Literature DB >> 20033579

Integration of computer-aided diagnosis/detection (CAD) results in a PACS environment using CAD-PACS toolkit and DICOM SR.

Anh H T Le1, Brent Liu, H K Huang.   

Abstract

PURPOSE: Picture Archiving and Communication System (PACS) is a mature technology in health care delivery for daily clinical imaging service and data management. Computer-aided detection and diagnosis (CAD) utilizes computer methods to obtain quantitative measurements from medical images and clinical information to assist clinicians to assess a patient's clinical state more objectively. CAD needs image input and related information from PACS to improve its accuracy; and PACS benefits from CAD results online and available at the PACS workstation as a second reader to assist physicians in the decision making process. Currently, these two technologies remain as two separate independent systems with only minimal system integration. This paper describes a universal method to integrate CAD results with PACS in its daily clinical environment.
METHODS: The method is based on Health Level 7 (HL7) and Digital imaging and communications in medicine (DICOM) standards, and Integrating the Healthcare Enterprise (IHE) workflow profiles. In addition, the integration method is Health Insurance Portability and Accountability Act (HIPAA) compliant.
SUMMARY: The paper presents (1) the clinical value and advantages of integrating CAD results in a PACS environment, (2) DICOM Structured Reporting formats and some important IHE workflow profiles utilized in the system integration, (3) the methodology using the CAD-PACS integration toolkit, and (4) clinical examples with step-by-step workflows of this integration.

Entities:  

Mesh:

Year:  2009        PMID: 20033579     DOI: 10.1007/s11548-009-0297-y

Source DB:  PubMed          Journal:  Int J Comput Assist Radiol Surg        ISSN: 1861-6410            Impact factor:   2.924


  2 in total

1.  CAD-PACS integration tool kit based on DICOM secondary capture, structured report and IHE workflow profiles.

Authors:  Zheng Zhou; Brent J Liu; Anh H Le
Journal:  Comput Med Imaging Graph       Date:  2007-03-26       Impact factor: 4.790

Review 2.  Computer-aided diagnosis in medical imaging: historical review, current status and future potential.

Authors:  Kunio Doi
Journal:  Comput Med Imaging Graph       Date:  2007-03-08       Impact factor: 4.790

  2 in total
  10 in total

1.  Impact of a computer-aided detection (CAD) system integrated into a picture archiving and communication system (PACS) on reader sensitivity and efficiency for the detection of lung nodules in thoracic CT exams.

Authors:  Luca Bogoni; Jane P Ko; Jeffrey Alpert; Vikram Anand; John Fantauzzi; Charles H Florin; Chi Wan Koo; Derek Mason; William Rom; Maria Shiau; Marcos Salganicoff; David P Naidich
Journal:  J Digit Imaging       Date:  2012-12       Impact factor: 4.056

Review 2.  Medical imaging informatics simulators: a tutorial.

Authors:  H K Huang; Ruchi Deshpande; Jorge Documet; Anh H Le; Jasper Lee; Kevin Ma; Brent J Liu
Journal:  Int J Comput Assist Radiol Surg       Date:  2013-09-14       Impact factor: 2.924

3.  Bridging the integration gap between imaging and information systems: a uniform data concept for content-based image retrieval in computer-aided diagnosis.

Authors:  Petra Welter; Jörg Riesmeier; Benedikt Fischer; Christoph Grouls; Christiane Kuhl; Thomas M Deserno
Journal:  J Am Med Inform Assoc       Date:  2011 Jul-Aug       Impact factor: 4.497

4.  Integrating CAD modules in a PACS environment using a wide computing infrastructure.

Authors:  Jorge J Suárez-Cuenca; Amara Tilve; Ricardo López; Gonzalo Ferro; Javier Quiles; Miguel Souto
Journal:  Int J Comput Assist Radiol Surg       Date:  2017-02-10       Impact factor: 2.924

5.  Automated Anatomic Labeling Architecture for Content Discovery in Medical Imaging Repositories.

Authors:  Eduardo Pinho; Carlos Costa
Journal:  J Med Syst       Date:  2018-06-29       Impact factor: 4.460

Review 6.  Machine Learning Techniques for Personalised Medicine Approaches in Immune-Mediated Chronic Inflammatory Diseases: Applications and Challenges.

Authors:  Junjie Peng; Elizabeth C Jury; Pierre Dönnes; Coziana Ciurtin
Journal:  Front Pharmacol       Date:  2021-09-30       Impact factor: 5.810

Review 7.  Clinical Implementation of Deep Learning in Thoracic Radiology: Potential Applications and Challenges.

Authors:  Eui Jin Hwang; Chang Min Park
Journal:  Korean J Radiol       Date:  2020-05       Impact factor: 3.500

Review 8.  An engineering view on megatrends in radiology: digitization to quantitative tools of medicine.

Authors:  Namkug Kim; Jaesoon Choi; Jaeyoun Yi; Seungwook Choi; Seyoun Park; Yongjun Chang; Joon Beom Seo
Journal:  Korean J Radiol       Date:  2013-02-22       Impact factor: 3.500

Review 9.  Deep Learning in Medical Imaging: General Overview.

Authors:  June-Goo Lee; Sanghoon Jun; Young-Won Cho; Hyunna Lee; Guk Bae Kim; Joon Beom Seo; Namkug Kim
Journal:  Korean J Radiol       Date:  2017-05-19       Impact factor: 3.500

10.  Integrating AI into radiology workflow: levels of research, production, and feedback maturity.

Authors:  Engin Dikici; Matthew Bigelow; Luciano M Prevedello; Richard D White; Barbaros S Erdal
Journal:  J Med Imaging (Bellingham)       Date:  2020-02-11
  10 in total

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