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 guideline‐recommended 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) guideline‐recommended PTP (AHA/ACC‐PTP) model in assessing the likelihood of obstructive CAD compared with previously proposed models. METHODS AND RESULTS: Symptomatic patients (N=50 561) referred for coronary computed tomography angiography were included. The reference standard was invasive coronary angiography with optional fractional flow reserve measurements. The AHA/ACC‐PTP 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 factor–weighted clinical likelihood values based on sex, age, symptoms, and risk factors. The AHA/ACC‐PTP maximum values overestimated by a factor of 2.6 the actual prevalence of CAD. Compared with the AHA/ACC‐PTP model (area under the receiver‐operating curve, 71.5 [95% CI, 70.7–72.2]), inclusion of typicality of symptoms in the European Society of Cardiology guideline PTP improved discrimination of CAD (area under the receiver‐operating curve, 75.5 [95% CI, 74.7–76.3]). Inclusion of both symptoms and risk factors in the risk factor–weighted clinical likelihood model further improved discrimination (area under the receiver‐operating curve, 77.7 [95% CI, 77.0–78.5]). The proportion of patients classified as very low PTP was lower using the AHA/ACC‐PTP (5%) compared with the European Society of Cardiology guideline PTP (19%) and the risk factor–weighted clinical likelihood (49%) models. CONCLUSIONS: The new AHA/ACC‐PTP 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