Developing a low-cost, open-source, locally manufactured workstation and computational pipeline for automated histopathology evaluation using deep learning

Abstract

Background: Deployment and access to state-of-the-art precision medicine technologies remains a fundamental challenge in providing equitable global cancer care in low-resource settings. The expansion of digital pathology in recent years and its potential interface with diagnostic artificial intelligence algorithms provides an opportunity to democratize access to personalized medicine. Current digital pathology workstations, however, cost thousands to hundreds of thousands of dollars. As cancer incidence rises in many low- and middle-income countries, the validation and implementation of low-cost automated diagnostic tools will be crucial to helping healthcare providers manage the growing burden of cancer.

Methods: Here we describe a low-cost ($230) workstation for digital slide capture and computational analysis composed of open-source components. We analyze the predictive performance of deep learning models when they are used to evaluate pathology images captured using this open-source workstation versus images captured using common, significantly more expensive hardware. Validation studies assessed model performance on three distinct datasets and predictive models: head and neck squamous cell carcinoma (HPV positive versus HPV negative), lung cancer (adenocarcinoma versus squamous cell carcinoma), and breast cancer (invasive ductal carcinoma versus invasive lobular carcinoma).

Findings: When compared to traditional pathology image capture methods, low-cost digital slide capture and analysis with the open-source workstation, including the low-cost microscope device, was associated with model performance of comparable accuracy for breast, lung, and HNSCC classification. At the patient level of analysis, AUROC was 0.84 for HNSCC HPV status prediction, 1.0 for lung cancer subtype prediction, and 0.80 for breast cancer classification.

Interpretation: Our ability to maintain model performance despite decreased image quality and low-power computational hardware demonstrates that it is feasible to massively reduce costs associated with deploying deep learning models for digital pathology applications. Improving access to cutting-edge diagnostic tools may provide an avenue for reducing disparities in cancer care between high- and low-income regions.

Funding: Funding for this project including personnel support was provided via grants from NIH/NCIR25-CA240134, NIH/NCIU01-CA243075, NIH/NIDCRR56-DE030958, NIH/NCIR01-CA276652, NIH/NCIK08-CA283261, NIH/NCI-SOAR25CA240134, SU2C (Stand Up to Cancer) Fanconi Anemia Research Fund - Farrah Fawcett Foundation Head and Neck Cancer Research Team Grant, and the European UnionHorizon Program (I3LUNG).

Keywords: Cancer diagnostics; Digital pathology; Global health; Low-cost microscope; Machine learning; Open-source; Precision oncology.

Fig. 1

Open-source workflow. (a) Low-cost, open-source digital pathology workstation. All hardware (OpenFlexure Microscope, Raspberry Pi 4 Model B, Raspberry Pi camera module, monitor) and software (OpenFlexure Connect, Raspberry Pi OS, Slideflow) components with their costs and licenses are shown. (b) Open-source user interface for interactive visualization and generation of model predictions. Slideflow can be used to deploy a variety of trained models for digital pathology image classification, generating predictions for both partial-slide and whole-slide images. Predictions can be rendered for whole slides (rendered as a heatmap, as shown) or focal areas (rendered as individual tiles, as shown in the bottom right corner). The Slideflow user interface has been optimized for both x86 and low-power ARM-based devices. The above screenshot displays a heatmap of a WSI prediction, captured on the Raspberry Pi 4B. (c) Effects of computational stain normalization. Stain normalization increases visual similarity between the images captured by the Aperio AT2 slide scanner and the low-cost OpenFlexure device.

Fig. 2

AUROC of model performance for images captured on high- versus low-cost image acquisition and computational hardware. AUROC compares tile-level accuracy between high-cost Aperio AT2 and low-cost OpenFlexure image acquisition hardware. Model accuracy in terms of AUROC was maintained when the low-cost OpenFlexure device was used for image capture instead of the gold standard Aperio AT2 slide scanner, with chi-squared tests showing no statistically significant differences in model performance between low- and high-cost methods for any of the cancer subtypes (Lung cancer: X² (1, N_low-cost = 595, N_high-cost = 29532) = 0.363, p = 0.5468; Breast cancer: X² (1, N_low-cost = 360, N_high-cost = 1947) = 0.286, p = 0.5929; HNSCC: X² (1, N_low-cost = 587, N_high-cost = 5932) = 0.629, p = 0.4276).

Fig. 3

Comparing model accuracy for high- versus low-cost image acquisition and computational hardware. (a) Confusion matrices showing patient-level accuracy when predictions were made using images captured with the Aperio AT2 or OpenFlexure device. Model accuracy at the patient level with the low-cost image capture and analysis pipeline was equal to or greater than accuracy with the high-cost hardware for image capture and analysis. (b) Correlation between DL numerical predictions made on images captured by Aperio AT2 versus OpenFlexure device. Strong and very strong correlation coefficients (Lung cancer R = 0.85; Breast cancer R = 0.85; HNSCC R = 0.95) imply that the underlying analyses involved in model prediction are similar whether the images are captured by the Aperio AT2 slide scanner or by the OpenFlexure device.