Effective pattern recognition requires carefully designed ground-truth datasets. In this technical note, we first summarize potential data collection issues in digital pathology and then propose guidelines to build more realistic ground-truth datasets and to control their quality. We hope our comments will foster the effective application of pattern recognition approaches in digital pathology.

Background: In the year 2014, Android smartphones accounted for one-third of mobile connections globally but are predicted to increase to two-thirds by 2020. In developing countries, where teleconsultations can benefit health-care providers most, the ratio is even higher. This study compared the use of two Android phones, an 8 megapixel (MP) and a 16 MP phone, for capturing microscopic images. Method: The Android phones were used to capture images and videos of a gastrointestinal biopsy teaching set of referred cases from the Armed Forces Institute of Pathology (AFIP). The acquired images and videos were reviewed online by two pathologists for image quality, adequacy for diagnosis, usefulness of video overviews, and confidence in diagnosis, on a 5-point Likert scale. Results: The results show higher means in a 5-point Likert scale for the 8 MP versus the 16 MP phone that were statistically significant in adequacy of images (4.0 vs. 3.75) for rendering diagnosis and for agreement with the reference diagnosis (2.33 vs. 2.07). Although the quality of images was found higher in the 16 MP phone (3.8 vs. 3.65), these were not statistically significant. Adding video images of the entire specimen was found to be useful for evaluating the slides (combined mean, 4.0). Conclusion: For telepathology and other image dependent practices in developing countries, Android phones could be a useful tool for capturing images.