Implementation of digital pathology in a low-resource setting: opportunities and challenges

Abstract Background Digital pathology (DP) offers significant advantages in diagnostic efficiency and reproducibility. However, its implementation in low-resource settings remains challenging due to cost, infrastructure limitations, and workflow constraints. Objective To describe the implementation and validation of a digital pathology workflow in a high-volume laboratory in Northeastern Brazil, highlighting strategies for deployment in a low-cost environment. Methods A midrange scanner (MoticEasyScan®) was integrated with the laboratory information system (apLIS®) to support whole slide imaging (WSI) for hematoxylin and eosin (H&E) stained and ancillary slides. The workflow was redesigned to include technical infrastructure upgrades and staff training. Validation followed CAP guidelines and included 384 slides from 64 cases, evaluated by two pathologists using both digital and physical formats. Results Concordance between digital and traditional diagnoses reached 98.72%, with near-perfect interobserver agreement (Kappa = 0.928 and 0.958; p < 0.05). Challenges included limited scanner throughput, storage demands (~ 12 TB/quarter), and variable monitor quality. Despite these constraints, the laboratory successfully digitized 60% of its routine workload, facilitating case review, image sharing, and research expansion. Conclusion This study demonstrates the feasibility of implementing digital pathology in resource-limited settings using cost-effective solutions and workflow optimization. The validated process offers a scalable model for similar laboratories, with potential to integrate artificial intelligence tools in future diagnostic applications.

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Publication Details

Journal
Surgical and Experimental Pathology
Published
2025-09-19
DOI
https://doi.org/10.1186/s42047-025-00207-2
Citations
1
Primary Topic
AI in cancer detection
Type
article
Field-Weighted Citation Impact
2.48
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article

Implementation of digital pathology in a low-resource setting: opportunities and challenges

Guilherme Velozo, Igor Brasil Costa, Cleto Dantas Nogueira, André Teixeira et al.
1 citations
Surgical and Experimental Pathology
AI in cancer detection
2.48
article

Implementation of digital pathology in a low-resource setting: opportunities and challenges

Guilherme Velozo, Igor Brasil Costa, Cleto Dantas Nogueira, André Teixeira, Juliana Cordeiro, Fábio Távora, Fabiana Vigatto
article en
1 citations

Abstract

Abstract Background Digital pathology (DP) offers significant advantages in diagnostic efficiency and reproducibility. However, its implementation in low-resource settings remains challenging due to cost, infrastructure limitations, and workflow constraints. Objective To describe the implementation and validation of a digital pathology workflow in a high-volume laboratory in Northeastern Brazil, highlighting strategies for deployment in a low-cost environment. Methods A midrange scanner (MoticEasyScan®) was integrated with the laboratory information system (apLIS®) to support whole slide imaging (WSI) for hematoxylin and eosin (H&E) stained and ancillary slides. The workflow was redesigned to include technical infrastructure upgrades and staff training. Validation followed CAP guidelines and included 384 slides from 64 cases, evaluated by two pathologists using both digital and physical formats. Results Concordance between digital and traditional diagnoses reached 98.72%, with near-perfect interobserver agreement (Kappa = 0.928 and 0.958; p < 0.05). Challenges included limited scanner throughput, storage demands (~ 12 TB/quarter), and variable monitor quality. Despite these constraints, the laboratory successfully digitized 60% of its routine workload, facilitating case review, image sharing, and research expansion. Conclusion This study demonstrates the feasibility of implementing digital pathology in resource-limited settings using cost-effective solutions and workflow optimization. The validated process offers a scalable model for similar laboratories, with potential to integrate artificial intelligence tools in future diagnostic applications.

Surgical and Experimental PathologyVol. 8(1)
Universidade Federal do Ceará (BR), Universidade de Fortaleza (BR)
Openalex Percentile: Top 8%
AI in cancer detection
2.48
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