ReviewJournal of biophotonics2025
Unstained Blood Smear Analysis: A Review of Rule-Based, Machine Learning, and Deep Learning Techniques.
Review in Journal of biophotonics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- Review
- Unstained Blood Smear Analysis: A Review of Rule-Based, Machine Learning, and Deep Learning Techniques.Journal of biophotonics · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
Blood cells are central to oxygen transport, immune defense, and hemostasis. Their number and morphology act as sensitive biomarkers, making accurate segmentation and classification essential for hematological diagnostics. Biophotonic techniques now provide label-free imaging of unstained smears by exploiting intrinsic phase and scattering contrast, yet such images exhibit low optical signal and subtle morphological variation that exacerbate segmentation errors. Label-free modalities nevertheless preserve contrast where dyes fail, motivating renewed interest in unstained workflows. This review analyzes rule-based, machine-learning, and deep-learning approaches for segmenting and classifying label-free blood cells, highlighting performance gains, persistent challenges, and future directions for clinical adoption.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.