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020			_a9783030993917
024	7		_a10.1007/978-3-030-99391-7 _2doi
040			_aTR-AnTOB _beng _erda _cTR-AnTOB
041			_aeng
060			_aWG 141
072		7	_aMJ _2bicssc
072		7	_aMED010000 _2bisacsh
072		7	_aMJ _2thema
096			_aWG141EBK
245	1	0	_aHybrid Cardiac Imaging for Clinical Decision-Making _h[electronic resource] : _bFrom Diagnosis to Prognosis / _cedited by Francesco Nudi, Orazio Schillaci, Giuseppe Biondi-Zoccai, Ami E. Iskandrian.
250			_a1st ed. 2022.
264		1	_aCham : _bSpringer International Publishing : _bImprint: Springer, _c2022.
300			_a1 online resource
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
347			_atext file _bPDF _2rda
505	0		_aPART I) SPECIALISTS’ PERSPECTIVES TO HYBRID CARDIAC IMAGING -- Chapter 1) Hybrid Cardiac Imaging for the Clinical Cardiologist -- Chapter 2) Hybrid Cardiac Imaging for the Cardiologist with Expertise in Echocardiography -- Chapter 3) Hybrid Cardiac Imaging for the Specialist with Expertise in Cardiac Magnetic Resonance -- Chapter 4) Hybrid Imaging Using Single Photon Emission Computed Tomography -- Chapter 5) Hybrid Cardiac Imaging for the Specialist with Expertise in Computed Tomography -- Chapter 6) Hybrid Cardiac Imaging for the Invasive Cardiologist -- Chapter 7) Hybrid Cardiac Imaging for the Interventional Cardiologist -- PART II) HYBRID IMAGING IN CLINICAL PRACTICE -- Chapter 8) Systematic Review of Hybrid Cardiac Imaging -- Chapter 9) Hybrid Cardiac Viability Assessment -- Chapter 10) Hybrid Cardiac Imaging in Clinical Practice: From Diagnosis to Prognosis and Management -- Chapter 11) Clinical Cases of Hybrid Cardiac Imaging -- Chapter 12) Hybrid Cardiac Imaging: The Role of Machine Learning and Artificial Intelligence.
520			_aPerforming any diagnostic test in medicine is always a matter of trying to get the condition of the patient diagnosed properly with the least effort, exposure, discomfort and at the same time with the lowest possible error probability. Pre-test probability is helpful but often imprecise, effectively overestimating the patient's risk profile. In a broader prevention objective, the phases of a disease, its onset, progression, and complications must be taken into account. The negative predictive value, which is so important, has in turn its main limitation in identifying the healthy patient, that is, the one who does not belong to any cluster of patients in which we would act in terms of prevention. In coronary syndromes, the goal is instead to evaluate coronary heart disease, from mild to more extensive and significant forms. For this purpose, it is necessary to use parameters that investigate different and complementary aspects: stenosis, ischemia, the morphology of the atherosclerotic plaque, metabolic processes, in particular vitality and apoptosis, the presence of inflammatory processes. The possibility, already present thanks to Hybrid Imaging, of 'joining’ exams that study different aspects, will allow the patient to be increasingly characterized not only from a diagnostic point of view but also from a prognostic and personalized therapeutic choice.
650		0	_aInternal medicine.
650		0	_aRadiology.
650		0	_aMedical informatics.
650		0	_aUltrasonics.
650	1	4	_aInternal Medicine.
650	2	4	_aRadiology.
650	2	4	_aRadiology.
650	2	4	_aRadiology.
650	2	4	_aHealth Informatics.
650	2	4	_aUltrasonics.
653		0	_aCardiac Imaging Techniques
653		0	_aMultimodal Imaging
653		0	_aHeart Diseases -- diagnostic imaging
653		0	_aClinical Decision-Making -- methods
653		0	_aDiagnosis, Computer-Assisted
700	1		_aNudi, Francesco. _eeditor. _4edt _4http://id.loc.gov/vocabulary/relators/edt
700	1		_aSchillaci, Orazio. _eeditor. _4edt _4http://id.loc.gov/vocabulary/relators/edt
700	1		_aBiondi-Zoccai, Giuseppe. _eeditor. _4edt _4http://id.loc.gov/vocabulary/relators/edt
700	1		_aIskandrian, Ami E. _eeditor. _4edt _4http://id.loc.gov/vocabulary/relators/edt
710	2		_aSpringerLink (Online service)
856	4	0	_uhttps://doi.org/10.1007/978-3-030-99391-7 _3Springer eBooks _zOnline access link to the resource
942			_2NLM _cEBK