A new method for the automatic retrieval of medical cases based on the RadLex ontology

Autor: Leo Joskowicz, Assaf B. Spanier, D. Cohen
Rok vydání: 2016
Předmět:
Databases
Factual
Computer science
Clinical Decision-Making
Biomedical Engineering
Information Storage and Retrieval
Health Informatics
02 engineering and technology
Ontology (information science)
Health informatics
030218 nuclear medicine & medical imaging
Automation
03 medical and health sciences
Imaging
Three-Dimensional
0302 clinical medicine
0202 electrical engineering
electronic engineering
information engineering
Humans
Radiology
Nuclear Medicine and imaging
Image retrieval
Information retrieval
business.industry
General Medicine
Hybrid approach
Computer Graphics and Computer-Aided Design
Computer Science Applications
Conjunction (grammar)
Tree (data structure)
Radiology Information Systems
Metric (mathematics)
Graph (abstract data type)
020201 artificial intelligence & image processing
Surgery
Computer Vision and Pattern Recognition
Radiology
Tomography
X-Ray Computed
business
Algorithms
Zdroj: International Journal of Computer Assisted Radiology and Surgery. 12:471-484
ISSN: 1861-6429
1861-6410
DOI: 10.1007/s11548-016-1496-y
Popis: The goal of medical case-based image retrieval (M-CBIR) is to assist radiologists in the clinical decision-making process by finding medical cases in large archives that most resemble a given case. Cases are described by radiology reports comprised of radiological images and textual information on the anatomy and pathology findings. The textual information, when available in standardized terminology, e.g., the RadLex ontology, and used in conjunction with the radiological images, provides a substantial advantage for M-CBIR systems. We present a new method for incorporating textual radiological findings from medical case reports in M-CBIR. The input is a database of medical cases, a query case, and the number of desired relevant cases. The output is an ordered list of the most relevant cases in the database. The method is based on a new case formulation, the Augmented RadLex Graph and an Anatomy–Pathology List. It uses a new case relatedness metric $${\textit{relCase}}$$ that prioritizes more specific medical terms in the RadLex tree over less specific ones and that incorporates the length of the query case. An experimental study on 8 CT queries from the 2015 VISCERAL 3D Case Retrieval Challenge database consisting of 1497 volumetric CT scans shows that our method has accuracy rates of 82 and 70% on the first 10 and 30 most relevant cases, respectively, thereby outperforming six other methods. The increasing amount of medical imaging data acquired in clinical practice constitutes a vast database of untapped diagnostically relevant information. This paper presents a new hybrid approach to retrieving the most relevant medical cases based on textual and image information.
Databáze: OpenAIRE
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