Image-Based Cardiac Diagnosis With Machine Learning: A Review

Cardiac imaging plays an important role in the diagnosis of cardiovascular disease (CVD). Until now, its role has been limited to visual and quantitative assessment of cardiac structure and function. However, with the advent of big data and machine learning, new opportunities are emerging to build artificial intelligence tools that will directly assist the clinician in the diagnosis of CVDs. This paper presents a thorough review of recent works in this field and provide the reader with a detailed presentation of the machine learning methods that can be further exploited to enable more automated, precise and early diagnosis of most CVDs

Pacing stress echocardiography: an alternative to pharmacologic stress testing

AbstractOBJECTIVESWe sought to evaluate the diagnostic accuracy and feasibility of bedside pacing stress echocardiography (PASE) as a potential substitute for pharmacologic stress echocardiography in patients admitted to the hospital with new-onset chest pain or worsening angina pectoris.BACKGROUNDAccurate and rapid noninvasive identification and evaluation of the extent of coronary artery disease (CAD) is essential for optimal management of these patients.METHODSBedside transthoracic stress echocardiography was performed in 54 consecutive patients admitted to a community hospital with new-onset chest pain, after acute myocardial infarction had been excluded. We used 10F transesophageal pacing catheters and a rapid and modified pacing protocol. The PASE results were validated in all patients by coronary angiography performed within 24 h of the test. Significant CAD was defined as ≥75% stenosis in at least one major epicardial coronary artery.RESULTSThe sensitivity of PASE for identifying patients with significant CAD was 95%, specificity was 87% and accuracy was 92%. The extent of significant CAD (single- or multivessel disease) was highly concordant with coronary angiography (kappa = 0.73, p < 0.001). Pacing stress echocardiography was well tolerated, and only 4% of the patients had minor adverse events. The mean rate–pressure product at peak pacing was 22,313 ± 5,357 beats/min per mm Hg, and heart rate >85% of the age-predicted target was achieved in 94% of patients. The average duration of the bedside PASE test, including image interpretation, was 38 ± 6 min.CONCLUSIONSBedside PASE is rapid, tolerable and accurate for identification of significant CAD in patients admitted to the hospital with new-onset chest pain or worsening angina pectoris

DIMETER: a haptic master device for tremor diagnosis in neurodegenerative diseases

In this study, a device based on patient motion capture is developed for the reliable and non-invasive diagnosis of neurodegenerative diseases. The primary objective of this study is the classification of differential diagnosis between Parkinson's disease (PD) and essential tremor (ET). The DIMETER system has been used in the diagnoses of a significant number of patients at two medical centers in Spain. Research studies on classification have primarily focused on the use of well-known and reliable diagnosis criteria developed by qualified personnel. Here, we first present a literature review of the methods used to detect and evaluate tremor; then, we describe the DIMETER device in terms of the software and hardware used and the battery of tests developed to obtain the best diagnoses. All of the tests are classified and described in terms of the characteristics of the data obtained. A list of parameters obtained from the tests is provided, and the results obtained using multilayer perceptron (MLP) neural networks are presented and analyzed

Characterisation and correction of respiratory-motion artefacts in cardiac PET-CT

Respiratory motion during cardiac Positron Emission Tomography (PET) Computed Tomography (CT) imaging results in blurring of the PET data and can induce mismatches between the PET and CT datasets, leading to attenuation-correction artefacts. The aim of this project was to develop a method of motion-correction to overcome both of these problems. The approach implemented was to transform a single CT to match the frames of a gated PET study, to facilitate respiratory-matched attenuation-correction, without the need for a gated CT. This is benecial for lowering the radiation dose to the patient and in reducing PETCT mismatches, which can arise even in gated studies. The heart and diaphragm were identied through phantom studies as the structures responsible for generating attenuation-correction artefacts in the heart and their motions therefore needed to be considered in transforming the CT. Estimating heart motion was straight-forward, due to its high contrast in PET, however the poor diaphragm contrast meant that additional information was required to track its position. Therefore a diaphragm shape model was constructed using segmented diaphragm surfaces, enabling complete diaphragm surfaces to be produced from incomplete and noisy initial estimates. These complete surfaces, in combination with the estimated heart motions were used to transform the CT. The PET frames were then attenuation-corrected with the transformed CT, reconstructed, aligned and summed, to produce motion-free images. It was found that motion-blurring was reduced through alignment, although benets were marginal in the presence of small respiratory motions. Quantitative accuracy was improved from use of the transformed CT for attenuation-correction (compared with no CT transformation), which was attributed to both the heart and the diaphragm transformations. In comparison to a gated CT, a substantial dose saving and a reduced dependence on gating techniques were achieved, indicating the potential value of the technique in routine clinical procedures

Signal analysis tool to investigate walking abnormalities

Abstract. This thesis presents a signal analysis tool, which has been designed to investigate walking abnormalities which are related to foot rolling movements during walking; interaction of foot with ground which is called stance phase. They would cause a wide range of severe anatomical damages such as ankle, leg, heel and back pain in the long-term. Comparing to the conventional data acquisition setups of biomechanical researches, inertial measurement sensors (IMU), which are being used widely as an appropriate alternative setup recently, facilitate monitoring human movement for a long-term period out of laboratory. This justifies the growing trend of improving the IMU-based algorithms which are designed for events detection, position calculation, and rotation estimation. Therefore, a set of 4 IMUs, placed on shank and foot of both legs, has been used for data collection. In data processing stage, two novel algorithms have been developed and implemented as the backbone of the designed software aiming to detect and integrate stance phases. The first algorithm was developed to detect stance phases in gait cycle data. Even though the detection of events in gait cycles has been the topic of a majority of biomechanical researches, stance phase as the interval between two consecutive events has not been studied sufficiently. The second algorithm, sensor alignment, generates a rotation matrix which is used to align IMU sensors placed on the same foot and shank. This alignment of the two sensors enables us to add or subtract the data point-wisely to make a more meaningful interpretation of the data regarding thought-out walking abnormalities during phase stances. The visualized results of the thesis can be considered as an early stage of a more comprehensive research which might lead to quantitative results corresponding to different walking abnormalities