The mean objective (3.9 ± 0.5 out of 5) and subjective (3.7 ± 1.0 out of 5) quality scores were moderate to high. Most heuristics were not violated, and the mean reading level was 10th grade, which is above the recommended 8th grade level. Four apps had been evaluated in published research articles. DiscussionThe contents of patient empowerment apps varied greatly, and the readability was exclusionary to the average reader. Apps. ConclusionPatient empowerment apps should be more rigorously designed and tested to ensure the apps are usable and beneficial to diverse groups of cancer survivors.

A Nested U-Net With Self-Attention and Dense Connectivity for Monaural Speech Enhancement

With the development of deep neural networks, speech enhancement technology has been vastly improved. However, commonly used speech enhancement approaches cannot fully leverage contextual information from different scales, which limits performance improvement. To address this problem, we propose a nested U-Net with self-attention and dense connectivity (SADNUNet) for monaural speech enhancement in the time domain. SADNUNet is an encoder-decoder structure with skip connections. In SADNUNet, the multi-scale aggregation block is proposed to explore more contextual information from different scales. By this means, the advantage of global and local speech features can be fully utilized to improve speech reconstruction ability. Furthermore, dense connectivity and self-attention are incorporated in the network for better feature extraction and utterance level context aggregation. The experimental results demonstrate that the proposed approach achieves on-par or better performance than other models in objective speech intelligibility and quality scores.

Two-level optimization approach with accelerated proximal gradient for objective measures in sparse speech reconstruction

&lt;p style='text-indent:20px;'&gt;Compressive speech enhancement makes use of the sparseness of speech and the non-sparseness of noise in time-frequency representation to perform speech enhancement. However, reconstructing the sparsest output may not necessarily translate to a good enhanced speech signal as speech distortion may be at risk. This paper proposes a two level optimization approach to incorporate objective quality measures in compressive speech enhancement. The proposed method combines the accelerated proximal gradient approach and a global one dimensional optimization method to solve the sparse reconstruction. By incorporating objective quality measures in the optimization process, the reconstructed output is not only sparse but also maintains the highest objective quality score possible. In other words, the sparse speech reconstruction process is now quality sparse speech reconstruction. Experimental results in a compressive speech enhancement consistently show score improvement in objectives measures in different noisy environments compared to the non-optimized method. Additionally, the proposed optimization yields a higher convergence rate with a lower computational complexity compared to the existing methods.&lt;/p&gt;

Perceptually Unimportant Information Reduction and Cosine Similarity-Based Quality Assessment of 3D-Synthesized Images.

Quality assessment of 3D-synthesized images has traditionally been based on detecting specific categories of distortions such as stretching, black-holes, blurring, etc. However, such approaches have limitations in accurately detecting distortions entirely in 3D synthesized images affecting their performance. This work proposes an algorithm to efficiently detect the distortions and subsequently evaluate the perceptual quality of 3D synthesized images. The process of generation of 3D synthesized images produces a few pixel shift between reference and 3D synthesized image, and hence they are not properly aligned with each other. To address this, we propose using morphological operation (opening) in the residual image to reduce perceptually unimportant information between the reference and the distorted 3D synthesized image. The residual image suppresses the perceptually unimportant information and highlights the geometric distortions which significantly affect the overall quality of 3D synthesized images. We utilized the information present in the residual image to quantify the perceptual quality measure and named this algorithm as Perceptually Unimportant Information Reduction (PU-IR) algorithm. At the same time, the residual image cannot capture the minor structural and geometric distortions due to the usage of erosion operation. To address this, we extract the perceptually important deep features from the pre-trained VGG-16 architectures on the Laplacian pyramid. The distortions in 3D synthesized images are present in patches, and the human visual system perceives even the small levels of these distortions. With this view, to compare these deep features between reference and distorted image, we propose using cosine similarity and named this algorithm as Deep Features extraction and comparison using Cosine Similarity (DF-CS) algorithm. The cosine similarity is based upon their similarity rather than computing the magnitude of the difference of deep features. Finally, the pooling is done to obtain the objective quality scores using simple multiplication to both PU-IR and DF-CS algorithms. Our source code is available online: https://github.com/sadbhawnathakur/3D-Image-Quality-Assessment.

CNN-based no-reference video quality assessment method using a spatiotemporal saliency patch selection procedure

We propose a yet lightweight no-reference (NR) video quality assessment (VQA) method, which uses a convolution neural network (CNN) architecture. The proposed method implements a spatiotemporal saliency patch selection procedure that crops the frame into small nonoverlapping blocks of images (patches) and selects the most perceptually relevant ones. The selected patches are then forwarded to the CNN. To determine which patches are the most relevant, spatial and temporal saliency features are computed for each frame. The proposed method does not require subjective scores to train the CNN. It uses objective quality scores as target quality scores for each video frame, which are computed using an NR image quality assessment method. Given the lack of large annotated video quality databases, this is an advantage of the proposed method. Finally, although it has much smaller cost of data-processing, compared with other state-of-the-art methods, the proposed NR-VQA obtains robust and competitive results.