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MEDICAL IMAGING SYSTEMS FOR REDUCING RADIATION EXPOSURE

发明专利审中
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20权利要求 · 3 独立
§ Ⅰ

卷宗概要

发明人

Phillip R. Kingma; Letrisha Weber

IPC 分类

A61B 6/G6T 7/G16H 10/60G16H 40/20G16H 40/40G16H 40/67

CPC 分类

A61B6/544A61B6/545G6T7/12G16H40/20G16H40/40G16H40/67G6T2200/24G6T2207/10121G6T2207/20081G6T2207/20084G6T2207/30168G16H10/60

Methods, systems, and apparatuses are described herein for using processes and machine learning techniques to optimize medical imaging processes to reduce inadvertent exposure to harmful radiation. A machine learning model may be trained to output recommended medical imaging device operating parameter settings. Available operating parameters of a medical imaging device may be determined, and patient data may be received. The patient data and the available operating parameters may be used as input to the trained machine learning model, which might output recommended operating parameter settings. In turn, this output in addition to other calculations might be used to transmit, to the medical imaging device, data that causes modification of the operating parameters of the medical imaging device. Metadata corresponding to one or more images captured by the medical imaging device may be received, and the trained machine learning model might be further trained based on that metadata.

原文(中文)

Methods, systems, and apparatuses are described herein for using processes and machine learning techniques to optimize medical imaging processes to reduce inadvertent exposure to harmful radiation. A machine learning model may be trained to output recommended medical imaging device operating parameter settings. Available operating parameters of a medical imaging device may be determined, and patient data may be received. The patient data and the available operating parameters may be used as input to the trained machine learning model, which might output recommended operating parameter settings. In turn, this output in addition to other calculations might be used to transmit, to the medical imaging device, data that causes modification of the operating parameters of the medical imaging device. Metadata corresponding to one or more images captured by the medical imaging device may be received, and the trained machine learning model might be further trained based on that metadata.