Machine learning and artificial intelligence: Enabling the clinical translation of atomic force microscopy-based biomarkers for cancer diagnosis

被引：2
作者：
O'Dowling, Aidan T. [1 ,2 ,3 ]
Rodriguez, Brian J. [2 ,4 ]
Gallagher, Tom K. [1 ,3 ]
Thorpe, Stephen D. [1 ,2 ,5 ]
机构：
[1] Univ Coll Dublin, UCD Sch Med, Dublin, Ireland
[2] Univ Coll Dublin, UCD Conway Inst Biomol & Biomed Res, Dublin, Ireland
[3] St Vincents Univ Hosp, Dept Hepatobiliary & Transplant Surg, Dublin, Ireland
[4] Univ Coll Dublin, UCD Sch Phys, Dublin, Ireland
[5] Trinity Coll Dublin, Trinity Ctr Bioengn, Dublin, Ireland
来源：
COMPUTATIONAL AND STRUCTURAL BIOTECHNOLOGY JOURNAL | 2024年 / 24卷
关键词：
Force spectroscopy; Tissue mechanics; Cell mechanics; Mechanobiology; Biophysics; Atomic force microscopy; EXTRACELLULAR-MATRIX; VISCOELASTIC PROPERTIES; OMICS DATA; CELLS; NANOINDENTATION; STIFFNESS; CONTACT; METASTASIS; ELASTICITY; EXPRESSION;
D O I：
10.1016/j.csbj.2024.10.006
中图分类号：
Q5 [生物化学]; Q7 [分子生物学];
学科分类号：
071010 ; 081704 ;
摘要：
The influence of biomechanics on cell function has become increasingly defined over recent years. Biomechanical changes are known to affect oncogenesis; however, these effects are not yet fully understood. Atomic force microscopy (AFM) is the gold standard method for measuring tissue mechanics on the micro- or nano-scale. Due to its complexity, however, AFM has yet to become integrated in routine clinical diagnosis. Artificial intelligence (AI) and machine learning (ML) have the potential to make AFM more accessible, principally through automation of analysis. In this review, AFM and its use for the assessment of cell and tissue mechanics in cancer is described. Research relating to the application of artificial intelligence and machine learning in the analysis of AFM topography and force spectroscopy of cancer tissue and cells are reviewed. The application of machine learning and artificial intelligence to AFM has the potential to enable the widespread use of nanoscale morphologic and biomechanical features as diagnostic and prognostic biomarkers in cancer treatment.
引用
收藏
页码：661 / 671
页数：11
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