1,520 research outputs found
Stochastic Rounding for Image Interpolation and Scan Conversion
The stochastic rounding (SR) function is proposed to evaluate and demonstrate
the effects of stochastically rounding row and column subscripts in image
interpolation and scan conversion. The proposed SR function is based on a
pseudorandom number, enabling the pseudorandom rounding up or down any
non-integer row and column subscripts. Also, the SR function exceptionally
enables rounding up any possible cases of subscript inputs that are inferior to
a pseudorandom number. The algorithm of interest is the nearest-neighbor
interpolation (NNI) which is traditionally based on the deterministic rounding
(DR) function. Experimental simulation results are provided to demonstrate the
performance of NNI-SR and NNI-DR algorithms before and after applying smoothing
and sharpening filters of interest. Additional results are also provided to
demonstrate the performance of NNI-SR and NNI-DR interpolated scan conversion
algorithms in cardiac ultrasound videos.Comment: 10 pages, 17 figures, 3 tables. International Journal of Advanced
Computer Science and Applications, 202
Performance of the American Heart Association/American College of Cardiology Guideline-Recommended Pretest Probability Model for the Diagnosis of Obstructive Coronary Artery Disease
BACKGROUND: Substantial differences exist between different guidelineârecommended pretest probability (PTP) models for the detection of obstructive coronary artery disease (CAD). This study was performed to study the performance of the 2021 American Heart Association/American College of Cardiology (AHA/ACC) guidelineârecommended PTP (AHA/ACCâPTP) model in assessing the likelihood of obstructive CAD compared with previously proposed models. METHODS AND RESULTS: Symptomatic patients (N=50â561) referred for coronary computed tomography angiography were included. The reference standard was invasive coronary angiography with optional fractional flow reserve measurements. The AHA/ACCâPTP values based on sex and age were calculated and compared with the 2019 European Society of Cardiology guideline PTP values based on sex, age, and symptoms as well as the risk factorâweighted clinical likelihood values based on sex, age, symptoms, and risk factors. The AHA/ACCâPTP maximum values overestimated by a factor of 2.6 the actual prevalence of CAD. Compared with the AHA/ACCâPTP model (area under the receiverâoperating curve, 71.5 [95% CI, 70.7â72.2]), inclusion of typicality of symptoms in the European Society of Cardiology guideline PTP improved discrimination of CAD (area under the receiverâoperating curve, 75.5 [95% CI, 74.7â76.3]). Inclusion of both symptoms and risk factors in the risk factorâweighted clinical likelihood model further improved discrimination (area under the receiverâoperating curve, 77.7 [95% CI, 77.0â78.5]). The proportion of patients classified as very low PTP was lower using the AHA/ACCâPTP (5%) compared with the European Society of Cardiology guideline PTP (19%) and the risk factorâweighted clinical likelihood (49%) models. CONCLUSIONS: The new AHA/ACCâPTP model overestimates the prevalence of obstructive CAD substantially if type of symptoms and risk factors are not taken into account. Inclusion of both symptoms and risk factors improves model performance and identifies more patients with very low likelihood of CAD in whom further testing can be deferred
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