ReviewBeilstein journal of nanotechnology2021
The role of convolutional neural networks in scanning probe microscopy: a review.
Review in Beilstein journal of nanotechnology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 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
11 citing papers in PubMed, 42 citations in OpenAlex.
- A Comprehensive Review of Nuclear Mechanics: Advances, Disease Relevance, Methodologies, and AI Applications.Cell biochemistry and biophysics · 2026Review
- Automated morphological classification and quantification of cerebrospinal fluid extracellular vesiclesNanoscale advances · 2025Article
- Advances in nanomechanical property mapping by atomic force microscopy.Nanoscale advances · 2025Review
- High-Throughput Nanorheology of Living Cells Powered by Supervised Machine Learning.Advanced intelligent systems (Weinheim an der Bergstrasse, Germany) · 2025Article
- Data-driven analysis for the evaluation of cortical mechanics of non-adherent cells.Scientific reports · 2025Article
- Deep learning for enhancement of low-resolution and noisy scanning probe microscopy images.Beilstein journal of nanotechnology · 2025Article
- On machine learning analysis of atomic force microscopy images for image classification, sample surface recognition.Physical chemistry chemical physics : PCCP · 2024Review
- Article
- Machine learning-aided atomic structure identification of interfacial ionic hydrates from AFM images.National science review · 2023Article
- Application of self-organizing maps to AFM-based viscoelastic characterization of breast cancer cell mechanics.Scientific reports · 2023Article
- Deep learning strategy for small dataset from atomic force microscopy mechano-imaging on macrophages phenotypes.Frontiers in bioengineering and biotechnology · 2023Article
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 at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Progress in computing capabilities has enhanced science in many ways. In recent years, various branches of machine learning have been the key facilitators in forging new paths, ranging from categorizing big data to instrumental control, from materials design through image analysis. Deep learning has the ability to identify abstract characteristics embedded within a data set, subsequently using that association to categorize, identify, and isolate subsets of the data. Scanning probe microscopy measures multimodal surface properties, combining morphology with electronic, mechanical, and other characteristics. In this review, we focus on a subset of deep learning algorithms, that is, convolutional neural networks, and how it is transforming the acquisition and analysis of scanning probe data.
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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.