Maximum likelihood (ML) techniques are defined for optimum block matching to enable motion estimation in sequences of ultrasound B-mode images. Such motion estimation is needed as a diagnostic tool in medical use of ultrasound imagery. It is also needed for the efficient compression of sequences of ultrasound images. The novel ML block matching techniques correspond to accurate statistical descriptions of ultrasound images, and are evaluated experimentally using sequences of transesophageal ultrasound images of the heart.
A method is described for the determination of an immobile region of an ultrasound image of the heart. Following a noise reduction operation wing an optimal signal-adaptive algorithm, a luminancebased block matching technique is used to determine the motion field. Subsequently, the motion field map is proceqsed and the immobile region is extracted b y using a 2 -0 texture separating algorithm.
Preterm birth (PTB) is defined as delivery occurring before 37 weeks of gestation. In this paper, Artificial Intelligence (AI)-based predictive models are adapted to accurately estimate the probability of PTB. In doing so, pregnant women’ objective results and variables extracted from the screening procedure in combination with demographics, medical history, social history, and other medical data are used. A dataset consisting of 375 pregnant women is used and a number of alternative Machine Learning (ML) algorithms are applied to predict PTB. The ensemble voting model produced the best results across all performance metrics with an area under the curve (ROC-AUC) of approximately 0.84 and a precision–recall curve (PR-AUC) of approximately 0.73. An attempt to provide clinicians with an explanation of the prediction is performed to increase trustworthiness.
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