title: A context-sensitive image annotation recommendation engine for radiology.
abstract: In the typical radiology reading workflow, a radiologist would go through an imaging study and annotate specific regions of interest. The radiologist has the option to select a suitable description (e.g., "calcification") from a list of predefined descriptions, or input the description directly as free-text. However, this process is time-consuming and the descriptions are not standardized over time, even for the same patient or the same general finding. In this paper, we describe an approach that presents finding descriptions based on textual information extracted from a patient's prior reports. Using 133 finding descriptions obtained in routine oncology workflow, we demonstrate how the system can be used to reduce keystrokes by up to 86% in about 38% of the instances. We have integrated our solution into a PACS and discuss how the system can be used in a clinical setting to improve the image annotation workflow efficiency and promote standardization of finding descriptions.
authors.affiliation: Philips Research North America.
authors.forename: Thusitha
authors.initials: T
authors.lastname: Mabotuwana
authors.affiliation: Philips Research North America.
authors.forename: Yuechen
authors.initials: Y
authors.lastname: Qian
authors.affiliation: Philips Research North America.
authors.forename: Merlijn
authors.initials: M
authors.lastname: Sevenster
mesh_terms: D001185:Artificial Intelligence; D004282:Documentation / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D009323:Natural Language Processing; D011873:Radiology Information Systems; D012984:Software; D014584:User-Computer Interface; D018875:Vocabulary, Controlled; D015443:Word Processing / Q000379:methods*; D014956:Writing