The PASCAL Visual Object Classes Challenge

Colloquially known as coronavirus, the Severe Acute Respiratory Syndrome CoronaVirus 2 (SARS-CoV-2), that causes CoronaVirus Disease 2019 (COVID-19), has become a matter of grave concern for every country around the world. The rapid growth of the pandemic has wreaked havoc and prompted the need for immediate reactions to curb the effects. To manage the problems, many research in a variety of area of science have started studying the issue. Artificial Intelligence is among the area of science that has found great applications in tackling the problem in many aspects. Here, we perform an overview on the applications of AI in a variety of fields including diagnosis of the disease via different types of tests and symptoms, monitoring patients, identifying severity of a patient, processing covid-19 related imaging tests, epidemiology, pharmaceutical studies, etc. The aim of this paper is to perform a comprehensive survey on the applications of AI in battling against the difficulties the outbreak has caused. Thus we cover every way that AI approaches have been employed and to cover all the research until the writing of this paper. We try organize the works in a way that overall picture is comprehensible. Such a picture, although full of details, is very helpful in understand where AI sits in current pandemonium. We also tried to conclude the paper with ideas on how the problems can be tackled in a better way and provide some suggestions for future works.

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Applications of artificial intelligence in battling against covid-19: A literature review.

Novel Coronavirus pandemic, which negatively affected public health in social, psychological and economical terms, spread to the whole world in a short period of 6 months However, the rate of increase in cases was not equal for every country The measures implemented by the countries changed the daily spreading speed of the disease This was determined by changes in the number of daily cases In this study, the performance of the Random Forest (RF) machine learning algorithm was investigated in estimating the near future case numbers for 190 countries in the world and it is mapped in comparison with actual confirmed cases results The number of confirmed cases between 23/01/2020 - 17/06/2020 were divided into 3 main sub-datasets: training sub-data, testing sub-data (interpolation data) and estimating sub-data (extrapolation data) for the random forest model At the end of the study, it has been found that R2 values for testing sub-data of RF model estimates range between 0843 and 0995 (average R2= 0959), and RMSE values between 14176 and 52618 (mean RMSE = 25938); and that R2 values for estimating sub-data range between 0690 and 0968 (mean R2 = 0914), and RMSE values between 54973 and 250079 (mean RMSE = 90937) These results show that the random forest machine learning algorithm performs well in estimating the number of cases for the near future in case of an epidemic like Novel Coronavirus, which outbreaks suddenly and spreads rapidly

Spatio-temporal estimation of the daily cases of COVID-19 in worldwide using random forest machine learning algorithm.

Objectives The rRT-PCR test, the current gold standard for the detection of coronavirus disease (COVID-19), presents with known shortcomings, such as long turnaround time, potential shortage of reagents, false-negative rates around 15-20%, and expensive equipment. The hematochemical values of routine blood exams could represent a faster and less expensive alternative. Methods Three different training data set of hematochemical values from 1,624 patients (52% COVID-19 positive), admitted at San Raphael Hospital (OSR) from February to May 2020, were used for developing machine learning (ML) models: the complete OSR dataset (72 features: complete blood count (CBC), biochemical, coagulation, hemogasanalysis and CO-Oxymetry values, age, sex and specific symptoms at triage) and two sub-datasets (COVID-specific and CBC dataset, 32 and 21 features respectively). 58 cases (50% COVID-19 positive) from another hospital, and 54 negative patients collected in 2018 at OSR, were used for internal-external and external validation. Results We developed five ML models: for the complete OSR dataset, the area under the receiver operating characteristic curve (AUC) for the algorithms ranged from 0.83 to 0.90; for the COVID-specific dataset from 0.83 to 0.87; and for the CBC dataset from 0.74 to 0.86. The validations also achieved good results: respectively, AUC from 0.75 to 0.78; and specificity from 0.92 to 0.96. Conclusions ML can be applied to blood tests as both an adjunct and alternative method to rRT-PCR for the fast and cost-effective identification of COVID-19-positive patients. This is especially useful in developing countries, or in countries facing an increase in contagions.

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Development, evaluation, and validation of machine learning models for COVID-19 detection based on routine blood tests.

Ensemble learning model for diagnosing COVID-19 from routine blood tests

X-ray angiography is the most commonly used medical imaging modality for the high resolution visualization of lumen structure in coronary arteries. Since the interpretation of 3D vascular geometry using multiple 2D image projections results in high intra- and inter-observer variability, the reconstruction of 3D coronary arterial (CA) tree is necessary. The automated 3D CA tree reconstruction from multiple 2D projections is challenging due to the existence of several imaging artifacts, most importantly the respiratory and cardiac motion. In this regard, the aim of the proposed work is to remove the effects of motion artifacts from non-simultaneous angiographic projections by developing a new iterative method for rigid motion correction. Our proposed approach is based on the optimal estimation of rigid transformation, occurred due to motion in the 3D tree, from each projection. The performance of the technique is qualitatively and quantitatively demonstrated using multiple angiographic projections of the left anterior descending, left circumflex, and right coronary artery from 15 patients.

Optimized Rigid Motion Correction from Multiple Non-simultaneous X-Ray Angiographic Projections

Background Patients with a history of COVID-19 infection are reported to have cardiac abnormalities on cardiovascular magnetic resonance (CMR) during convalescence. However, it is unclear whether these abnormalities were present during the acute COVID-19 illness and how they may evolve over time. Methods We prospectively recruited unvaccinated patients hospitalized with acute COVID-19 (n = 23), and compared them with matched outpatient controls without COVID-19 (n = 19) between May 2020 and May 2021. Only those without a past history of cardiac disease were recruited. We performed in-hospital CMR at a median of 3 days (IQR 1–7 days) after admission, and assessed cardiac function, edema and necrosis/fibrosis, using left and right ventricular ejection fraction (LVEF, RVEF), T1-mapping, T2 signal intensity ratio (T2SI), late gadolinium enhancement (LGE) and extracellular volume (ECV). Acute COVID-19 patients were invited for follow-up CMR and blood tests at 6 months. Results The two cohorts were well matched in baseline clinical characteristics. Both had normal LVEF (62 ± 7 vs. 65 ± 6%), RVEF (60 ± 6 vs. 58 ± 6%), ECV (31 ± 3 vs. 31 ± 4%), and similar frequency of LGE abnormalities (16 vs. 14%; all p > 0.05). However, measures of acute myocardial edema (T1 and T2SI) were significantly higher in patients with acute COVID-19 when compared to controls (T1 = 1,217 ± 41 ms vs. 1,183 ± 22 ms; p = 0.002; T2SI = 1.48 ± 0.36 vs. 1.13 ± 0.09; p < 0.001). All COVID-19 patients who returned for follow up (n = 12) at 6 months had normal biventricular function, T1 and T2SI. Conclusion Unvaccinated patients hospitalized for acute COVID-19 demonstrated CMR imaging evidence of acute myocardial edema, which normalized at 6 months, while biventricular function and scar burden were similar when compared to controls. Acute COVID-19 appears to induce acute myocardial edema in some patients, which resolves in convalescence, without significant impact on biventricular structure and function in the acute and short-term. Further studies with larger numbers are needed to confirm these findings.

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Acute changes in myocardial tissue characteristics during hospitalization in patients with COVID-19

Myocardial infarction (MI) is one of the most common causes of death in the world. Image-based biomarkers commonly used in the clinic, such as ejection fraction, fail to capture more complex patterns in the heart's 3D anatomy and thus limit diagnostic accuracy. In this work, we present the multi-objective point cloud autoencoder as a novel geometric deep learning approach for explainable infarction prediction, based on multi-class 3D point cloud representations of cardiac anatomy and function. Its architecture consists of multiple task-specific branches connected by a low-dimensional latent space to allow for effective multi-objective learning of both reconstruction and MI prediction, while capturing pathology-specific 3D shape information in an interpretable latent space. Furthermore, its hierarchical branch design with point cloud-based deep learning operations enables efficient multi-scale feature learning directly on high-resolution anatomy point clouds. In our experiments on a large UK Biobank dataset, the multi-objective point cloud autoencoder is able to accurately reconstruct multi-temporal 3D shapes with Chamfer distances between predicted and input anatomies below the underlying images' pixel resolution. Our method outperforms multiple machine learning and deep learning benchmarks for the task of incident MI prediction by 19% in terms of Area Under the Receiver Operating Characteristic curve. In addition, its task-specific compact latent space exhibits easily separable control and MI clusters with clinically plausible associations between subject encodings and corresponding 3D shapes, thus demonstrating the explainability of the prediction.

Multi-objective point cloud autoencoders for explainable myocardial infarction prediction

Abstract Funding Acknowledgements Type of funding sources: None. Background The strive towards personalised medicine is universal across all aspects of healthcare. In the management of atrial fibrillation (AF), this can take the form of shape analysis tools to predict an individual’s outcome from catheter ablation (1–3), or electrophysiological simulations to plan an ablation strategy (4–6). Fundamental to their application is our ability to derive accurate three-dimensional (3D) anatomical models of a patient’s left atrial geometry from cross-sectional imaging. MRI is widely utilised in this regard due to its excellent tissue characterisation. However, constructing surface meshes can be challenging given its relatively sparse two-dimensional (2D) image planes. A particular challenge exists in producing a mesh that balances concordance with left atrium segmentation and smoothing terms to eliminate sharp edges and artifact from the contouring process. Objective Here, we describe a novel method for left atrium surface mesh reconstruction from MRI contours. Methods Cardiac MRI was performed on ten patients with paroxysmal or persistent AF, on a 3T clinical MR scanner using phased-array receiver coils. Image sequences included respiratory and ECG-gated 15-minute post contrast late gadolinium enhancement, with a 1.3mm slice thickness and in-plane resolution of 0.66×0.66mm2. Seven individuals were in sinus rhythm, and three in AF at the time of MRI. A single, custom, graphical interface tool implemented in Matlab R2014a enabled 3D mesh reconstruction from 2D MRI slices (7,8). Endocardial surfaces of the left atrium were manually segmented by an experienced clinician, with the pulmonary veins and left atrial appendage excluded at their ostia (Figure 1A). Our tool produced an initial sparse representation with vertices equally spaced along the contours (Figure 1B). Attractor points were defined along the contours, with subdivision and Laplacian smoothing applied to the mesh. This was balanced with a deformation step, which iteratively pulled the mesh towards the attractor points (Figure 1C), resulting in its final shape (Figure 1D). Results Reconstruction performance was evaluated by measuring the distance between heart contours and the reconstructed 3D surface for each case, producing the value for average misalignment 0.71 ± 0.58mm, which compares favourably to the in-plane resolution. Conclusions Our approach offers a highly accurate method for producing 3D anatomical models of the left atrium from MRI, which is fully automated following segmentation through an interactive graphical interface. The iterative optimisation between attractor-based deformation towards the original contours and the Laplacian smoothing enables us to create smooth geometries which remain true to their segmentations. Furthermore, our approach does not require the use of a template mesh, which can result in final geometries being biased towards the template. Figure 1

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Left atrium surface mesh reconstruction from cardiac MRI

Myocardial infarction (MI) demands precise and swift diagnosis. Cardiac digital twins (CDTs) have the potential to offer individualized evaluation of cardiac function in a non-invasive manner, making them a promising approach for personalized diagnosis and treatment planning of MI. The inference of accurate myocardial tissue properties is crucial in creating a reliable CDT platform, and particularly in the context of studying MI. In this work, we investigate the feasibility of inferring myocardial tissue properties from the electrocardiogram (ECG), focusing on the development of a comprehensive CDT platform specifically designed for MI. The platform integrates multi-modal data, such as cardiac MRI and ECG, to enhance the accuracy and reliability of the inferred tissue properties. We perform a sensitivity analysis based on computer simulations, systematically exploring the effects of infarct location, size, degree of transmurality, and electrical activity alteration on the simulated QRS complex of ECG, to establish the limits of the approach. We subsequently propose a deep computational model to infer infarct location and distribution from the simulated QRS. The in silico experimental results show that our model can effectively capture the complex relationships between the QRS signals and the corresponding infarct regions, with promising potential for clinical application in the future. The code will be released publicly once the manuscript is accepted for publication.

Abhirup Banerjee

Papers

Optimized Rigid Motion Correction from Multiple Non-simultaneous X-Ray Angiographic Projections

Acute changes in myocardial tissue characteristics during hospitalization in patients with COVID-19

Multi-objective point cloud autoencoders for explainable myocardial infarction prediction

Left atrium surface mesh reconstruction from cardiac MRI

Towards Enabling Cardiac Digital Twins of Myocardial Infarction Using Deep Computational Models for Inverse Inference