# Evidence-Based Investigation of Coronary Calcium Score in Cardiac Computed Tomography
## 4 Discussion  
With recent advances in deep learning technology, numerous studies have reported improvements in automated calcium scoring [30; 31; 32]. Deep learning technology uses the traditional threshold of 130 HU, above which a region is considered to contain calcium. However, the potential for noise risks introducing errors into automated calcium scoring, necessitating the accurate identification of the causes of noise in CT images. The results of the present study suggest that an increase in the BMI of a patient leads to greater effective thickness, subsequently elevating noise levels in CT images and influencing calcium scores. Several previous studies have also reported an association between BMI and noise [33; 34; 35]. Based on these previous studies, we excluded other variables affecting noise by extracting the noise from images of overweight and obese patients and inserting it into the images of normal weight patients to generate hypothetical overweight and obese patient images. This allowed us to compare the calcium scores of hypothetical overweight and obese patients to those of normal weight patients, with the increased noise caused only by increased BMI, and other factors kept constant. It was thus confirmed that noise increased along with BMI, which resulted in an increase in calcium scores. The findings of this study indicate that the impact of noise can be minimized by utilizing a training model based on BMI when implementing automated measurements. Furthermore, our phantom study allowed us to manipulate effective thickness, tube voltage, and slice thickness, which all impact noise, to observe the corresponding changes in calcium scores. An increase in tube voltage permits the transmission of a greater number of X-rays to the detector when examining a range of effective thicknesses with a constant mAs. This may also enhance the image quality. Furthermore, a reduction in slice thickness increases image noise. Currently, there are fewer advantages to using thin slices because the increase in noise can impact the accuracy of manual diagnoses, however, it is advantageous to use thin slices that  
Figure 8: (**a**) Total calcium scores of calcium phantoms with and without a bolus at slice thicknesses of 3 and 1 mm. (**b**) Total calcium scores of the calcium phantoms C4, C5, and C6 with and without a bolus at slice thicknesses of 3 and 1 mm. Figure 7: (**a**) Total calcium scores of calcium phantoms with and without a bolus at tube voltages of 100 and 120 kV. (**b**) Total calcium scores of the calcium phantoms C4, C5, and C6 with and without a bolus at tube voltages of 100 and 120 kV. provide more anatomical information when using automated measurement. We therefore investigated the effect of CT settings on noise levels for calcium scoring. The calcium scores for hypothetical overweight and obese patients were increased compared to normal weight patients, with a more notable rise observed in hypothetical obese patients. Additionally, diagnostic performance was poorer in hypothetical obese patients than in hypothetical overweight patients, with the reduced specificity in hypothetical obese patients suggesting that greater effective thickness results in images with more noise, affecting calcium scores. ICC and kappa values indicated very high agreement among the three radiologists, and the increase in calcium scores was consistent across all radiologists. Previous studies have measured the calcium score when the patient was irradiated according to the guidelines and without considering BMI, and compared it to the calcium score of the low-dose image [36; 37]. However, the results of this study showed that an increase in effective thickness caused by an increase in BMI can increase the uncertainty of the calcium score; the higher the BMI, the greater the uncertainty. Therefore, efforts are needed to reduce this error in the calcium score. The Agatston calcium score risk level was increased in the hypothetical obese group compared with the hypothetical overweight group.