Search

Ischemic heart disease (IHD) has been linked with poor brain outcomes. The brain magnetic resonance imaging–derived difference between predicted brain age and actual chronological age (brain-age delta in years, positive for accelerated brain aging) may serve as an effective means of communicating brain health to patients to promote healthier lifestyles. The authors investigated the impact of prevalent IHD on brain aging, potential underlying mechanisms, and its relationship with dementia risk, vascular risk factors, cardiovascular structure, and function. Brain age was estimated in subjects with prevalent IHD (n = 1,341) using a Bayesian ridge regression model with 25 structural (volumetric) brain magnetic resonance imaging features and built using UK Biobank participants with no prevalent IHD (n = 35,237). Prevalent IHD was linked to significantly accelerated brain aging (P < 0.001) that was not fully mediated by microvascular injury. Brain aging (positive brain-age delta) was associated with increased risk of dementia (OR: 1.13 [95% CI: 1.04-1.22]; P = 0.002), vascular risk factors (such as diabetes), and high adiposity. In the absence of IHD, brain aging was also associated with cardiovascular structural and functional changes typically observed in aging hearts. However, such alterations were not linked with risk of dementia. Prevalent IHD and coexisting vascular risk factors are associated with accelerated brain aging and risk of dementia. Positive brain-age delta representing accelerated brain aging may serve as an effective communication tool to show the impact of modifiable risk factors and disease supporting preventative strategies. [Display omitted] [ABSTRACT FROM AUTHOR]

• Image pre-processing tools are critical to develop and assess AI solutions. • Open access tools and data are widely available for medical image preparation. • AI needs Big Data to develop and fine-tune a model properly. • Big Data needs AI to fully interpret the decision making process. The vast amount of data produced by today's medical imaging systems has led medical professionals to turn to novel technologies in order to efficiently handle their data and exploit the rich information present in them. In this context, artificial intelligence (AI) is emerging as one of the most prominent solutions, promising to revolutionise every day clinical practice and medical research. The pillar supporting the development of reliable and robust AI algorithms is the appropriate preparation of the medical images to be used by the AI-driven solutions. Here, we provide a comprehensive guide for the necessary steps to prepare medical images prior to developing or applying AI algorithms. The main steps involved in a typical medical image preparation pipeline include: (i) image acquisition at clinical sites, (ii) image de-identification to remove personal information and protect patient privacy, (iii) data curation to control for image and associated information quality, (iv) image storage, and (v) image annotation. There exists a plethora of open access tools to perform each of the aforementioned tasks and are hereby reviewed. Furthermore, we detail medical image repositories covering different organs and diseases. Such repositories are constantly increasing and enriched with the advent of big data. Lastly, we offer directions for future work in this rapidly evolving field. [ABSTRACT FROM AUTHOR]

Study Objective: To determine if the number of myomas removed during myomectomy for symptomatic relief affects long-term fertility outcomes in reproductive-aged women.Design: Retrospective cohort survey study (Canadian Task Force classification II-2).Setting: University hospital.Patients: One hundred forty-four patients who underwent myomectomy for symptomatic myomas and attempted to conceive afterward.Intervention: Questionnaire mailed to reproductive-aged women who received robotic, laparoscopic, or abdominal myomectomy.Measurements and Main Results: Patients with >6 myomas removed were less likely to achieve pregnancy after myomectomy than patients with ≤6 myomas removed (22.9% vs 70.8%, respectively; p < .001). To achieve pregnancy, 45% of those with >6 myomas removed (vs 17.6% of those with ≤6 myomas removed) relied on fertility treatment (clomiphene citrate, letrozole, intrauterine insemination, or in vitro fertilization). Of those with >6 myomas removed who became pregnant, 45.5% had a term birth, 45.5% miscarried, and 9.1% had an ectopic pregnancy. Of those with ≤6 myomas removed who became pregnant, 61.8% had a term birth, 23.5% had a preterm birth, and 13.2% miscarried.Conclusion: The number of myomas removed during myomectomy significantly affects fertility. Women with >6 myomas removed were less likely to become pregnant, more likely to require fertility treatment, and less likely to have a term birth when compared with women with ≤6 myomas removed. [ABSTRACT FROM AUTHOR]

Automatic tools for the analysis of human behaviour are very important when aiming to understand the lifestyle of people. Egocentric wearable cameras allow the capture of images during long periods of time and in this way bring objective evidence of the experiences of the user. In this paper, we propose a novel framework to discover behavioural patterns following an unsupervised greedy approach based on extracted image descriptors. The method collects and constructs time-frames to extract the semantics of user behaviour in terms of contextual information, such as places, activity, present objects, and others. Later, the similarity among the user time-frames is computed to assess correlations and thus obtain the user's routine descriptors. To evaluate the performance of our method, we present several score metrics and compare them to state-of-the-art works in the field. We validated our method on 315 days and more than 390,000 images extracted from 14 users. Results show that behavioural patterns can be successfully discovered and that they are able to characterize the routine of people bringing important information about their lifestyle and behaviour change. [ABSTRACT FROM AUTHOR]

Background We previously identified the positive result of the supine apprehension test after completion of rehabilitation following a first dislocation as a possible predictor of high risk for redislocation. We extend the follow-up of a previous cohort of patients with first-time shoulder dislocations to better assess this test. Methods Fifty-three men aged 17 to 27 years who sustained a first traumatic shoulder dislocation were treated by shoulder immobilization for 4 weeks and then rehabilitated with a standard physical therapy protocol. At 6-week follow-up, a supine anterior apprehension test was performed to assess the risk of redislocation. The patients were observed prospectively for a minimum of 75 months. Results Of the 53 participants, 52 (mean age, 20.2 years) completed the study follow-up. Of the 52 subjects, 41 (79%) were combat soldiers. Follow-up was between 75 and 112 months. Of the 52 subjects, 31 (60%) redislocated at a range of 3 to 70 months after the initial dislocation. Eleven of 14 subjects (79%; confidence interval, 52%-92%) with a positive anterior apprehension test result redislocated, compared with 20 of 38 patients (53%; confidence interval, 37%-68%) with a negative test result. Patients with a positive test result redislocated more and earlier ( P = .02, PROC LIFETEST, SAS). Conclusions The results of the supine apprehension test after a first shoulder dislocation and rehabilitation can help predict risk for recurrent instability. It potentially may be included as a variable in decision analysis models. [ABSTRACT FROM AUTHOR]

Abstract: Wireless sensor networks (WSNs) deployed in hostile environments suffer from a high rate of node failure. We investigate the effect of such failure rate on network connectivity. We provide a formal analysis that establishes the relationship between node density, network size, failure probability, and network connectivity. We show that large networks can maintain connectivity despite a significantly high probability of node failure. We derive mathematical functions that provide lower bounds on network connectivity in WSNs. We compute these functions for some realistic values of node reliability, area covered by the network, and node density, to show that, for instance, networks with over a million nodes can maintain connectivity with a probability exceeding 95% despite node failure probability exceeding 53%. [Copyright &y& Elsevier]

Abstract: This paper proposes dividing of Black Sea Catchment Area''s river basins into four morpho hydrographic regions – Dobrudja, East Balkan mountain, lowland of Burgas and Strandzha Mountain. It is based on geomorphologic and hydrographic characteristics of rivers and river basins (altitude, valleys, slopes, drainage density, river length, catchment shape, stream orders, and curve of the rivers). The work uses GIS to analyze and data of the rivers and the river basins collected from basic topographic maps. Digital Elevation Model (DEM), created on the base of satellite images, was also used. Based on the data collected for this study some basic hydrographical parameters were calculated. This is the first experiment of morpho-hydrographic dividing of the main catchment area in Bulgaria using river basins like basic units. It shows a way to unify separate river basins into major areas that can be useful for analyzing and research of water resources. [Copyright &y& Elsevier]

Background & Aims: Evaluation of small bowel motility by intestinal manometry is invasive and requires expertise for interpretation. Our aim was to use capsule technology for evaluation of small bowel motor function based on a fully computerized image analysis program. Methods: Thirty-six consecutive patients with severe intestinal motor disorders (19 fulfilling manometric criteria of intestinal dysmotility and 17 not) and 50 healthy subjects received the endoscopic capsule (Pillcam; Given Imaging, Yokneam, Israel). Endoluminal image analysis was performed with a computer vision program specifically developed for the detection of contractile patterns (phasic luminal closure and radial wrinkles by wall texture analysis), noncontractile patterns (tunnel and wall appearance by Laplacian filtering), intestinal content (by color decomposition analysis), and endoluminal motion (by chromatic stability). Automatic classification of normal and abnormal intestinal motility was performed by means of a machine-learning technique. Results: As compared with healthy subjects, patients exhibited less contractile activity (25% less phasic luminal closures, P < .05) and more noncontractile patterns (151% more tunnel pattern, P < .05), static sequences (56% more static images, P < .01), and turbid intestinal content (94% more static turbid images, P < .01). On cross validation, the classifier identified as abnormal all but 1 patient with manometric criteria of dysmotility and as normal all healthy subjects. Out of the 17 patients without manometric criteria of dysmotility, 11 were identified as abnormal and 6 as normal. Conclusions: Our study shows that endoluminal image analysis, by means of computer vision and machine-learning techniques, constitutes a reliable, noninvasive, and automated diagnostic test of intestinal motor disorders. [Copyright &y& Elsevier]

Background: In a multinational clinical trial, valsartan was statistically not inferior to captopril in reducing mortality and cardiovascular morbidity after myocardial infarction (MI) in patients with signs of heart failure and/or left ventricular dysfunction. We conducted a prospective economic evaluation to compare within-trial resource use, costs, and quality of life in patients receiving valsartan, captopril, or both after MI. Methods: We assigned country-specific unit costs to resource use data for 14703 patients and measured health-related quality of life in a subset of 4524 patients. We used the nonparametric bootstrap method to compare rates of resource use and costs, and a piecewise linear mixed-effects regression analysis to compare longitudinal measures of quality of life. Results: There were no significant differences in rates of resource use between the valsartan and captopril groups. During an average follow-up of 2 years, total costs for patients receiving valsartan were significantly higher than for patients receiving captopril (US$14103 vs US$13038; 95% CI US$369-US$1875). The cost differential was caused primarily by the cost of the study medications (US$1056 for valsartan vs US$165 for captopril; 95% CI US$867 to US$912). Quality of life did not differ significantly between groups. Conclusions: For most patients at high risk after MI, the availability of generic captopril confers a cost advantage over valsartan because of lower medication costs. The difference will be smaller or nonexistent in settings where brand-name ACE inhibitors are prescribed. [Copyright &y& Elsevier]

• The paper presents an algorithm to fuse multimodality neurological images. • The proposed fusion model is based on an optimized biologically inspired feedforward neural model in two-scale hybrid ℓ 1 − ℓ 0 decomposition domain. • The hyper-parameters of decomposition method and BISFNN model are estimated using GWO technique. • The proposed method provides better-fused images with more detail information compared to the state-of-the-art methods. Medical image fusion plays an important role in the clinical diagnosis of several critical neurological diseases by merging complementary information available in multimodal images. In this paper, a novel CT-MR neurological image fusion framework is proposed using an optimized biologically inspired feedforward neural model in two-scale hybrid ℓ 1 − ℓ 0 decomposition domain using gray wolf optimization to preserve the structural as well as texture information present in source CT and MR images. Initially, the source images are subjected to two-scale ℓ 1 − ℓ 0 decomposition with optimized parameters, giving a scale-1 detail layer, a scale-2 detail layer and a scale-2 base layer. Two detail layers at scale-1 and 2 are fused using an optimized biologically inspired neural model and weighted average scheme based on local energy and modified spatial frequency to maximize the preservation of edges and local textures, respectively, while the scale-2 base layer gets fused using choose max rule to preserve the background information. To optimize the hyper-parameters of hybrid ℓ 1 − ℓ 0 decomposition and biologically inspired neural model, a fitness function is evaluated based on spatial frequency and edge index of the resultant fused image obtained by adding all the fused components. The fusion performance is analyzed by conducting extensive experiments on different CT-MR neurological images. Experimental results indicate that the proposed method provides better-fused images and outperforms the other state-of-the-art fusion methods in both visual and quantitative assessments. [ABSTRACT FROM AUTHOR]

Objective Access-related hand ischemia (ARHI) is a major complication after hemodialysis access construction. This study was designed to prospectively describe its incidence, predictors, interventions, and associated access maturation. Methods The Hemodialysis Fistula Maturation Study is a multicenter prospective cohort study designed to identify predictors of autogenous arteriovenous access (arteriovenous fistula [AVF]) maturation. Symptoms and interventions for ARHI were documented, and participants who received interventions for ARHI were compared with other participants using a nested case-control design. Associations of ARHI with clinical, ultrasound, vascular function, and vein histologic variables were each individually evaluated using conditional logistic regression; the association with maturation was assessed by relative risk, Pearson χ 2 test, and multiple logistic regression. Results The study cohort included 602 participants with median follow-up of 2.1 years (10th-90th percentiles, 0.7-3.5 years). Mean age was 55.1 ± 13.4 (standard deviation) years; the majority were male (70%), white (47%), diabetic (59%), smokers (55%), and on dialysis (64%) and underwent an upper arm AVF (76%). Symptoms of ARHI occurred in 45 (7%) participants, and intervention was required in 26 (4%). Interventions included distal revascularization with interval ligation (13), ligation (7), banding (4), revision using distal inflow (1), and proximalization of arterial inflow (1). Interventions were performed ≤7 days after AVF creation in 4 participants (15%), between 8 and 30 days in 6 (23%), and >30 days in 16 (63%). Female gender (odds ratio, 3.17; 95% confidence interval, 1.27-7.91; P = .013), diabetes (13.62 [1.81-102.4]; P = .011), coronary artery disease (2.60 [1.03-6.58]; P = .044), higher preoperative venous capacitance (per %/10 mm Hg, 2.76 [1.07-6.52]; P = .021), and maximum venous outflow slope (per [mL/100 mL/min]/10 mm Hg, 1.13 [1.03-1.25]; P = .011) were significantly associated with interventions; a lower carotid-femoral pulse wave velocity and the outflow vein diameter in the early postoperative period (days 0-3) approached significance ( P < .10). Intervention for ARHI was not associated with AVF maturation failure (unadjusted risk ratio, 1.18 [0.69-2.04], P = .56; adjusted odds ratio, 0.97 [0.41-2.31], P = .95). Conclusions Remedial intervention for ARHI after AVF construction is uncommon. Diabetes, female gender, capacitant outflow veins, and coronary artery disease are all associated with an increased risk of intervention. These higher risk patients should be counseled preoperatively, their operative plans should be designed to reduce the risk of hand ischemia, and they should be observed closely. [ABSTRACT FROM AUTHOR]