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Proceedings Paper

Medical sieve: a cognitive assistant for radiologists and cardiologists
Author(s): T. Syeda-Mahmood; E. Walach; D. Beymer; F. Gilboa-Solomon; M. Moradi; P. Kisilev; D. Kakrania; C. Compas; H. Wang; R. Negahdar; Y. Cao; T. Baldwin; Y. Guo; Y. Gur; D. Rajan; A. Zlotnick; S. Rabinovici-Cohen; R. Ben-Ari; Amit Guy; P. Prasanna; J. Morey; O. Boyko; S. Hashoul
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Paper Abstract

Radiologists and cardiologists today have to view large amounts of imaging data relatively quickly leading to eye fatigue. Further, they have only limited access to clinical information relying mostly on their visual interpretation of imaging studies for their diagnostic decisions. In this paper, we present Medical Sieve, an automated cognitive assistant for radiologists and cardiologists designed to help in their clinical decision-making. The sieve is a clinical informatics system that collects clinical, textual and imaging data of patients from electronic health records systems. It then analyzes multimodal content to detect anomalies if any, and summarizes the patient record collecting all relevant information pertinent to a chief complaint. The results of anomaly detection are then fed into a reasoning engine which uses evidence from both patient-independent clinical knowledge and large-scale patient-driven similar patient statistics to arrive at potential differential diagnosis to help in clinical decision making. In compactly summarizing all relevant information to the clinician per chief complaint, the system still retains links to the raw data for detailed review providing holistic summaries of patient conditions. Results of clinical studies in the domains of cardiology and breast radiology have already shown the promise of the system in differential diagnosis and imaging studies summarization.

Paper Details

Date Published: 24 March 2016
PDF: 6 pages
Proc. SPIE 9785, Medical Imaging 2016: Computer-Aided Diagnosis, 97850A (24 March 2016); doi: 10.1117/12.2217382
Show Author Affiliations
T. Syeda-Mahmood, IBM Research - Almaden (United States)
E. Walach, IBM Research - Haifa (Israel)
D. Beymer, IBM Research - Almaden (United States)
F. Gilboa-Solomon, IBM Research - Haifa (Israel)
M. Moradi, IBM Research - Almaden (United States)
P. Kisilev, IBM Research - Haifa (Israel)
D. Kakrania, IBM Research - Almaden (United States)
C. Compas, IBM Research - Almaden (United States)
H. Wang, IBM Research - Almaden (United States)
R. Negahdar, IBM Research - Almaden (United States)
Y. Cao, IBM Research - Almaden (United States)
T. Baldwin, IBM Research - Almaden (United States)
Y. Guo, IBM Research - Almaden (United States)
Y. Gur, IBM Research - Almaden (United States)
D. Rajan, IBM Research - Almaden (United States)
A. Zlotnick, IBM Research - Haifa (Israel)
S. Rabinovici-Cohen, IBM Research - Haifa (Israel)
R. Ben-Ari, IBM Research - Haifa (Israel)
Amit Guy, IBM Research - Haifa (Israel)
P. Prasanna, IBM Research - Almaden (United States)
J. Morey, IBM Research - Almaden (United States)
O. Boyko, IBM Research - Almaden (United States)
S. Hashoul, IBM Research - Haifa (Israel)

Published in SPIE Proceedings Vol. 9785:
Medical Imaging 2016: Computer-Aided Diagnosis
Georgia D. Tourassi; Samuel G. Armato III, Editor(s)

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