ArticleBiosensors2024
Enhanced Nanoparticle Recognition via Deep Learning-Accelerated Plasmonic Sensing.
Article in Biosensors, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 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
3 citing papers in PubMed.
- Artificial intelligence and extracellular vesicles in oncology: towards tumor diagnosis, prediction, and therapy.Drug delivery · 2026Review
- Morphology-guided deep learning for nanoparticle agglomeration diagnostic assays.Scientific reports · 2026Article
- Deep Learning-Enhanced Nanozyme-Based Biosensors for Next-Generation Medical Diagnostics.Biosensors · 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
5 authors.
Funding
Abstract
Surface plasmon microscopy proves to be a potent tool for capturing interferometric scattering imaging data of individual particles at both micro and nanoscales, offering considerable potential for label-free analysis of bio-particles and bio-molecules such as exosomes, viruses, and bacteria. However, the manual analysis of acquired images remains a challenge, particularly when dealing with dense samples or strong background noise, common in practical measurements. Manual analysis is not only prone to errors but is also time-consuming, especially when handling a large volume of experimental images. Currently, automated methods for sensing and analysis of such data are lacking. In this paper, we develop an accelerated approach for surface plasmon microscopy imaging of individual particles based on combining the interference scattering model of single particle and deep learning processing. We create hybrid datasets by combining the theoretical simulation of particle images with the actual measurements. Subsequently, we construct a neural network utilizing the EfficientNet architecture. Our results demonstrate the effectiveness of this novel deep learning technique in classifying interferometric scattering images and identifying multiple particles under noisy conditions. This advancement paves the way for practical bio-applications through efficient automated particle analysis.
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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.