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INTRODUCTION: Cardiology has been one of the most important areas of medicine. For several applications to diagnose the heart functions diseases the measurement of left ventricular (LV) cavity area and LV fractional area change are of vital necessity. To achieve this task, it is necessary to trace the border of left ventricle, which manual tracing is a tedious and time-consuming work. To solve this problem, many techniques to automate this border detection have been developed using the specialist tracing as gold standard. METHODS: The purpose of this approach is to analyze the features of the main techniques applied to left ventricle border detection in medical imaging. To facilitate understanding, the left ventricle border detection techniques are divided into three categories: image-based techniques, model-based techniques and pixel-based techniques. For each of the category, a literature review was made to get examples of the techniques applied to left ventricle border detection and to describe them. The result of this review is a comparative tablewhere the main features of each technique is compared. CONCLUSION: From the comparative table we can conclude that the not mentioning of many features of the techniques by the authors and the lack of a standardization of the evaluation techniques hamper a more satisfactory comparison.

The electrocardiogram (ECG) is the recording of the heart's electrical activity from the surface of the body and has been worldwide used in routine medical practice since the last century. This is a first-rate tool for a rapid interpretation of cardiac rhythm and for assessment and immediate therapeutic management in patients with suspected acute coronary syndrome, which is a clinical entity that includes unstable angina and acute myocardial infarction with and without ST-segment elevation. Currently, electrocardiographs from various manufacturers are capable of providing automatic diagnosis of ECGs, but there are few published studies about the performance of these equipments. The objective of this paper is to evaluate the performance of an interpretive electrocardiograph in order to determine their suitability for use in a computer system that uses the medical history information obtained by the general practitioner and the electrocardiographic report emitted by the equipment evaluated to aid the diagnosis of acute coronary syndromes, when the patient is not treated by cardiologists. We used an interpretive electrocardigraph Nihon Kohden Cardiofax 9620. The electrocardiograph simulator Metron PS 420 was the choice for the simulated tests, and also we compared the performance of the Cardiofax with a telemedicine system available in a semi-rural area in Brazil. The tests resulted in 93.8% sensitivity and 83.3% specificity that were considered satisfactory in clinical situations compatible with acute myocardial ischemia, validating their use in an artificial intelligence system for the diagnosis of ACS.

Being non-invasive and low cost, the echocardiography has become a diagnostic technique largely applied for the determination of the left ventricle systolic and diastolic volumes, which are used indirectly to calculate the left ventricle ejection volume, the cardiac cavities muscular contraction, the regional ejection fraction, the myocardial thickness, and the ventricular mass, etc. However, the image is very noisy, which renders the delineation of the borders of the left ventricle very difficult. While there are many techniques image segmentation, this work chooses the artificial neural network (ANN) since it is not very sensitive to noise. In order to reduce the processing time, the operator selects the region of interest where the neural network will identify the borders. Neighborhood and gradient search techniques are then employed to link the points and the left ventricle contour is traced. The present method has been efficient in detecting the left ventricle borders echocardiography images compared to those whose borders were delineated by the specialists. For good results, it is important to choose properly the areas to be analyzed and the central points of these areas.