Figure 4 from Deep learning-assisted smartphone-based quantitative microscopy for label-free peripheral blood smear analysis | Semantic Scholar (2024)

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It is verified that the subcellular morphology of blood components can be visualized without complex staining due to the factor that defocusing can convert the second-order derivative distribution of samples' optical phase into intensity and the illumination of 415 nm light can significantly enhance the contrast.

The Trainable Weka Segmentation (TWS), a machine learning tool that leverages a limited number of manual annotations in order to train a classifier and segment the remaining data automatically, is introduced.

The results confirm that the smartphone imaging flow cytometer (sIFC) is capable of both enumerating single particles in flow and identifying morphological features with high resolution and minimal hardware.

The developed system is approximately 10 times less expensive than previous configurations and can serve as a point-of-care hematology analyzer, as well as be applied broadly in biomedicine as a simple compact, low-cost, quantitative molecular imaging system.

A fully automated hematological analysis framework based on single-channel (single-wavelength), label-free deep-ultraviolet (UV) microscopy that serves as a fast, cost-effective alternative to conventional hematology analyzers is presented.

While current blood autofluorescence detection approaches may not be suitable for primary clinical diagnosis, it certainly has tremendous potential in developing methods for large scale screening that can identify high risk groups for further diagnosis using highly specific diagnostic tests.

This protocol enables the sensitive detection of norovirus from environmental water samples in situ using a custom-built smartphone-based fluorescence microscope to detect anti-norovirus antibody-conjugated fluorescent particles on a paper microfluidic chip.

An artificial-intelligence enabled reagent-free imaging hematology analyzer (AIRFIHA) modality that can accurately classify subpopulations of leukocytes with minimal sample preparation that is clinically translatable and can be deployed in resource-limited settings, e.g., during pandemic situations for the rapid screening of infectious diseases.

This manuscript demonstrates the first practical implementation of Microscopy with Ultraviolet Surface Excitation (MUSE) in a compact smartphone microscope called Pocket MUSE, resulting in a remarkably effective design.

This is the first report of deep learning for spatio-temporal-based cell identification and disease detection using a digital holographic microscopy system and the proposed deep learning strategy is demonstrated as having improved performance over conventional machine learning approaches on a clinically relevant dataset.

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