Tuberculosis of the Heart: A Diagnostic Challenge.
Karuna M DasTaleb Al MansooriYousef Habeeb AlattarKlaus V GorkomAli ShamisiAnisha Pulinchani MelethilJamal Aldeen AlkoteeshPublished in: Tomography (Ann Arbor, Mich.) (2022)
Tuberculosis of the heart is relatively rare and presents a significant diagnostic difficulty for physicians. It is the leading cause of death from infectious illness. It is one of the top 10 leading causes of death worldwide, with a disproportionate impact in low- and middle-income nations. The radiologist plays a pivotal role as CMR is a non-invasive radiological method that can aid in identifying potential overlap and differential diagnosis between tuberculosis, mass lesions, pericarditis, and myocarditis. Regardless of similarities or overlap in observations, the combination of clinical and certain particular radiological features, which are also detected by comparison to earlier and follow-up CMR scans, may aid in the differential diagnosis. CMR offers a significant advantage over echocardiography for detecting, characterizing, and assessing cardiovascular abnormalities. In conjunction with clinical presentation, knowledge of LGE, feature tracking, and parametric imaging in CMR may help in the early detection of tuberculous myopericarditis and serve as a surrogate for endomyocardial biopsy resulting in a quicker diagnosis and therapy. This article aims to explain the current state of cardiac tuberculosis, the diagnostic utility of CMR in tuberculosis (TB) patients, and offer an overview of the various imaging and laboratory procedures used to detect cardiac tuberculosis.
Keyphrases
- mycobacterium tuberculosis
- pulmonary tuberculosis
- hiv aids
- high resolution
- left ventricular
- computed tomography
- healthcare
- primary care
- end stage renal disease
- heart failure
- ejection fraction
- adverse drug
- emergency department
- stem cells
- chronic kidney disease
- machine learning
- newly diagnosed
- atrial fibrillation
- magnetic resonance imaging
- mesenchymal stem cells
- deep learning
- risk assessment
- hepatitis c virus
- prognostic factors
- human immunodeficiency virus
- neural network
- patient reported