AI4S News · AI and science frontiers

bioRxiv Oct 20, 2026

Flowing cells get organelle-level eyes: three structured-light frames plus self-supervised reconstruction reach about 118 nm at up to roughly 720 cells per second

Super-resolution imaging flow cytometry (SR-IFC) brings structured-illumination microscopy into a continuous microfluidic stream: each cell yields only three phase-shifted frames, physical modulation sharpens the flow axis, and a network trained solely on the imaged sample's own data infers the missing transverse detail. On 100 nm fluorescent beads, SR-IFC reached about 118 nm along the flow axis and 110 nm across it, versus 222 to 227 nm for wide-field imaging of the same beads; with a 2048 × 320 pixel readout, the theoretical throughput ceiling is about 720 cells per second.

Okayama University Hospital Tohoku University Okayama University Wuhan University University of California, Los Angeles Ludwig-Maximilians-Universität München The University of Tokyo Kindai University

Yingdong Luo（First author）Keisuke Goda（Corresponding author）Tianben Ding（Corresponding author）

SR-IFC Structured Illumination Microscopy Image Super-Resolution Self-Supervised Learning

Development of an AI-Based Smartphone Application for Rapid Tick Identification and Geospatial Mapping: A Pilot Study

This pilot study collected ticks between May 2023 and December 2024 from patients presenting tick bites at 10 medical institutions in Okayama, Hiroshima, and Kagawa prefectures, supplemented with wild tick images, and developed a two-stage AI pipeline of object detection and genus-level classification in which a YOLO-based model trained on 3258 annotated public images achieved mAP@0.5 of 0.954 and ResNet50 achieved mean validation accuracy of 95% on 533 tick images covering four genera, integrating the system into a prototype smartphone application with geospatial visualization to support clinical risk assessment and public health surveillance.