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Echocardiography frequently serves as the first-line treatment of diagnostic imaging for several pathological entities in cardiology. Artificial intelligence (AI) has been growing substantially in information technology and various commercial industries. Machine learning (ML), a branch of AI, has been shown to expand the capabilities and potential of echocardiography. ML algorithms expand the field of echocardiography by automated assessment of the ejection fraction and left ventricular function, integrating novel approaches such as speckle tracking or tissue Doppler echocardiography or vector flow mapping, improved phenotyping, distinguishing between cardiac conditions, and incorporating information from mobile health and genomics. In this review article, we assess the impact of AI and ML in echocardiography.
Echocardiography is the most common test in cardiovascular imaging and helps diagnose multiple different diseases. Machine learning, a branch of artificial intelligence (AI), will reduce the workload for medical professionals and help improve clinical workflows. It can rapidly calculate a lot of important cardiac parameters such as the ejection fraction or important metrics during different phases of the cardiac cycle. Machine learning algorithms can include new technology in echocardiography such as speckle tracking, tissue Doppler echocardiography, vector flow mapping, and other approaches in a user-friendly manner. Furthermore, it can help find new subtypes of existing diseases in cardiology. In this review article, we look at the current role of machine learning and AI in the field of echocardiography.
RESUMO
In this digital era, artificial intelligence (AI) is establishing a strong foothold in commercial industry and the field of technology. These effects are trickling into the healthcare industry, especially in the clinical arena of cardiology. Machine learning (ML) algorithms are making substantial progress in various subspecialties of cardiology. This will have a positive impact on patient care and move the field towards precision medicine. In this review article, we explore the progress of ML in cardiovascular imaging, electrophysiology, heart failure, and interventional cardiology.
RESUMO
Computed tomography (CT) is emerging as a prominent diagnostic modality in the field of cardiovascular imaging. Artificial intelligence (AI) is making significant strides in the field of information technology, the commercial industry, and health care. Machine learning (ML), a branch of AI, can optimize the performance of CT and augment the assessment of coronary artery disease. These ML platforms can automate multiple tasks, perform calculations, and integrate information from a variety of data sources. In this review article, we explore the ML in CT imaging.
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Emphysematous aortitis is a rare but lethal form of infectious vasculitis. This condition was found incidentally on computed tomography of the chest during the evaluation of a patient presenting with pneumonia coincident with adynamic ileus. The patient did not have a history of malignancy. While colon cancer could not be ruled out, it is possible that ileus may have contributed to or resulted in bacterial translocation in this case. Appropriate investigations and empirical therapy against Clostridium septicum should be initiated in the presence of clinical and radiological findings suggestive of emphysematous aortitis.
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We describe 2 cases in which the prolonged use of linezolid to treat complicated methicillin-resistant Staphylococcus aureus infections was followed by acutely developed blurred vision and progressive loss of vision and color perception during the ensuing few weeks. Both patients received a diagnosis of toxic optic neuropathy, and linezolid therapy was stopped. The patients experienced an initial rapid partial improvement and a subsequent gradual, almost complete, recovery over many months.