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An intelligent decision support system for quantitative assessment of gastric atrophy
  1. Faruq A Al-Omari1,
  2. Ismail I Matalka2,
  3. Mohammad A Al-Jarrah1,
  4. Fatima N Obeidat3,
  5. Faisal M Kanaan2
  1. 1Computer Engineering Department, Hijjawi Faculty for Engineering Technology, Yarmouk University, Irbid, Jordan
  2. 2Department of Pathology and Laboratory Medicine, Jordan University of Science and Technology, Irbid, Jordan
  3. 3Department of Pathology and Laboratory Medicine, Jordan University Hospital, University of Jordan, Amman, Jordan
  1. Correspondence to Faruq A Al-Omari, Computer Engineering Department, Hijjawi Faculty for Engineering Technology, Yarmouk University, Irbid 21163, Jordan; fomari{at}yu.edu.jo

Abstract

Aims To build an automated decision support system to assist pathologists in grading gastric atrophy according to the updated Sydney system.

Methods A database of 143 biopsies was used to train and examine the proposed system. A panel of three experienced pathologists reached a consensus regarding the grading of the studied biopsies using the visual scale of the updated Sydney system. Digital imaging techniques were utilised to extract a set of discriminating morphological features that describe each atrophy grade sufficiently and uniquely. A probabilistic neural networks structure was used to build a grading system. To evaluate the performance of the proposed system, 66% of the biopsies (94 biopsy images) were used for training purposes and 34% (49 biopsy images) were used for testing and validation purposes.

Results During the training phase, a 98.9% precision was achieved, whereas during testing, a precision of 95.9% was achieved. The overall precision achieved was 97.9%.

Conclusions A fully automated decision support system to grade gastric atrophy according to the updated Sydney system is proposed. The system utilises advanced image processing techniques and probabilistic neural networks in conducting the assessment. The proposed system eliminates inter- and intra-observer variations with high reproducibility.

  • Gastric atrophy
  • updated Sydney system
  • probabilistic neural networks
  • automatic assessment
  • decision support systems
  • computer assisted
  • computer systems
  • gastric pathology
  • gastritis
  • image analysis

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Footnotes

  • Funding The authors are grateful to the Deanships of Scientific Research at Jordan University of Science and Technology and Yarmouk University for funding this project.

  • Competing interests None.

  • Provenance and peer review Not commissioned; externally peer reviewed.

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