Machine learning and artificial intelligence: Enabling the clinical translation of atomic force microscopy-based biomarkers for cancer diagnosis
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 th...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2024-12-01
|
| Series: | Computational and Structural Biotechnology Journal |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S200103702400326X |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850250550083321856 |
|---|---|
| author | Aidan T. O’Dowling Brian J. Rodriguez Tom K. Gallagher Stephen D. Thorpe |
| author_facet | Aidan T. O’Dowling Brian J. Rodriguez Tom K. Gallagher Stephen D. Thorpe |
| author_sort | Aidan T. O’Dowling |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-3ddc98631da143d798e2c9ca3a4130de |
| institution | OA Journals |
| issn | 2001-0370 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Computational and Structural Biotechnology Journal |
| spelling | doaj-art-3ddc98631da143d798e2c9ca3a4130de2025-08-20T01:58:11ZengElsevierComputational and Structural Biotechnology Journal2001-03702024-12-012466167110.1016/j.csbj.2024.10.006Machine learning and artificial intelligence: Enabling the clinical translation of atomic force microscopy-based biomarkers for cancer diagnosisAidan T. O’Dowling0Brian J. Rodriguez1Tom K. Gallagher2Stephen D. Thorpe3UCD School of Medicine, University College Dublin, Dublin, Ireland; UCD Conway Institute of Biomolecular and Biomedical Research, University College Dublin, Dublin, Ireland; Department of Hepatobiliary and Transplant Surgery, St Vincent’s University Hospital, Dublin, IrelandUCD Conway Institute of Biomolecular and Biomedical Research, University College Dublin, Dublin, Ireland; UCD School of Physics, University College Dublin, Dublin, IrelandUCD School of Medicine, University College Dublin, Dublin, Ireland; Department of Hepatobiliary and Transplant Surgery, St Vincent’s University Hospital, Dublin, IrelandUCD School of Medicine, University College Dublin, Dublin, Ireland; UCD Conway Institute of Biomolecular and Biomedical Research, University College Dublin, Dublin, Ireland; Trinity Centre for Bioengineering, Trinity College Dublin, Dublin, Ireland; Correspondence to: UCD Conway Institute, University College Dublin, Belfield, Dublin D04 V1W8, Ireland.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.http://www.sciencedirect.com/science/article/pii/S200103702400326XForce spectroscopyTissue mechanicsCell mechanicsMechanobiologyBiophysicsAtomic force microscopy |
| spellingShingle | Aidan T. O’Dowling Brian J. Rodriguez Tom K. Gallagher Stephen D. Thorpe Machine learning and artificial intelligence: Enabling the clinical translation of atomic force microscopy-based biomarkers for cancer diagnosis Computational and Structural Biotechnology Journal Force spectroscopy Tissue mechanics Cell mechanics Mechanobiology Biophysics Atomic force microscopy |
| title | Machine learning and artificial intelligence: Enabling the clinical translation of atomic force microscopy-based biomarkers for cancer diagnosis |
| title_full | Machine learning and artificial intelligence: Enabling the clinical translation of atomic force microscopy-based biomarkers for cancer diagnosis |
| title_fullStr | Machine learning and artificial intelligence: Enabling the clinical translation of atomic force microscopy-based biomarkers for cancer diagnosis |
| title_full_unstemmed | Machine learning and artificial intelligence: Enabling the clinical translation of atomic force microscopy-based biomarkers for cancer diagnosis |
| title_short | Machine learning and artificial intelligence: Enabling the clinical translation of atomic force microscopy-based biomarkers for cancer diagnosis |
| title_sort | machine learning and artificial intelligence enabling the clinical translation of atomic force microscopy based biomarkers for cancer diagnosis |
| topic | Force spectroscopy Tissue mechanics Cell mechanics Mechanobiology Biophysics Atomic force microscopy |
| url | http://www.sciencedirect.com/science/article/pii/S200103702400326X |
| work_keys_str_mv | AT aidantodowling machinelearningandartificialintelligenceenablingtheclinicaltranslationofatomicforcemicroscopybasedbiomarkersforcancerdiagnosis AT brianjrodriguez machinelearningandartificialintelligenceenablingtheclinicaltranslationofatomicforcemicroscopybasedbiomarkersforcancerdiagnosis AT tomkgallagher machinelearningandartificialintelligenceenablingtheclinicaltranslationofatomicforcemicroscopybasedbiomarkersforcancerdiagnosis AT stephendthorpe machinelearningandartificialintelligenceenablingtheclinicaltranslationofatomicforcemicroscopybasedbiomarkersforcancerdiagnosis |