Artificial Intelligence Pipeline for Risk Prediction in Cardiovascular Imaging

Abstract Introduction Emergency laparotomy has a considerable mortality risk, with more than one in ten patients not surviving to discharge. Preoperative risk prediction using clinical tools is well established, however implemented variably. Preoperative CT is undertaken almost universally and contains granular data beyond diagnostics, including body composition, disease severity and other abstract features with the potential to enhance risk prediction. In this study we established the value of features extracted in an automated fashion from pre-operative CT in predicting 90-day post-surgery mortality. Method Anonymised CTs were collated from patients undergoing emergency laparotomy at ten hospitals in Southern England (2016–2017). For each case, axial portal venous abdominal/pelvic series were analysed using a pre-trained neural network, with each image converted into a matrix of numerical features. An elastic-net regression model to predict 90-day mortality was trained using these features and evaluated by bootstrapping with 1000 resampled datasets. Result A total of 136,709 images from 274 cases were available for analysis with a mean of 503 per case. Mortality within 90 days occurred in 34 cases (12.4%) with an average NELA mortality prediction of 8.5%. On internal (bootstrap) validation, the elastic net model derived from CT yielded excellent performance (AUC 0.903 95%CI 0.897–0.909), significantly in excess of the NELA risk calculator (AUC 0.809 95%CI 0.736–0.875), with a broader prediction range (0.01%-89.71%). Conclusion Artificial intelligence techniques applied to routinely performed cross-sectional imaging predicts emergency laparotomy mortality with greater accuracy than clinical data alone. Integration of these automated tools may be possible in the future. Take-home Message Automated analysis of CT can accurately predict risk of mortality after emergency laparotomy.

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The year 2019 in the European Heart Journal – Cardiovascular Imaging: part II

European Heart Journal - Cardiovascular Imaging ◽

10.1093/ehjci/jeaa292 ◽

2020 ◽

Vol 21 (12) ◽

pp. 1331-1340

Author(s):

Bernard Cosyns ◽

Kristina H Haugaa ◽

Bernrhard Gerber ◽

Alessia Gimelli ◽

Leyla Elif Sade ◽

...

Keyword(s):

Heart Failure ◽

Congenital Heart Disease ◽

Heart Disease ◽

Risk Prediction ◽

Myocardial Ischaemia ◽

Valvular Heart Disease ◽

Congenital Heart ◽

Myocardial Function ◽

European Heart Journal ◽

Cardiovascular Imaging

Abstract The European Heart Journal – Cardiovascular Imaging was launched in 2012 and has during these years become one of the leading multimodality cardiovascular imaging journal. The journal is now established as one of the top cardiovascular journals and is the most important cardiovascular imaging journal in Europe. The most important studies published in our Journal from 2019 will be highlighted in two reports. Part II will focus on valvular heart disease, heart failure, cardiomyopathies, and congenital heart disease. While Part I of the review has focused on studies about myocardial function and risk prediction, myocardial ischaemia, and emerging techniques in cardiovascular imaging.

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Artificial Intelligence Applications to Improve Risk Prediction Tools in Electrophysiology

Current Cardiovascular Risk Reports ◽

10.1007/s12170-020-00649-1 ◽

2020 ◽

Vol 14 (9) ◽

Author(s):

Gurukripa N. Kowlgi ◽

Fatima M. Ezzeddine ◽

Suraj Kapa

Keyword(s):

Artificial Intelligence ◽

Risk Prediction ◽

Prediction Tools

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The utility of artificial intelligence in suicide risk prediction and the management of suicidal behaviors

Australian & New Zealand Journal of Psychiatry ◽

10.1177/0004867419864428 ◽

2019 ◽

Vol 53 (10) ◽

pp. 954-964 ◽

Cited By ~ 3

Author(s):

Trehani M Fonseka ◽

Venkat Bhat ◽

Sidney H Kennedy

Keyword(s):

Artificial Intelligence ◽

At Risk ◽

Risk Prediction ◽

Clinical Management ◽

Behavior Management ◽

Suicide Risk ◽

English Language ◽

Case Reports ◽

Mental Healthcare ◽

Health Concern

Objective: Suicide is a growing public health concern with a global prevalence of approximately 800,000 deaths per year. The current process of evaluating suicide risk is highly subjective, which can limit the efficacy and accuracy of prediction efforts. Consequently, suicide detection strategies are shifting toward artificial intelligence platforms that can identify patterns within ‘big data’ to generate risk algorithms that can determine the effects of risk (and protective) factors on suicide outcomes, predict suicide outbreaks and identify at-risk individuals or populations. In this review, we summarize the role of artificial intelligence in optimizing suicide risk prediction and behavior management. Methods: This paper provides a general review of the literature. A literature search was conducted in OVID Medline, EMBASE and PsycINFO databases with coverage from January 1990 to June 2019. Results were restricted to peer-reviewed, English-language articles. Conference and dissertation proceedings, case reports, protocol papers and opinion pieces were excluded. Reference lists were also examined for additional articles of relevance. Results: At the individual level, prediction analytics help to identify individuals in crisis to intervene with emotional support, crisis and psychoeducational resources, and alerts for emergency assistance. At the population level, algorithms can identify at-risk groups or suicide hotspots, which help inform resource mobilization, policy reform and advocacy efforts. Artificial intelligence has also been used to support the clinical management of suicide across diagnostics and evaluation, medication management and behavioral therapy delivery. There could be several advantages of incorporating artificial intelligence into suicide care, which includes a time- and resource-effective alternative to clinician-based strategies, adaptability to various settings and demographics, and suitability for use in remote locations with limited access to mental healthcare supports. Conclusion: Based on the observed benefits to date, artificial intelligence has a demonstrated utility within suicide prediction and clinical management efforts and will continue to advance mental healthcare forward.

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Artificial Intelligence in Cardiovascular Imaging

Journal of the American College of Cardiology ◽

10.1016/j.jacc.2018.12.054 ◽

2019 ◽

Vol 73 (11) ◽

pp. 1317-1335 ◽

Cited By ~ 72

Author(s):

Damini Dey ◽

Piotr J. Slomka ◽

Paul Leeson ◽

Dorin Comaniciu ◽

Sirish Shrestha ◽

...

Keyword(s):

Artificial Intelligence ◽

Cardiovascular Imaging

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Artificial intelligence in medical imaging: A radiomic guide to precision phenotyping of cardiovascular disease

Cardiovascular Research ◽

10.1093/cvr/cvaa021 ◽

2020 ◽

Vol 116 (13) ◽

pp. 2040-2054 ◽

Cited By ~ 5

Author(s):

Evangelos K Oikonomou ◽

Musib Siddique ◽

Charalambos Antoniades

Keyword(s):

Artificial Intelligence ◽

Cardiovascular Disease ◽

Computational Models ◽

Cardiac Computed Tomography ◽

Clinical Care ◽

Cardiovascular Imaging ◽

Current Status ◽

Current Evidence ◽

Scientific Validity ◽

Non Invasive

Abstract Rapid technological advances in non-invasive imaging, coupled with the availability of large data sets and the expansion of computational models and power, have revolutionized the role of imaging in medicine. Non-invasive imaging is the pillar of modern cardiovascular diagnostics, with modalities such as cardiac computed tomography (CT) now recognized as first-line options for cardiovascular risk stratification and the assessment of stable or even unstable patients. To date, cardiovascular imaging has lagged behind other fields, such as oncology, in the clinical translational of artificial intelligence (AI)-based approaches. We hereby review the current status of AI in non-invasive cardiovascular imaging, using cardiac CT as a running example of how novel machine learning (ML)-based radiomic approaches can improve clinical care. The integration of ML, deep learning, and radiomic methods has revealed direct links between tissue imaging phenotyping and tissue biology, with important clinical implications. More specifically, we discuss the current evidence, strengths, limitations, and future directions for AI in cardiac imaging and CT, as well as lessons that can be learned from other areas. Finally, we propose a scientific framework in order to ensure the clinical and scientific validity of future studies in this novel, yet highly promising field. Still in its infancy, AI-based cardiovascular imaging has a lot to offer to both the patients and their doctors as it catalyzes the transition towards a more precise phenotyping of cardiovascular disease.

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Hybrid Model for Credit Risk Prediction: An Application of Neural Network Approaches

International Journal of Artificial Intelligence Tools ◽

10.1142/s0218213019500179 ◽

2019 ◽

Vol 28 (05) ◽

pp. 1950017 ◽

Cited By ~ 1

Author(s):

Guotai Chi ◽

Mohammad Shamsu Uddin ◽

Mohammad Zoynul Abedin ◽

Kunpeng Yuan

Keyword(s):

Neural Network ◽

Artificial Intelligence ◽

Logistic Regression ◽

Credit Risk ◽

Risk Prediction ◽

Hybrid Model ◽

Credit Scoring ◽

Hybrid Models ◽

Multilayer Perceptrons ◽

Inference Systems

Credit risk prediction is essential for banks and financial institutions as it helps them to evade any inappropriate assessments that can lead to wasted opportunities or monetary losses. In recent times, the hybrid prediction model, a combination of traditional and modern artificial intelligence (AI) methods that provides better prediction capacity than the use of single techniques, has been introduced. Similarly, using conventional and topical artificial intelligence (AI) technologies, researchers have recommended hybrid models which amalgamate logistic regression (LR) with multilayer perceptron (MLP). To investigate the efficiency and viability of the proposed hybrid models, we compared 16 hybrid models created by combining logistic regression (LR), discriminant analysis (DA), and decision trees (DT) with four types of neural network (NN): adaptive neurofuzzy inference systems (ANFISs), deep neural networks (DNNs), radial basis function networks (RBFs) and multilayer perceptrons (MLPs). The experimental outcome, investigation, and statistical examination express the capacity of the planned hybrid model to develop a credit risk prediction technique different from all other approaches, as indicated by ten different performance measures. The classifier was authenticated on five real-world credit scoring data sets.

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