Abstract/Details

Automated triage of acute stroke cases: Integrating imaging and clinical information to prioritize clinical interpretation


2007 2007

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Abstract (summary)

Stroke is the third leading cause of death in the United States. The timely detection and treatment of stroke is important in reducing disability and death. The diagnosis of acute stroke in the clinical setting involves an initial neurological examination. However, the clinical presentation of other neurological disorders may mimic stroke, making it difficult to establish a diagnosis of stroke based on clinical findings alone. Neuroimaging has been identified as a key component to the initial evaluation of acute stroke since it can confirm the presence or absence of acute stroke. The inclusion of neuroimaging in the initial evaluation requires the radiologist to play a prominent role in the triaging of stroke patients.

In order for radiologists to provide a final clinical diagnosis of acute stroke, several different sets of images and clinical information must be reviewed and analyzed. With advances in imaging modalities, sequences, and technology, the number of images available for triaging stroke patients has increased tremendously. The radiologist is confronted with the challenge of organizing and analyzing a large volume of images in order to provide timely diagnosis. Current image storage and management systems such as Picture Archival and Communications Systems (PACS) generally provide radiologists with a first in, first out (FIFO) or a last in, first out (LIFO) method for organizing imaging cases rather than ranking images based on urgency. This requires the radiologist to manually review all image datasets acquired, including non-stroke cases, in order to detect an acute stroke. This can potentially increase the time between image acquisition and the final stroke diagnosis, which can lead to a delay in the treatment and management of stroke patients.

The objective of this thesis is to develop automated methods that can be integrated into a fully automated platform for the triaging of radiological images. Since radiologists utilize images and clinical information in diagnosing acute infarcts, a set of automated techniques using pattern recognition, natural language processing, and machine learning is implemented and applied to both imaging and clinical information.

The results from these automated techniques indicate that triaging acute infarct cases based on combined imaging and clinical data outperforms triaging based on clinical data alone, imaging data alone or random triaging. The automated methods developed in this thesis can assist the radiologist in triaging a large number of cases and can potentially reduce the time between image acquisition, interpretation, and final diagnosis.

Indexing (details)


Subject
Bioinformatics
Classification
0715: Bioinformatics
Identifier / keyword
Biological sciences; Automated triage; Clinical information; Clinical interpretation; Computer-aided detection; Natural language processing; Stroke; Triage
Title
Automated triage of acute stroke cases: Integrating imaging and clinical information to prioritize clinical interpretation
Author
Tulipano, Paola Karina
Number of pages
167
Publication year
2007
Degree date
2007
School code
0054
Source
DAI-B 68/09, Dissertation Abstracts International
Place of publication
Ann Arbor
Country of publication
United States
ISBN
9780549271628
Advisor
Friedman, Carol
University/institution
Columbia University
University location
United States -- New York
Degree
Ph.D.
Source type
Dissertations & Theses
Language
English
Document type
Dissertation/Thesis
Dissertation/thesis number
3285189
ProQuest document ID
304863322
Copyright
Database copyright ProQuest LLC; ProQuest does not claim copyright in the individual underlying works.
Document URL
http://search.proquest.com/docview/304863322
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