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Background: 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., Objectives: 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., Methods: 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)., Results: 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., Conclusions: 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., Competing Interests: Funding Support and Author Disclosures Barts Charity (G-002346) contributed to fees required to access UK Biobank data (access application #2964). Drs Rauseo and Petersen have received grants from the London Medical Imaging and Artificial Intelligence Centre for Value Based Healthcare (AI4VBH), which is funded from the Data to Early Diagnosis and Precision Medicine strand of the government’s Industrial Strategy Challenge Fund, managed and delivered by Innovate UK on behalf of UK Research and Innovation (UKRI). Mr Salih is supported by INVITE program co-financed by the European Union within the Horizon 2020 Programme and by the Regione del Veneto. Dr Raisi-Estebragh has received grants from the National Institute for Health Research (NIHR) Integrated Academic Training programme which supports her Academic Clinical Lectureship post; she has also received grants from the British Heart Foundation Clinical Research Training Fellowship (No. FS/17/81/33318). Dr Aung has received grants from the NIHR Integrated Academic Training programme which supports his Academic Clinical Lectureship post, and the Academy of Medical Sciences Starter Grant for Clinical Lecturers (SGL024∖1024). Dr Slabaugh has received consulting fees from MindRank AI. Dr Marshall has received grants from Barts Charity; and has received personal fees from Biogen and GE Healthcare. Dr Neubauer has received grants from the Oxford NIHR Biomedical Research Centre. Drs Galazzo and Menegaz have received grants from the Fondazione CariVerona (EDIPO project—reference number 2018.0855.2019). Dr Petersen has received grants from the NIHR Biomedical Research Centre at Barts, and from the British Heart Foundation for funding the manual analysis to create a cardiovascular magnetic resonance imaging reference standard for the UK Biobank imaging resource in 5,000 CMR scans (www.bhf.org.uk; PG/14/89/31194), and from the SmartHeart EPSRC programme grant (www.nihr.ac.uk; EP/P001009/1); and has received consulting fees from Circle Cardiovascular Imaging Inc, Calgary, Alberta, Canada. All other authors have reported that they have no relationships relevant to the contents of this paper to disclose., (Copyright © 2023 The Authors. Published by Elsevier Inc. All rights reserved.)

Deep learning is a machine learning technique that has revolutionized the research community due to its impressive results on various real-life problems. Recently, ensembles of Convolutional Neural Networks (CNN) have proven to achieve high robustness and accuracy in numerous computer vision challenges. As expected, the more models we add to the ensemble, the better performance we can obtain, but, in contrast, more computer resources are needed. Hence, the importance of deciding how many models to use and which models to select from a pool of trained models is huge. From the latter, a common strategy in deep learning is to select the models randomly or according to the results on the validation set. However, in this way models are chosen based on individual performance ignoring how they are expected to work together. Alternatively, to ensure a better complement between models, an exhaustive search can be used by evaluating the performance of several ensemble models based on different numbers and combinations of trained models. Nevertheless, this may result in being high computationally expensive. Considering that epistemic uncertainty analysis has recently been successfully employed to understand model learning, we aim to analyze whether an uncertainty-aware epistemic method can help us decide which groups of CNN models may work best. The method was validated on several food datasets and with different CNN architectures. In most cases, our proposal outperforms the results by a statistically significant range with respect to the baseline techniques and is much less computationally expensive compared to the brute-force search., (Copyright © 2022 Elsevier Ltd. All rights reserved.)

Background and Objective: The most common tool for population-wide COVID-19 identification is the Reverse Transcription-Polymerase Chain Reaction test that detects the presence of the virus in the throat (or sputum) in swab samples. This test has a sensitivity between 59% and 71%. However, this test does not provide precise information regarding the extension of the pulmonary infection. Moreover, it has been proven that through the reading of a computed tomography (CT) scan, a clinician can provide a more complete perspective of the severity of the disease. Therefore, we propose a comprehensive system for fully-automated COVID-19 detection and lesion segmentation from CT scans, powered by deep learning strategies to support decision-making process for the diagnosis of COVID-19., Methods: In the workflow proposed, the input CT image initially goes through lung delineation, then COVID-19 detection and finally lesion segmentation. The chosen neural network has a U-shaped architecture using a newly introduced Multiple Convolutional Layers structure, that produces a lung segmentation mask within a novel pipeline for direct COVID-19 detection and segmentation. In addition, we propose a customized loss function that guarantees an optimal balance on average between sensitivity and precision., Results: Lungs' segmentation results show a sensitivity near 99% and Dice-score of 97%. No false positives were observed in the detection network after 10 different runs with an average accuracy of 97.1%. The average accuracy for lesion segmentation was approximately 99%. Using UNet as a benchmark, we compared our results with several other techniques proposed in the literature, obtaining the largest improvement over the UNet outcomes., Conclusions: The method proposed in this paper outperformed the state-of-the-art methods for COVID-19 lesion segmentation from CT images, and improved by 38.2% the results for F1-score of UNet. The high accuracy observed in this work opens up a wide range of possible applications of our algorithm in other fields related to medical image segmentation., (Copyright © 2021 Elsevier Ltd. All rights reserved.)

Purpose of Review: Atherosclerosis is the leading cause of cardiovascular disease (CVD) and stroke. Typically, atherosclerotic calcium is found during the mature stage of the atherosclerosis disease. It is therefore often a challenge to identify and quantify the calcium. This is due to the presence of multiple components of plaque buildup in the arterial walls. The American College of Cardiology/American Heart Association guidelines point to the importance of calcium in the coronary and carotid arteries and further recommend its quantification for the prevention of heart disease. It is therefore essential to stratify the CVD risk of the patient into low- and high-risk bins., Recent Finding: Calcium formation in the artery walls is multifocal in nature with sizes at the micrometer level. Thus, its detection requires high-resolution imaging. Clinical experience has shown that even though optical coherence tomography offers better resolution, intravascular ultrasound still remains an important imaging modality for coronary wall imaging. For a computer-based analysis system to be complete, it must be scientifically and clinically validated. This study presents a state-of-the-art review (condensation of 152 publications after examining 200 articles) covering the methods for calcium detection and its quantification for coronary and carotid arteries, the pros and cons of these methods, and the risk stratification strategies. The review also presents different kinds of statistical models and gold standard solutions for the evaluation of software systems useful for calcium detection and quantification. Finally, the review concludes with a possible vision for designing the next-generation system for better clinical outcomes., (Copyright © 2018 Elsevier Ltd. All rights reserved.)

Background: Planning of percutaneous interventional procedures involves a pre-screening and risk stratification of the coronary artery disease. Current screening tools use stand-alone plaque texture-based features and therefore lack the ability to stratify the risk., Method: This IRB approved study presents a novel strategy for coronary artery disease risk stratification using an amalgamation of IVUS plaque texture-based and wall-based measurement features. Due to common genetic plaque makeup, carotid plaque burden was chosen as a gold standard for risk labels during training-phase of machine learning (ML) paradigm. Cross-validation protocol was adopted to compute the accuracy of the ML framework. A set of 59 plaque texture-based features was padded with six wall-based measurement features to show the improvement in stratification accuracy. The ML system was executed using principle component analysis-based framework for dimensionality reduction and uses support vector machine classifier for training and testing-phases., Results: The ML system produced a stratification accuracy of 91.28%, demonstrating an improvement of 5.69% when wall-based measurement features were combined with plaque texture-based features. The fused system showed an improvement in mean sensitivity, specificity, positive predictive value, and area under the curve by: 6.39%, 4.59%, 3.31% and 5.48%, respectively when compared to the stand-alone system. While meeting the stability criteria of 5%, the ML system also showed a high average feature retaining power and mean reliability of 89.32% and 98.24%, respectively., Conclusions: The ML system showed an improvement in risk stratification accuracy when the wall-based measurement features were fused with the plaque texture-based features., (Copyright © 2017 Elsevier Ltd. All rights reserved.)

Background: Accurate and fast quantitative assessment of calcium volume is required during the planning of percutaneous coronary interventions procedures. Low resolution in intravascular ultrasound (IVUS) coronary videos poses a threat to calcium detection causing over-estimation in volume measurement. We introduce a correction block that counter-balances the bias introduced during the calcium detection process., Method: Nineteen patients image dataset (around 40,090 frames), IRB approved, were collected using 40MHz IVUS catheter (Atlantis® SR Pro, Boston Scientific®, pullback speed of 0.5mm/sec). A new set of 20 generalized and well-balanced systems each consisting of three stages: (i) calcium detection, (ii) calibration and (iii) measurement, while ensuring accuracy of four soft classifiers (Threshold, FCM, K-means and HMRF) and workflow speed using five multiresolution techniques (bilinear, bicubic, wavelet, Lanczos, Gaussian Pyramid) were designed. Results of the three calcium detection methods were benchmarked against the Threshold-based method., Results: All 20 well-balanced systems with calibration block show superior performance. Using calibration block, FCM versus Threshold-based method shows the highest cross-correlation 0.99 (P<0.0001), Jaccard index 0.984±0.013 (P<0.0001), and Dice similarity 0.992±0.007 (P<0.0001). The corresponding area under the curve for four calcium detection techniques is: 1.0, 1.0, 0.97 and 0.93, respectively. The mean overall performance improvement is 38.54% and when adapting calibration block. The mean workflow speed improvement is 62.14% when adapting multiresolution paradigm. Three clinical tests shows consistency, reliability, and stability of our well-balanced system., Conclusions: A well-balanced system with a combination of Threshold embedded with Lanczos multiresolution was optimal and can be useable in clinical settings., (Copyright © 2017 Elsevier Ltd. All rights reserved.)

The interpretation and analysis of wireless capsule endoscopy (WCE) recordings is a complex task which requires sophisticated computer aided decision (CAD) systems to help physicians with video screening and, finally, with the diagnosis. Most CAD systems used in capsule endoscopy share a common system design, but use very different image and video representations. As a result, each time a new clinical application of WCE appears, a new CAD system has to be designed from the scratch. This makes the design of new CAD systems very time consuming. Therefore, in this paper we introduce a system for small intestine motility characterization, based on Deep Convolutional Neural Networks, which circumvents the laborious step of designing specific features for individual motility events. Experimental results show the superiority of the learned features over alternative classifiers constructed using state-of-the-art handcrafted features. In particular, it reaches a mean classification accuracy of 96% for six intestinal motility events, outperforming the other classifiers by a large margin (a 14% relative performance increase)., (Copyright © 2016 Elsevier Ltd. All rights reserved.)

Wireless Capsule Endoscopy (WCE) provides a new perspective of the small intestine, since it enables, for the first time, visualization of the entire organ. However, the long visual video analysis time, due to the large number of data in a single WCE study, was an important factor impeding the widespread use of the capsule as a tool for intestinal abnormalities detection. Therefore, the introduction of WCE triggered a new field for the application of computational methods, and in particular, of computer vision. In this paper, we follow the computational approach and come up with a new perspective on the small intestine motility problem. Our approach consists of three steps: first, we review a tool for the visualization of the motility information contained in WCE video; second, we propose algorithms for the characterization of two motility building-blocks: contraction detector and lumen size estimation; finally, we introduce an approach to detect segments of stable motility behavior. Our claims are supported by an evaluation performed with 10 WCE videos, suggesting that our methods ably capture the intestinal motility information., (Copyright © 2015 Elsevier Ltd. All rights reserved.)

We present a fully automatic methodology for the detection of the Media-Adventitia border (MAb) in human coronary artery in Intravascular Ultrasound (IVUS) images. A robust border detection is achieved by means of a holistic interpretation of the detection problem where the target object, i.e. the media layer, is considered as part of the whole vessel in the image and all the relationships between tissues are learnt. A fairly general framework exploiting multi-class tissue characterization as well as contextual information on the morphology and the appearance of the tissues is presented. The methodology is (i) validated through an exhaustive comparison with both Inter-observer variability on two challenging databases and (ii) compared with state-of-the-art methods for the detection of the MAb in IVUS. The obtained averaged values for the mean radial distance and the percentage of area difference are 0.211 mm and 10.1%, respectively. The applicability of the proposed methodology to clinical practice is also discussed., (Copyright © 2012 Elsevier B.V. All rights reserved.)

Three-dimensional intravascular ultrasound (IVUS) allows to visualize and obtain volumetric measurements of coronary lesions through an exploration of the cross sections and longitudinal views of arteries. However, the visualization and subsequent morpho-geometric measurements in IVUS longitudinal cuts are subject to distortion caused by periodic image/vessel motion around the IVUS catheter. Usually, to overcome the image motion artifact ECG-gating and image-gated approaches are proposed, leading to slowing the pullback acquisition or disregarding part of IVUS data. In this paper, we argue that the image motion is due to 3-D vessel geometry as well as cardiac dynamics, and propose a dynamic model based on the tracking of an elliptical vessel approximation to recover the rigid transformation and align IVUS images without loosing any IVUS data. We report an extensive validation with synthetic simulated data and in vivo IVUS sequences of 30 patients achieving an average reduction of the image artifact of 97% in synthetic data and 79% in real-data. Our study shows that IVUS alignment improves longitudinal analysis of the IVUS data and is a necessary step towards accurate reconstruction and volumetric measurements of 3-D IVUS.

In this work a new statistic deformable model for 3D segmentation of anatomical organs in medical images is proposed. A statistic discriminant snake performs a supervised learning of the object boundary in an image slice to segment the next slice of the image sequence. Each part of the object boundary is projected in a feature space generated by a bank of Gaussian filters. Then, clusters corresponding to different boundary pieces are constructed by means of linear discriminant analysis. Finally, a parametric classifier is generated from each contour in the image slice and embodied into the snake energy-minimization process to guide the snake deformation in the next image slice. The discriminant snake selects and classifies image features by the parametric classifier and deforms to minimize the dissimilarity between the learned and found image features. The new approach is of particular interest for segmenting 3D images with anisotropic spatial resolution, and for tracking temporal image sequences. In particular, several anatomical organs from different imaging modalities are segmented and the results compared to expert tracings.