ReviewPhysical chemistry chemical physics : PCCP2024
On machine learning analysis of atomic force microscopy images for image classification, sample surface recognition.
Review in Physical chemistry chemical physics : PCCP, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
What it found
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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.
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Who cites it
10 citing papers in PubMed.
- High-speed atomic force microscopy of membrane proteins: From dynamic imaging to integrative structural biology.Current opinion in structural biology · 2026Review
- Artificial Intelligence for Discovery in Life Sciences.Bioconjugate chemistry · 2026Review
- Estimating Protein Conformational States from High-Speed AFM Images with Molecular Dynamics and Deep Learning.Journal of chemical information and modeling · 2026Article
- Nanomechanical Phenotyping of Circulating Tumor Cells with Atomic Force Microscopy.Methods in molecular biology (Clifton, N.J.) · 2026Article
- Automated morphological classification and quantification of cerebrospinal fluid extracellular vesiclesNanoscale advances · 2025Article
- Multiplexed Nanoscale Viscoelastic Mapping at Multiple Time Scales of Melanoma Cells as a Label-Free Cancer Biomarker.ACS nano · 2025Article
- Analysis of biofilm assembly by large area automated AFM.NPJ biofilms and microbiomes · 2025Article
- Detection of Human Bladder Epithelial Cancerous Cells with Atomic Force Microscopy and Machine Learning.Cells · 2024Article
- Mechanistic Insights and Technical Challenges in Sulfur-Based Batteries: A ComprehensiveACS energy letters · 2024Review
- Mechanical spectroscopy of materials using atomic force microscopy (AFM-MS).Materials today (Kidlington, England) · 2024Article
Corrections and comments
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Authors and funding
1 author.
Funding
Abstract
Atomic force microscopy (AFM or SPM) imaging is one of the best matches with machine learning (ML) analysis among microscopy techniques. The digital format of AFM images allows for direct utilization in ML algorithms without the need for additional processing. Additionally, AFM enables the simultaneous imaging of distributions of over a dozen different physicochemical properties of sample surfaces, a process known as multidimensional imaging. While this wealth of information can be challenging to analyze using traditional methods, ML provides a seamless approach to this task. However, the relatively slow speed of AFM imaging poses a challenge in applying deep learning methods broadly used in image recognition. This prospective is focused on ML recognition/classification when using a relatively small number of AFM images,
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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.