An accurate and time-efficient deep learning-based system for automated segmentation and reporting of cardiac magnetic resonance-detected ischemic scar

Background and objectives: Myocardial infarction scar (MIS) assessment by cardiac magnetic resonance provides prognostic information and guides patients' clinical management. However, MIS segmentation is time-consuming and not performed routinely. This study presents a deep-learning-based computational workflow for the segmentation of left ventricular (LV) MIS, for the first time performed on state-of-the-art dark-blood late gadolinium enhancement (DB-LGE) images, and the computation of MIS transmurality and extent.Methods: DB-LGE short-axis images of consecutive patients with myocardial infarction were acquired at 1.5T in two centres between Jan 1, 2019, and June 1, 2021. Two convolutional neural network (CNN) mod-els based on the U-Net architecture were trained to sequentially segment the LV and MIS, by processing an incoming series of DB-LGE images. A 5-fold cross-validation was performed to assess the performance of the models. Model outputs were compared respectively with manual (LV endo-and epicardial border) and semi-automated (MIS, 4-Standard Deviation technique) ground truth to assess the accuracy of the segmentation. An automated post-processing and reporting tool was developed, computing MIS extent (expressed as relative infarcted mass) and transmurality.Results: The dataset included 1355 DB-LGE short-axis images from 144 patients (MIS in 942 images). High performance (> 0.85) as measured by the Intersection over Union metric was obtained for both the LV and MIS segmentations on the training sets. The performance for both LV and MIS segmentations was 0.83 on the test sets.Compared to the 4-Standard Deviation segmentation technique, our system was five times quicker ( <1 min versus 7 +/- 3 min), and required minimal user interaction. Conclusions: Our solution successfully addresses different issues related to automatic MIS segmentation, including accuracy, time-effectiveness, and the automatic generation of a clinical report.(c) 2022 Elsevier B.V. All rights reserved

AI Enabled Drug Design and Side Effect Prediction Powered by Multi-Objective Evolutionary Algorithms & Transformer Models

Due to the large search space and conflicting objectives, drug design and discovery is a difficult problem for which new machine learning (ML) approaches are required. Here, the problem is to invent a method by which new, therapeutically useful, compounds can be discovered; and to simultaneously avoid compounds which will fail clinical trials or pass unwanted effects onto the end patient. By extending current technologies as well as adding new ones, more design criteria can be included, and more promising novel drugs can be discovered. This work advances the field of computational drug design by (1) developing MOEA-DT, a non-deep learning application for multi-objective molecular optimization, which generates new molecules with high performance in a variety of design criteria; and (2) developing SEMTL-BERT, a side effect prediction algorithm which leverages the latest ML techniques and datasets to accomplish its task. Experiments performed show that MOEA-DT either matches or outperforms other similar methods, and that SEMTL-BERT can enhance predictive ability

sistem deteksi kelainan jantung pada penyakit Acute Myocardial Infarction menggunakan sinyal elektrokardiogram dengan metode Aldrich Score

ABSTRAKSI: Acute Myocardial Infarction yang sering diartikan sebagai serangan jantung akut atau kematian jaringan otot jantung adalah penyakit jantung yang disebabkan oleh adanya sumbatan akut pada Arteri Koroner. Sumbatan akut ini terjadi karena adanya lesi lemak yang berbentuk plak pada dinding Arteri Koroner, sehingga menyumbat aliran darah ke jaringan otot jantung. Penderita Acute Myocardial Infarction (AMI) harus segera di tangani untuk mencegah terjadinya Infark miokard atau kematian otot jantung yang semakin luas. Salah satu teknik utama dalam mendiagnosis penyakit jantung adalah berdasarkan rekaman sinyal Elektrokardiogram (EKG) sehingga dapat ditarik kesimpulan secara cepat seberapa besar kematian otot jantung yang terjadi. Hal ini penting agar dapat ditangani secara cepat pula dengan pemilihan pengobatan yang sesuai dengan masing-masing tingkat dan daerah kematian otot jantung yang terjadi .Dalam tugas akhir ini menggunakan metode ekstraksi ciri Empirical Mode Decomposition (EMD). EMD memiliki cara kerja yaitu menguraikan sinyal asli menjadi dua bagian yaitu Independent Intrinsic Mode Function (IMFs) dan komponen sisa. Proses penghitungannya yaitu dengan cara mengurangi jumlah sinyal yang diamati dengan mean (rata-rata) dari jumlah sinyal tadi. Langkah penghitungan tersebut dilakukan berulang-ulang hingga didapatkan kondisi sinyal yang stabil. Untuk metode klasifikasi menggunakan Aldrich Score karena memiliki parameter Theoritical Memory Occupation (TMO) atau penempatan memori yang rendah dan Complexity Computational (CC) yang sederhana sehingga perhitungan dapat lebih cepat.Hasil akhir dari tugas akhir ini adalah suatu program yang mampu mendeteksi kondisi jantung seseorang berdasarkan sinyal yang dihasilkan oleh EKG yang diolah menggunakan metode EMD dan Aldrich Score. Kondisi jantung dibagi menjadi 2 yaitu AMI (Anterior atau Inferior) dan non AMI beserta persentase kematian otot yang terjadi pada jantung. Tingkat pengenalan terbaik yang diujikan pada lead 2 pada IMF 2 dengan CC antara 15 s/d 24 operasi, TMO antara 11 s/d 17 bytes dengan processing time 8.1239 s/d 17.5696 detik. Oleh karena itu, metode ini cukup representatif untuk mengenali sinyal EKG yang diujicobakan.Kata Kunci : Acute Myocardial Infarction (AMI), Aldrich Score Method, Theoritical MemoryABSTRACT: Acute Myocardial Infarction is often interpreted as an acute heart attack or death of heart muscle tissue is heart disease caused by the presence of acute obstruction on Coronary Artery. Acute blockage occurs because of lesions in the form of fatty plaque on the wall of Coronary Artery, thus blocking blood flow to heart muscle tissue. Patients with Acute Myocardial Infarction (AMI) should be handled to prevent the occurrence of myocardial infarction or death of heart muscle that is increasingly widespread. One of the main techniques in diagnosing heart disease is based on the signal recording electrocardiogram (ECG) so that it can be concluded quickly how much the death of heart muscle that occurs. This is important so that can be handled sooner with the selection of an appropriate treatment to each level and area of heart muscle death that occurred.In this Final Project a method of feature extraction using empirical Mode Decomposition (EMD). EMD has a way of working which describes the original signal into two parts, namely the Independent Intrinsic Mode Function (IMFs) and the remaining components. Process is calculated by subtracting the number of signals were observed with a mean (average) of the amount of the signal. Calculation steps are repeated until the obtained conditions for a stable signal. For the classification method using Aldrich score because it has Theoretical parameters Memory Occupation (TMO) or placement of a low memory and Computational Complexity (CC) is simple so that calculations can be faster.The end result of this final project is a program that can detect a person\u27s heart condition based on signals generated by the ECG is processed using the method of EMD and Aldrich Score. Heart conditions were divided into 2 of AMI (anterior or inferior) and non-AMI together with the percentage of deaths that occur in heart muscle. The best recognition rate is tested in lead 2 on the IMF 2 with CC between 15 s / d 24 operations, TMO between 11 s / d 17 bytes with a processing time 8.1239 s / d 17.5696 seconds. Therefore, this method is sufficiently representative to recognize the ECG signals are tested.Keyword: Acute Myocardial Infarction (AMI), Aldrich Score Method, Theoritical Memor

Echocardiography

The book "Echocardiography - New Techniques" brings worldwide contributions from highly acclaimed clinical and imaging science investigators, and representatives from academic medical centers. Each chapter is designed and written to be accessible to those with a basic knowledge of echocardiography. Additionally, the chapters are meant to be stimulating and educational to the experts and investigators in the field of echocardiography. This book is aimed primarily at cardiology fellows on their basic echocardiography rotation, fellows in general internal medicine, radiology and emergency medicine, and experts in the arena of echocardiography. Over the last few decades, the rate of technological advancements has developed dramatically, resulting in new techniques and improved echocardiographic imaging. The authors of this book focused on presenting the most advanced techniques useful in today's research and in daily clinical practice. These advanced techniques are utilized in the detection of different cardiac pathologies in patients, in contributing to their clinical decision, as well as follow-up and outcome predictions. In addition to the advanced techniques covered, this book expounds upon several special pathologies with respect to the functions of echocardiography

Multi-scale symbolic entropy analysis provides prognostic prediction in patients receiving extracorporeal life support

Introduction: Extracorporeal life support (ECLS) can temporarily support cardiopulmonary function, and is occasionally used in resuscitation. Multi-scale entropy (MSE) derived from heart rate variability (HRV) is a powerful tool in outcome prediction of patients with cardiovascular diseases. Multi-scale symbolic entropy analysis (MSsE), a new method derived from MSE, mitigates the effect of arrhythmia on analysis. The objective is to evaluate the prognostic value of MSsE in patients receiving ECLS. The primary outcome is death or urgent transplantation during the index admission. Methods: Fifty-seven patients receiving ECLS less than 24 hours and 23 control subjects were enrolled. Digital 24-hour Holter electrocardiograms were recorded and three MSsE parameters (slope 5, Area 6–20, Area 6–40) associated with the multiscale correlation and complexity of heart beat fluctuation were calculated. Results: Patients receiving ECLS had significantly lower value of slope 5, area 6 to 20, and area 6 to 40 than control subjects. During the follow-up period, 29 patients met primary outcome. Age, slope 5, Area 6 to 20, Area 6 to 40, acute physiology and chronic health evaluation II score, multiple organ dysfunction score (MODS), logistic organ dysfunction score (LODS), and myocardial infarction history were significantly associated with primary outcome. Slope 5 showed the greatest discriminatory power. In a net reclassification improvement model, slope 5 significantly improved the predictive power of LODS; Area 6 to 20 and Area 6 to 40 significantly improved the predictive power in MODS. In an integrated discrimination improvement model, slope 5 added significantly to the prediction power of each clinical parameter. Area 6 to 20 and Area 6 to 40 significantly improved the predictive power in sequential organ failure assessment. Conclusions: MSsE provides additional prognostic information in patients receiving ECLS. Electronic supplementary material The online version of this article (doi:10.1186/s13054-014-0548-3) contains supplementary material, which is available to authorized users

Role of deep learning techniques in non-invasive diagnosis of human diseases.

Machine learning, a sub-discipline in the domain of artificial intelligence, concentrates on algorithms able to learn and/or adapt their structure (e.g., parameters) based on a set of observed data. The adaptation is performed by optimizing over a cost function. Machine learning obtained a great attention in the biomedical community because it offers a promise for improving sensitivity and/or specificity of detection and diagnosis of diseases. It also can increase objectivity of the decision making, decrease the time and effort on health care professionals during the process of disease detection and diagnosis. The potential impact of machine learning is greater than ever due to the increase in medical data being acquired, the presence of novel modalities being developed and the complexity of medical data. In all of these scenarios, machine learning can come up with new tools for interpreting the complex datasets that confront clinicians. Much of the excitement for the application of machine learning to biomedical research comes from the development of deep learning which is modeled after computation in the brain. Deep learning can help in attaining insights that would be impossible to obtain through manual analysis. Deep learning algorithms and in particular convolutional neural networks are different from traditional machine learning approaches. Deep learning algorithms are known by their ability to learn complex representations to enhance pattern recognition from raw data. On the other hand, traditional machine learning requires human engineering and domain expertise to design feature extractors and structure data. With increasing demands upon current radiologists, there are growing needs for automating the diagnosis. This is a concern that deep learning is able to address. In this dissertation, we present four different successful applications of deep learning for diseases diagnosis. All the work presented in the dissertation utilizes medical images. In the first application, we introduce a deep-learning based computer-aided diagnostic system for the early detection of acute renal transplant rejection. The system is based on the fusion of both imaging markers (apparent diffusion coefficients derived from diffusion-weighted magnetic resonance imaging) and clinical biomarkers (creatinine clearance and serum plasma creatinine). The fused data is then used as an input to train and test a convolutional neural network based classifier. The proposed system is tested on scans collected from 56 subjects from geographically diverse populations and different scanner types/image collection protocols. The overall accuracy of the proposed system is 92.9% with 93.3% sensitivity and 92.3% specificity in distinguishing non-rejected kidney transplants from rejected ones. In the second application, we propose a novel deep learning approach for the automated segmentation and quantification of the LV from cardiac cine MR images. We aimed at achieving lower errors for the estimated heart parameters compared to the previous studies by proposing a novel deep learning segmentation method. Using fully convolutional neural networks, we proposed novel methods for the extraction of a region of interest that contains the left ventricle, and the segmentation of the left ventricle. Following myocardial segmentation, functional and mass parameters of the left ventricle are estimated. Automated Cardiac Diagnosis Challenge dataset was used to validate our framework, which gave better segmentation, accurate estimation of cardiac parameters, and produced less error compared to other methods applied on the same dataset. Furthermore, we showed that our segmentation approach generalizes well across different datasets by testing its performance on a locally acquired dataset. In the third application, we propose a novel deep learning approach for automated quantification of strain from cardiac cine MR images of mice. For strain analysis, we developed a Laplace-based approach to track the LV wall points by solving the Laplace equation between the LV contours of each two successive image frames over the cardiac cycle. Following tracking, the strain estimation is performed using the Lagrangian-based approach. This new automated system for strain analysis was validated by comparing the outcome of these analysis with the tagged MR images from the same mice. There were no significant differences between the strain data obtained from our algorithm using cine compared to tagged MR imaging. In the fourth application, we demonstrate how a deep learning approach can be utilized for the automated classification of kidney histopathological images. Our approach can classify four classes: the fat, the parenchyma, the clear cell renal cell carcinoma, and the unusual cancer which has been discovered recently, called clear cell papillary renal cell carcinoma. Our framework consists of three convolutional neural networks and the whole-slide kidney images were divided into patches with three different sizes to be inputted to the networks. Our approach can provide patch-wise and pixel-wise classification. Our approach classified the four classes accurately and surpassed other state-of-the-art methods such as ResNet (pixel accuracy: 0.89 Resnet18, 0.93 proposed). In conclusion, the results of our proposed systems demonstrate the potential of deep learning for the efficient, reproducible, fast, and affordable disease diagnosis

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