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

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International Journal of Computer Assisted Radiology and Surgery Aims and scope Submit manuscript

Abstract

Purpose

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.

Methods

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.

Results

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.

Conclusions

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.

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Acknowledgments

This research was partially funded by Grant 53681 from the Israeli Ministry of Science and Technology.

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Authors and Affiliations

  1. The Rachel and Selim Benin School of Computer Science and Engineering, The Hebrew University of Jerusalem, Givat Ram Campus, 91904, Jerusalem, Israel

    A. B. Spanier, D. Cohen & L. Joskowicz

  2. Alexander Grass Center for Bioengineering, The Hebrew University of Jerusalem, Jerusalem, Israel

    A. B. Spanier

Authors
  1. A. B. Spanier
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  2. D. Cohen
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  3. L. Joskowicz
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Corresponding author

Correspondence to A. B. Spanier.

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None of the authors has any conflict of interest. The authors have no personal financial or institutional interest in any of the materials, software or devices described in this article.

Protection of human and animal rights statement

No animals or humans were involved in this research. All scans were anonymized before delivery to the researchers.

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Spanier, A.B., Cohen, D. & Joskowicz, L. A new method for the automatic retrieval of medical cases based on the RadLex ontology. Int J CARS 12, 471–484 (2017). https://doi.org/10.1007/s11548-016-1496-y

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