Feasibility Study of the Prediction of Radiologist’s Instructions with the Bi-LSTM Model Trained with Descriptions of MR Imaging Order-statement

• Published in: Japanese Journal of Radiological Technology Vol. 79; no. 6; pp. 565 - 576
• Main Authors: Shimizu, Kozo, Tachiiri, Tetsuya, Yamatani, Yuya, Mai, Yoshimasa, Marugami, Nagaaki
• Format: Journal Article
• Language: Japanese
• Published: Japan Japanese Society of Radiological Technology 20.06.2023; Japan Science and Technology Agency
• Subjects: classification; Feasibility studies; Language; machine learning; Magnetic resonance imaging; Medical imaging; MRI protocol; Natural language processing; Optimization; prediction; Predictions; Quality assurance; Recall; prediction; natural language processing; classification; machine learning; MRI protocol

Purpose: Magnetic resonance (MR) images provide essential diagnostic information; however, it is also a very burdensome examination for patients. At our hospital, radiologists make imaging instructions for all MR examination orders, but this is a time-consuming task. If a natural language processing...