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Bridging the Gap: Digital Adaptation in Low-Resource Settings.

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PubMed
Summary
This summary is machine-generated.

This study introduces DeepLIIF, a free, open-source platform for digital pathology (DP) and computational pathology (CP). It enables cost-effective WSI digitization and analysis, making advanced tools accessible for low-resource settings.

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Area of Science:

  • Pathology
  • Medical Informatics
  • Biotechnology

Background:

  • Digital pathology (DP) and computational pathology (CP) are revolutionizing histopathology by integrating digital tools and deep learning (DL) for image analysis.
  • Traditional methods are being replaced by digitized whole slide images (WSIs) for various applications, including diagnosis and research.
  • The high cost of advanced DP and CP tools limits accessibility for low-resource organizations, especially in low- and middle-income countries (LMICs).

Purpose of the Study:

  • To present DeepLIIF, a novel, free, open-source, cloud-based platform designed for cost-effective WSI digitization, sharing, and semi-automated analysis.
  • To demonstrate the platform's utility in low-resource settings using accessible technology.
  • To overcome the financial barriers associated with advanced digital pathology solutions.

Main Methods:

  • Development and implementation of a cloud-based platform (DeepLIIF) utilizing low-cost equipment for WSI digitization.
  • Testing the platform on diverse tissue biopsies, including skin, liver, prostate, and breast samples.
  • Evaluating the platform's ease of use, accessibility via smartphones, and WSI sharing capabilities for telepathology and research.

Main Results:

  • The DeepLIIF platform successfully enabled cost-effective digitization and semi-automated analysis of WSIs across various tissue types.
  • Satisfactory image quality was achieved using low-cost equipment and accessible devices like smartphones.
  • The platform facilitates WSI sharing through browser links, supporting telepathology, education, and research initiatives.

Conclusions:

  • DeepLIIF offers a viable, low-cost solution for implementing digital pathology and computational pathology in resource-limited environments.
  • The platform enhances accessibility to advanced histopathology tools, promoting wider adoption in LMICs.
  • Future development should address limitations such as biopsy type restrictions and video capture resolution inconsistencies.