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A survey on Barrett's esophagus analysis using machine learning.

Luis A de Souza1, Christoph Palm2, Robert Mendel3

  • 1Department of Computing, São Paulo State University, UNESP, Brazil; Regensburg Medical Image Computing (ReMIC), Ostbayerische Technische Hochschule Regensburg (OTH Regensburg), Germany.

Computers in Biology and Medicine
|April 8, 2018
PubMed
Summary

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This summary is machine-generated.

Machine learning and artificial intelligence offer promising new methods for diagnosing Barrett's esophagus (BE). This review explores recent studies on AI-driven technologies for BE diagnosis and treatment, aiding in early detection of neoplastic changes.

Area of Science:

  • Medical Informatics
  • Computer Science
  • Oncology

Background:

  • Barrett's esophagus (BE) poses challenges in diagnosis and surveillance due to complex progression patterns.
  • Early detection of dysplasia and adenocarcinoma in BE is crucial for effective treatment.
  • Current endoscopic surveillance methods can be limited in identifying subtle neoplastic changes.

Purpose of the Study:

  • To systematically review recent studies on machine learning (ML) and artificial intelligence (AI) applications in Barrett's esophagus diagnosis and treatment.
  • To analyze the objectives, methodologies, and results of ML-based approaches for BE evaluation.
  • To highlight the potential of computer analysis in assisting the diagnosis and automatic identification of BE.

Main Methods:

  • Systematic literature review of studies from major scientific databases (e.g., PubMed, IEEE Xplore, Science Direct).
Keywords:
AdenocarcinomaBarrett's esophagusComputer-aided diagnosisImage processingMachine learningPattern recognition

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  • Analysis of selected works focusing on their objectives, methodologies, and reported results.
  • Examination of machine learning techniques applied to segmentation and classification of BE neoplastic regions.

    • Main Results:

    • Identified a growing body of research utilizing ML and AI for BE diagnosis.
    • Demonstrated the application of ML techniques in automated segmentation for BE dysplasia evaluation.
    • Highlighted studies focused on automatic detection and classification of neoplastic regions in BE.

    Conclusions:

    • Machine learning and AI represent a promising frontier for improving Barrett's esophagus diagnosis and management.
    • Automated analysis using ML techniques can significantly aid in the detection of BE progression.
    • Further research in this area is valuable for enhancing endoscopic surveillance and patient outcomes.