μMobileScan: A Smartphone Whole Slide Imaging App for Red Blood Cell Counting with Real-time Guidance

Red blood cell counting is an essential metric in hematology analysis for disease diagnosis. Low red blood cell count can indicate anemia, and high red blood cell count can indicate tumors. The current clinical standard for counting the cells involves human experts counting the cells on hemocytometers through a microscope. This approach is labor-intensive and volatile. The manual process of counting cells via hemocytometer has no record (i.e., images) other than the clinicians' count. Whole slide imaging (WSI) systems can be used for slide scanning and digitization, but the high cost limits access to WSI technology. We propose a smartphone app called μMobileScan to make this measure more readily available. μMobileScan solely relies on commonly available tools, smartphone cameras, and microscopes, to reconstruct a virtual WSI hemocytometer by providing real-time guidance to the users and operating cell counting on the constructed images. We conducted studies to evaluate the WSI construction quality, learning curve, and cell counting accuracy demonstrating usability. The studies demonstrated the impact of scanning routes and overlap percentage on feature mapping. The learning curve evaluation on 8 participants illustrated that our system can guide users to scan the hemocytometer with consistent overlap percentages, and the scanning time and success rate converge after 5 trials. Usability testing with four participants (two experts and two novices) on de-identified samples from three donors under various configurations achieved high cell counting accuracy (MAPE = 1.40%, SDAPE = 1.48%). Evaluations across diverse hardware and optical configurations demonstrated the robustness and practicality of the proposed system.

Authors

Institutions

Publication Details

Journal
Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies
Published
2026-03-16
DOI
https://doi.org/10.1145/3790109
Citations
1
Primary Topic
Digital Imaging for Blood Diseases
Type
article
Field-Weighted Citation Impact
46.84
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

μMobileScan: A Smartphone Whole Slide Imaging App for Red Blood Cell Counting with Real-time Guidance

Alexander T. Adams, J. Alexander Bae, Sixuan Wu
1 citations
Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies
Digital Imaging for Blood Diseases
46.84
article

μMobileScan: A Smartphone Whole Slide Imaging App for Red Blood Cell Counting with Real-time Guidance

Alexander T. Adams, J. Alexander Bae, Sixuan Wu
article en
1 citations

Abstract

Red blood cell counting is an essential metric in hematology analysis for disease diagnosis. Low red blood cell count can indicate anemia, and high red blood cell count can indicate tumors. The current clinical standard for counting the cells involves human experts counting the cells on hemocytometers through a microscope. This approach is labor-intensive and volatile. The manual process of counting cells via hemocytometer has no record (i.e., images) other than the clinicians' count. Whole slide imaging (WSI) systems can be used for slide scanning and digitization, but the high cost limits access to WSI technology. We propose a smartphone app called μMobileScan to make this measure more readily available. μMobileScan solely relies on commonly available tools, smartphone cameras, and microscopes, to reconstruct a virtual WSI hemocytometer by providing real-time guidance to the users and operating cell counting on the constructed images. We conducted studies to evaluate the WSI construction quality, learning curve, and cell counting accuracy demonstrating usability. The studies demonstrated the impact of scanning routes and overlap percentage on feature mapping. The learning curve evaluation on 8 participants illustrated that our system can guide users to scan the hemocytometer with consistent overlap percentages, and the scanning time and success rate converge after 5 trials. Usability testing with four participants (two experts and two novices) on de-identified samples from three donors under various configurations achieved high cell counting accuracy (MAPE = 1.40%, SDAPE = 1.48%). Evaluations across diverse hardware and optical configurations demonstrated the robustness and practicality of the proposed system.

Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous TechnologiesVol. 10(1)
Georgia Institute of Technology (US)
Decent work and economic growth
Openalex Percentile: Top 0%
Digital Imaging for Blood Diseases
46.84
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.