Can Machine Learning Identify the Intravenous Contrast Dose and Injection Rate Needed for Optimal Enhancement on Dynamic Liver Computed Tomography?

Objectives: This study aimed to investigate whether machine learning (ML) is useful for predicting the contrast material (CM) dose required to obtain a clinically optimal contrast enhancement in hepatic dynamic computed tomography (CT).
Methods: We trained and evaluated ensemble ML regressors to predict the CM doses needed for optimal enhancement in hepatic dynamic CT using 236 patients for a training data set and 94 patients for a test data set. After the ML training, we randomly divided using the ML-based (n = 100) and the body weight (BW)-based protocols (n = 100) by the prospective trial. The BW protocol was performed using routine protocol (600 mg/kg of iodine) by the prospective trial. The CT numbers of the abdominal aorta and hepatic parenchyma, CM dose, and injection rate were compared between each protocol using the paired t test. Equivalence tests were performed with equivalent margins of 100 and 20 Hounsfield units for the aorta and liver, respectively.
Results: The CM dose and injection rate for the ML and BW protocols were 112.3 mL and 3.7 mL/s, and 118.0 mL and 3.9 mL/s ( P < 0.05). There were no significant differences in the CT numbers of the abdominal aorta and hepatic parenchyma between the 2 protocols ( P = 0.20 and 0.45). The 95% confidence interval for the difference in the CT number of the abdominal aorta and hepatic parenchyma between 2 protocols was within the range of predetermined equivalence margins.
Conclusions: Machine learning is useful for predicting the CM dose and injection rate required to obtain the optimal clinical contrast enhancement for hepatic dynamic CT without reducing the CT number of the abdominal aorta and hepatic parenchyma.
Competing Interests: The authors declare no conflict of interest.
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