Whole Slide Imaging in Genitourinary Pathology: A Cloud-Based Digitisation Workflow for Resource-Limited Settings
Year: 2025
Type: article
Abstract: OBJECTIVE: To digitise genitourinary glass slides and use them for research, education, and knowledge sharing. STUDY DESIGN: Observational study. Place and Duration of the Study: Department of Pathology and Laboratory Medicine, The Aga Khan University Hospital, Karachi, Pakistan, from May 2025 to June 2025. METHODOLOGY: This study used 31 renal biopsy cores with special stains and 21 prostate tissue cores with haematoxylin and eosin stains. Videos at 10x magnification were recorded using a microscope-mounted camera and uploaded to the DeepLIIF open-source cloud platform to generate whole-slide images (WSIs). Three pathologists independently reviewed the images, and inter-observer agreement was assessed using the Kappa statistic. RESULTS: Pathologist A showed 100% agreement with the reference diagnosis across all renal stains. Pathologist B demonstrated concordance of 77% for haemoxylin and eosin (H&E) and 74% for periodic Acid-Schiff (PAS), Jones methenamine silver (JMS), and trichrome stains. Pathologist C reported 100% agreement for H&E and PAS, 96% for trichrome, and 74% for JMS. In the prostate biopsies, among 21 cases (6 malignant and 15 benign), the diagnosis regarding malignancy and perineural invasion was compared to the reference diagnosis. CONCLUSION: WSIs can be viewed through shareable links, even on smartphones. This helps in getting consensus on improved diagnosis, especially in resource-limited settings. KEY WORDS: Digitisation, Genitourinary pathology, Image interpretation, Telepathology.
Language: English
Topic: AI in cancer detection
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4417070757
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