Heart volume on health checkup CT scans inversely correlates with pulse rate: data-driven analysis using
Kanato Masayoshi1, Masahiro Hashimoto2, Naoki Toda1
1Department of Radiology, Keio University School of Medicine, Tokyo, Japan.
Insights
Pulse rate is the strongest predictor of heart volume on CT scans, more so than blood pressure. This study highlights the effectiveness of AI in analyzing large CT datasets for health insights.
Area of Science:
- Medical Imaging
- Cardiology
- Artificial Intelligence
Background:
- Computed tomography (CT) enables detailed cardiac imaging.
- Analyzing large CT datasets for health correlations is challenging.
- Deep learning offers potential for automated image analysis.
Purpose of the Study:
- To correlate heart volume on CT with general health checkup data.
- To assess the utility of a deep-learning tool for CT data analysis.
- To identify key health indicators related to heart volume.
Main Methods:
- Retrospective analysis of CT images and health data (2013, 2018).
- Heart volume quantified using the TotalSegmentator deep-learning model.
- Statistical analysis included Spearman's correlation and ANOVA, adjusting for covariates.
Main Results:
- Pulse rate showed the strongest inverse correlation with heart volume.
- A 10 bpm increase in pulse rate correlated with a 0.5% decrease in cardiothoracic ratio.
- Hemoglobin, hematocrit, total protein, albumin, and cholinesterase also showed weak inverse correlations.
Conclusions:
- Pulse rate is a significant covariate of heart volume, outweighing other cardiovascular markers.
- AI-assisted, data-driven research on CT big data is feasible and effective.
- Findings support using AI for large-scale medical image analysis.
Purpose:
This study aims to elucidate correlation between heart volume on computed tomography (CT) and various health checkup examination data in the general population. Furthermore, this study aims to examine the utility of a deep-learning segmentation tool in the data-driven analysis of CT big data.
Materials And Methods:
Health checkup examination data and CT images acquired in 2013 and 2018 were retrospectively analyzed. We first quantified heart volume using a public deep-learning model, TotalSegmentator. The accuracy of segmentation was evaluated using Dice score on 30 randomly chosen images and annotation by a radiologist. Then, Spearman's partial correlation was calculated for 58 numerical items, and the analysis of covariance was performed for 13 categorical items, adjusting for the effect of gender, medication, height, weight, abdominal circumference, and age. The variables found to be significant proceeded to longitudinal analysis.
Results:
In the dataset, 7993 records were eligible for cross-sectional analysis and 1306 individuals were eligible for longitudinal analysis. Pulse rate was most strongly inversely correlated with the heart volume (Spearman's correlation coefficients ranging from - 0.29 to - 0.33). A 10 bpm increase in pulse rate was correlated with roughly a 0.5 percentage point decrease in the cardiothoracic ratio. Hemoglobin, hematocrit, total protein, albumin, and cholinesterase also showed weak inverse correlation. Five-year longitudinal analysis corroborated these findings.
Conclusions:
We found that pulse rate was the strongest covariate of the heart volume on CT, rather than other cardiovascular-related variables such as blood pressure. The study also demonstrated the feasibility and utility of the artificial intelligence-assisted data-driven research on CT big data.
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