Academic Journal
Artificial Intelligence and Machine Learning in AFM-Based Nanomechanical Biomarkers: From Force Curves and Stiffness Maps to Disease Classification and Treatment Monitoring.
| Τίτλος: | Artificial Intelligence and Machine Learning in AFM-Based Nanomechanical Biomarkers: From Force Curves and Stiffness Maps to Disease Classification and Treatment Monitoring. |
|---|---|
| Συγγραφείς: | Stylianou, Andreas |
| Πηγή: | Applied Sciences (2076-3417); Aug2026, Vol. 16 Issue 15, p7821, 40p |
| Θεματικοί όροι: | Atomic force microscopy, Artificial intelligence, Machine learning, Tumors, Nosology, Treatment effectiveness, Biomarkers, Fibrosis |
| Περίληψη: | Atomic force microscopy (AFM) has emerged as a powerful platform for quantifying nanoscale mechanical properties of cells, tissues, and extracellular matrix (ECM) components, providing candidate biomarkers for disease diagnosis, classification, prognosis, and treatment monitoring. However, the clinical translation of AFM-based nanomechanical biomarkers remains limited by low throughput, operator dependence, complex force-curve interpretation, heterogeneous biological samples, and the lack of standardized analytical pipelines. Artificial intelligence (AI) and machine learning (ML) approaches are increasingly being used to address these limitations by enabling automated AFM image and force-curve analysis, multiparametric feature extraction, cell and tissue classification, quality control, and high-throughput mechanophenotyping. This review summarizes how AI and ML have already been applied to AFM-derived nanomechanical and morphological data, with emphasis on cancer, fibrotic disease, and treatment response monitoring. We discuss classical ML models, deep learning approaches, clustering, fuzzy logic methods, and emerging automated Bio-AFM workflows. We further highlight current limitations, including small datasets, limited external validation, lack of reproducible reporting standards, and insufficient integration with clinical metadata. Finally, we propose a roadmap for AI-enabled AFM mechanobiomarkers, focusing on standardized datasets, explainable models, multimodal mechano-optical imaging, and clinically relevant validation strategies. [ABSTRACT FROM AUTHOR] |
| Copyright of Applied Sciences (2076-3417) is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Βάση Δεδομένων: | Complementary Index |
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| Items | – Name: Title Label: Title Group: Ti Data: Artificial Intelligence and Machine Learning in AFM-Based Nanomechanical Biomarkers: From Force Curves and Stiffness Maps to Disease Classification and Treatment Monitoring. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Stylianou%2C+Andreas%22">Stylianou, Andreas</searchLink> – Name: TitleSource Label: Source Group: Src Data: Applied Sciences (2076-3417); Aug2026, Vol. 16 Issue 15, p7821, 40p – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Atomic+force+microscopy%22">Atomic force microscopy</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+intelligence%22">Artificial intelligence</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Tumors%22">Tumors</searchLink><br /><searchLink fieldCode="DE" term="%22Nosology%22">Nosology</searchLink><br /><searchLink fieldCode="DE" term="%22Treatment+effectiveness%22">Treatment effectiveness</searchLink><br /><searchLink fieldCode="DE" term="%22Biomarkers%22">Biomarkers</searchLink><br /><searchLink fieldCode="DE" term="%22Fibrosis%22">Fibrosis</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Atomic force microscopy (AFM) has emerged as a powerful platform for quantifying nanoscale mechanical properties of cells, tissues, and extracellular matrix (ECM) components, providing candidate biomarkers for disease diagnosis, classification, prognosis, and treatment monitoring. However, the clinical translation of AFM-based nanomechanical biomarkers remains limited by low throughput, operator dependence, complex force-curve interpretation, heterogeneous biological samples, and the lack of standardized analytical pipelines. Artificial intelligence (AI) and machine learning (ML) approaches are increasingly being used to address these limitations by enabling automated AFM image and force-curve analysis, multiparametric feature extraction, cell and tissue classification, quality control, and high-throughput mechanophenotyping. This review summarizes how AI and ML have already been applied to AFM-derived nanomechanical and morphological data, with emphasis on cancer, fibrotic disease, and treatment response monitoring. We discuss classical ML models, deep learning approaches, clustering, fuzzy logic methods, and emerging automated Bio-AFM workflows. We further highlight current limitations, including small datasets, limited external validation, lack of reproducible reporting standards, and insufficient integration with clinical metadata. Finally, we propose a roadmap for AI-enabled AFM mechanobiomarkers, focusing on standardized datasets, explainable models, multimodal mechano-optical imaging, and clinically relevant validation strategies. [ABSTRACT FROM AUTHOR] – Name: Abstract Label: Group: Ab Data: <i>Copyright of Applied Sciences (2076-3417) is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.3390/app16157821 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 40 StartPage: 7821 Subjects: – SubjectFull: Atomic force microscopy Type: general – SubjectFull: Artificial intelligence Type: general – SubjectFull: Machine learning Type: general – SubjectFull: Tumors Type: general – SubjectFull: Nosology Type: general – SubjectFull: Treatment effectiveness Type: general – SubjectFull: Biomarkers Type: general – SubjectFull: Fibrosis Type: general Titles: – TitleFull: Artificial Intelligence and Machine Learning in AFM-Based Nanomechanical Biomarkers: From Force Curves and Stiffness Maps to Disease Classification and Treatment Monitoring. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Stylianou, Andreas IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 08 Text: Aug2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 20763417 Numbering: – Type: volume Value: 16 – Type: issue Value: 15 Titles: – TitleFull: Applied Sciences (2076-3417) Type: main |
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