Using Machine Learning to Identify Intravenous Contrast Phases on Computed Tomography. (March 2022)

• Record Type: Journal Article
• Authors: Muhamedrahimov, Raouf; Bar, Amir; Laserson, Jonathan; Akselrod-Ballin, Ayelet; Elnekave, Eldad
• Is Part Of: Computer methods and programs in biomedicine. Volume 215(2022)
• Publication Date: 2022-03
• Subjects: Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2021.106603
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: It is possible to train a fully automatic system for contrast enhancement phase identification on abdomen CT using solely weak labels extracted from DICOM metadata Using a Zero-Shot Learning approach, it is possible to further classify contrast enhancement phases by modeling the delay in seconds following I.V contrast administration Utilizing a transfer learning method, it is possible to train a contrast enhancement classification system for chest CT using little or no labelled data Abstract: Purpose: The purpose of the present work is to demonstrate the application of machine learning (ML) techniques to automatically identify the presence and physiologic phase of intravenous (IV) contrast in Computed Tomography (CT) scans of the Chest, Abdomen and Pelvis. Materials and Methods: Training, testing and validation data were acquired from a dataset of 82, 690 chest and abdomen CT examinations performed at 17 different institutions. Free text in DICOM metadata was utilized as weak labels for semi-supervised classification training. Contrast phase identification was approached as a classification task, using a 12-layer CNN and ResNet18 with four contrast-phase output. The model was reformulated to fit a regression task aimed to predict actual seconds from time of IV contrast administration to series image acquisition. Finally, transfer learning was used to optimize the model to predict contrast presence on CT Chest. Results: By training based on labels inferred from noisy, free text DICOM information, contrast phase was predicted with 93.3% test accuracy (95% CI: 89.3%, 96.6%) . Regression analysis resulted in delineation of early vs late arterial phases and a nephrogenic phase in between the portal venous and delayed excretory phase. Transfer learning applied to Chest CT achieved an AUROC of 0.776 (95% CI: 0.721, 0.832) directly using the model trained for abdomen CT and 0.999 (95% CI: 0.998, 1.000) by fine-tuning. Conclusions: The presence and phase of contrast on CT examinations of the Abdomen-pelvis accurately and automatically be ascertained by a machine learning algorithm. Transfer learning applied to CT Chest achieves high precision with as little as 100 labeled samples.