There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

https://www.zora.uzh.ch/id/eprint/146170/1/1-s2.0-S1078588416301782-main.pdf

Editor's Choice - Management of Descending Thoracic Aorta Diseases: Clinical Practice Guidelines of the European Society for Vascular Surgery (ESVS)

One of the most promising areas of health innovation is the application of artificial intelligence (AI), primarily in medical imaging. This article provides basic definitions of terms such as “machine/deep learning” and analyses the integration of AI into radiology. Publications on AI have drastically increased from about 100–150 per year in 2007–2008 to 700–800 per year in 2016–2017. Magnetic resonance imaging and computed tomography collectively account for more than 50% of current articles. Neuroradiology appears in about one-third of the papers, followed by musculoskeletal, cardiovascular, breast, urogenital, lung/thorax, and abdomen, each representing 6–9% of articles. With an irreversible increase in the amount of data and the possibility to use AI to identify findings either detectable or not by the human eye, radiology is now moving from a subjective perceptual skill to a more objective science. Radiologists, who were on the forefront of the digital era in medicine, can guide the introduction of AI into healthcare. Yet, they will not be replaced because radiology includes communication of diagnosis, consideration of patient’s values and preferences, medical judgment, quality assurance, education, policy-making, and interventional procedures. The higher efficiency provided by AI will allow radiologists to perform more value-added tasks, becoming more visible to patients and playing a vital role in multidisciplinary clinical teams.

/pdf/artificial-intelligence-in-medical-imaging-threat-or-2fzcyw9ypc.pdf

Artificial intelligence in medical imaging: threat or opportunity? Radiologists again at the forefront of innovation in medicine

Embodiments include a system for determining cardiovascular information for a patient. The system may include at least one computer system configured to receive patient-specific data regarding a geometry of the patient's heart, and create a three-dimensional model representing at least a portion of the patient's heart based on the patient-specific data. The at least one computer system may be further configured to create a physics-based model relating to a blood flow characteristic of the patient's heart and determine a fractional flow reserve within the patient's heart based on the three-dimensional model and the physics-based model.

Method and system for patient-specific modeling of blood flow

A method for providing a personalized evaluation of CAD for a patient includes acquiring one or more non-invasive images depicting a patient's coronary arteries and extracting a first set of features of interest from the one or more non-invasive images. A machine learning model is applied to the first set of features of interest to yield a prediction of one or more coronary measures of interest. One or more invasive images depicting the patient's coronary arteries are acquired and a second set of features of interest are extracted from the one or more invasive images. The first set of features of interest and the second set of features of interest are combined to yield a combined set of features of interest. Then, the machine learning model may be applied to the combined set of features of interest to yield an enhanced prediction of the coronary measures of interest.

Enhanced personalized evaluation of coronary artery disease using an integration of multiple medical imaging techniques

In this chapter we introduce methodologies for modeling whole-body cardiovascular dynamics Lumped parameter modeling techniques are employed to model both open-loop and closed-loop dynamics The main constituents of the model are the pulmonary arterial and venous circulation, the systemic arterial and venous circulation, and the four chambers of the heart A fully automated parameter estimation framework is introduced, which is based on two sequential steps: first, a series of parameters are computed directly, and, next, a fully automatic optimization-based calibration method is employed to iteratively estimate the values of the remaining parameters A detailed sensitivity analysis has been performed for identifying the parameters which require calibration Advanced objectives defined based on slopes and interval of times determined from the measured volume and pressure curves are formulated to improve the overall agreement between computed and measured quantities Furthermore, methods for modeling subtle influences, eg from the KG diaphragm, and pathologic heart valves (stenosed, regurgitant) are introduced

Lumped Parameter Whole Body Circulation Modelling

Background: The prevalence of chronic kidney disease (CKD) correlates with the prevalence of hypertension (HT). We studied the prevalence and predictors of CKD in a representative sample of the Romanian adult population. Methods: A sample of 1470 subjects were enrolled in the SEPHAR IV (Study for the Evaluation of Prevalence of Hypertension and Cardiovascular Risk) survey. All subjects were evaluated for blood pressure (BP) and extensive evaluations of target organ damage, blood, and urine samples were undertaken. Results: A total of 883 subjects were included in the statistical analysis. Those experiencing CKD with an eGFR < 60 mL/min/1.73 m2 were older at 71.94 ± 7.4 years (n = 19, 2.15%) compared with those without renal impairment at 50.3 ± 16.21 years (n = 864, 97.85%), p < 0.0001. The prevalence of CKD among hypertensives (379 from 883) was 4.49% (17/379), while 17 out of 19 subjects with CKD had HT (89.47%). After adjusting for age, sex, and diabetic status, only serum uric acid (SUR) > 6.9 mg/dL (OR: 6.61; 95% CI: 2.063, 10.83; p = 0.004) was an independent risk factor and a predictor of CKD. Conclusions: The prevalence of CKD in hypertensive Romanian adults was more than ten times higher than in the normotensive population. Levels of SUR > 6.9 mg/dL were predictors of CKD.

/pdf/prevalence-and-predictors-of-renal-disease-in-a-national-18rfcc5t.pdf

Prevalence and Predictors of Renal Disease in a National Representative Sample of the Romanian Adult Population: Data from the SEPHAR IV Survey

A CT-based clinical decision support system provides coronary decision support. With or without CT-FFR, a machine learnt predictor predicts the clinical decision for the patient based on input from various sources. Using the machine learnt predictor provides more consistent and comprehensive consideration of the available information. The clinical decision support may be provided prior to review of coronary CT data by a radiologist and/or treating physician, providing a starting point or recommendation that may be used by the radiologist and/or treating physician.

Coronary computed tomography clinical decision support system

TCT-40 Image-Based Computation of Instantaneous Wave-free Ratio from Routine Coronary Angiography - Initial Validation by Invasively Measured Coronary Pressures

Lucian Mihai Itu

Papers

Enhanced personalized evaluation of coronary artery disease using an integration of multiple medical imaging techniques

Lumped Parameter Whole Body Circulation Modelling

Prevalence and Predictors of Renal Disease in a National Representative Sample of the Romanian Adult Population: Data from the SEPHAR IV Survey

Coronary computed tomography clinical decision support system

TCT-40 Image-Based Computation of Instantaneous Wave-free Ratio from Routine Coronary Angiography - Initial Validation by Invasively Measured Coronary Pressures