Deep Learning in Cardiology

The medical field is creating large amount of data that physicians are unable to decipher and use efficiently. Moreover, rule-based expert systems are inefficient in solving complicated medical tasks or for creating insights using big data. Deep learning has emerged as a more accurate and effective technology in a wide range of medical problems such as diagnosis, prediction and intervention. Deep learning is a representation learning method that consists of layers that transform the data non-linearly, thus, revealing hierarchical relationships and structures. In this review we survey deep learning application papers that use structured data, signal and imaging modalities from cardiology. We discuss the advantages and limitations of applying deep learning in cardiology that also apply in medicine in general, while proposing certain directions as the most viable for clinical use.Comment: 27 pages, 2 figures, 10 table

A Review on Computer Aided Diagnosis of Acute Brain Stroke.

Amongst the most common causes of death globally, stroke is one of top three affecting over 100 million people worldwide annually. There are two classes of stroke, namely ischemic stroke (due to impairment of blood supply, accounting for ~70% of all strokes) and hemorrhagic stroke (due to bleeding), both of which can result, if untreated, in permanently damaged brain tissue. The discovery that the affected brain tissue (i.e., 'ischemic penumbra') can be salvaged from permanent damage and the bourgeoning growth in computer aided diagnosis has led to major advances in stroke management. Abiding to the Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA) guidelines, we have surveyed a total of 177 research papers published between 2010 and 2021 to highlight the current status and challenges faced by computer aided diagnosis (CAD), machine learning (ML) and deep learning (DL) based techniques for CT and MRI as prime modalities for stroke detection and lesion region segmentation. This work concludes by showcasing the current requirement of this domain, the preferred modality, and prospective research areas

Prediction of final infarct volume from native CT perfusion and treatment parameters using deep learning

CT Perfusion (CTP) imaging has gained importance in the diagnosis of acute stroke. Conventional perfusion analysis performs a deconvolution of the measurements and thresholds the perfusion parameters to determine the tissue status. We pursue a data-driven and deconvolution-free approach, where a deep neural network learns to predict the final infarct volume directly from the native CTP images and metadata such as the time parameters and treatment. This would allow clinicians to simulate various treatments and gain insight into predicted tissue status over time. We demonstrate on a multicenter dataset that our approach is able to predict the final infarct and effectively uses the metadata. An ablation study shows that using the native CTP measurements instead of the deconvolved measurements improves the prediction.Comment: Accepted for publication in Medical Image Analysi

The Integration of Positron Emission Tomography With Magnetic Resonance Imaging

A number of laboratories and companies are currently exploring the development of integrated imaging systems for magnetic resonance imaging (MRI) and positron emission tomography (PET). Scanners for both preclinical and human research applications are being pursued. In contrast to the widely distributed and now quite mature PET/computed tomography technology, most PET/MRI designs allow for simultaneous rather than sequential acquisition of PET and MRI data. While this offers the possibility of novel imaging strategies, it also creates considerable challenges for acquiring artifact-free images from both modalities. This paper discusses the motivation for developing combined PET/MRI technology, outlines the obstacles in realizing such an integrated instrument, and presents recent progress in the development of both the instrumentation and of novel imaging agents for combined PET/MRI studies. The performance of the first-generation PET/MRI systems is described. Finally, a range of possible biomedical applications for PET/MRI are outlined

ISLES 2015 - A public evaluation benchmark for ischemic stroke lesion segmentation from multispectral MRI

Ischemic stroke is the most common cerebrovascular disease, and its diagnosis, treatment, and study relies on non-invasive imaging. Algorithms for stroke lesion segmentation from magnetic resonance imaging (MRI) volumes are intensely researched, but the reported results are largely incomparable due to different datasets and evaluation schemes. We approached this urgent problem of comparability with the Ischemic Stroke Lesion Segmentation (ISLES) challenge organized in conjunction with the MICCAI 2015 conference. In this paper we propose a common evaluation framework, describe the publicly available datasets, and present the results of the two sub-challenges: Sub-Acute Stroke Lesion Segmentation (SISS) and Stroke Perfusion Estimation (SPES). A total of 16 research groups participated with a wide range of state-of-the-art automatic segmentation algorithms. A thorough analysis of the obtained data enables a critical evaluation of the current state-of-the-art, recommendations for further developments, and the identification of remaining challenges. The segmentation of acute perfusion lesions addressed in SPES was found to be feasible. However, algorithms applied to sub-acute lesion segmentation in SISS still lack accuracy. Overall, no algorithmic characteristic of any method was found to perform superior to the others. Instead, the characteristics of stroke lesion appearances, their evolution, and the observed challenges should be studied in detail. The annotated ISLES image datasets continue to be publicly available through an online evaluation system to serve as an ongoing benchmarking resource (www.isles-challenge.org).Peer reviewe

Imaging of the Ischemic Penumbra in Acute Stroke

One of the main reasons for the soaring interest in acute ischemic stroke among radiologists is the advent of new magnetic resonance techniques such as diffusion-weighted imaging. This new modality has prompted us to seek a better understanding of the pathophysiologic mechanisms of cerebral ischemia/infarction. The ischemic penumbra is an important concept and tissue region because this is the target of various recanalization treatments during the acute phase of stroke. In this context, it is high time for a thorough review of the concept, especially from the imaging point of view

Multi-Modality Multi-Scale Cardiovascular Disease Subtypes Classification Using Raman Image and Medical History

Raman spectroscopy (RS) has been widely used for disease diagnosis, e.g., cardiovascular disease (CVD), owing to its efficiency and component-specific testing capabilities. A series of popular deep learning methods have recently been introduced to learn nuance features from RS for binary classifications and achieved outstanding performance than conventional machine learning methods. However, these existing deep learning methods still confront some challenges in classifying subtypes of CVD. For example, the nuance between subtypes is quite hard to capture and represent by intelligent models due to the chillingly similar shape of RS sequences. Moreover, medical history information is an essential resource for distinguishing subtypes, but they are underutilized. In light of this, we propose a multi-modality multi-scale model called M3S, which is a novel deep learning method with two core modules to address these issues. First, we convert RS data to various resolution images by the Gramian angular field (GAF) to enlarge nuance, and a two-branch structure is leveraged to get embeddings for distinction in the multi-scale feature extraction module. Second, a probability matrix and a weight matrix are used to enhance the classification capacity by combining the RS and medical history data in the multi-modality data fusion module. We perform extensive evaluations of M3S and found its outstanding performance on our in-house dataset, with accuracy, precision, recall, specificity, and F1 score of 0.9330, 0.9379, 0.9291, 0.9752, and 0.9334, respectively. These results demonstrate that the M3S has high performance and robustness compared with popular methods in diagnosing CVD subtypes

Automated infaction core delineation

Tato práce se zabývá návrhem metody, která by mohla lékařům poskytnout dodatečné informace při rozhodování o nejvhodnější léčbě pro pacienty s ischemickou cévní mozkovou příhodou. Navrhovaná metoda je automatická a dokáže označit nekrotickou tkáň na snímcích, které vznikly kombinací angiografického a nekontrastního vyšetření počítačovou tomografií. Tato kombinace dokáže pokrýt oblast celého mozku. Objem nekrotické tkáně je kritickým faktorem pro aplikaci trombolytické léčby. Takový parametr nemají lékaři v současné době k dispozici. Lidem, kteří přesáhnou určitou hodnotu objemu nekrotické tkáně, by neměla být aplikována trombolytická léčba, neboť je vysoké riziko dalšího krvácení.Katedra informatiky a výpočetní technikyObhájenoThis thesis deals with methods that could provide additional information for physicians when deciding about the most appropriate treatment for patients with ischemic strokes. The thesis proposes a method for automated infarction core delineation based on combination of angiography and non contract computed tomography examinations. Such perfused blood volume mapping has advantage in whole brain coverage. The volume of the infarction core area is the crucial factor for thrombolytic treatment indication. Such information is not yet available for physicians. Patients who exceed certain level of the infarction core volume should not be indicated for thrombolytic treatment because of high risk of further bleeding

Brain lesion volume and capacity for consent in stroke trials: potential regulatory barriers to the use of surrogate markers

<p><b>Background and Purpose:</b> European directives and legislation in some countries forbid inclusion of subjects incapable of consent in research if recruitment of patients capable of consent will yield similar results. We compared brain lesion volumes in stroke patients deemed to have capacity to consent with those defined as incapacitated.</p> <p><b>Methods:</b> Data were obtained from 3 trials recruiting patients primarily with cortical stroke syndromes. Patients were recruited within 24 hours of onset and used MRI based selection or outcome criteria. Method of recruitment was recorded with stroke severity, age, and brain lesion volumes on Diffusion Weighted Imaging.</p> <p><b>Results:</b> Of the 56 subjects included, 38 (68%) were recruited by assent and 18 (32%) by consent. The assent group had a median lesion volume of 18.35 cubic centimetres (cc) (interquartile range [IQR] 8.27–110.31 cc), compared to 2.79 cc (IQR 1.31–12.33 cc) when patients consented (P=0.0004). Lesions were smaller than 5 cc in 7/38 (18%) in the assent group and 11/18 (61%) in the consent group (P=0.0024). There was good correlation between neurological deficit by NIH stroke scale score and lesion volume (r=0.584, P<0.0001). Logistic regression demonstrated NIHSS or lesion volume predicted capacity to consent.</p> <p><b>Conclusions:</b> Patients with acute stroke who retain capacity to consent have significantly smaller infarct volumes than those incapable of consent, and these are frequently below the limits where measurement error significantly compromises valid use of volumetric end points. Only a small proportion of patients with capacity to consent would be eligible for, and contribute usefully to, most acute stroke trial protocols.</p&gt