Mechanical properties of biological cells can serve as biomarkers for indicating various diseases like cancer and sickle cell disease. Hertzian model-based prediction of mechanical properties of biological cells, although most widely used, has shown to have limited potential in determining constitutive parameters of cells of uneven shape and non-linear force-indentation responses of AFM-based cell nanoindentation. We report a new artificial neural network-aided approach, which takes into account, the variation in cell shapes and their effect on the predictions in cell mechanophenotyping. We have developed an artificial neural network (ANN) model which could predict the mechanical properties of biological cells by utilizing the force vs. indentation curve of AFM and we obtained a recall of 0.98 ± 0.03 and 1 ± 0.0 for hyperelastic and elastic cells respectively for the prediction error of less than 10%. We envisage that the developed technique can be used for the validation of quantitative biomechanical markers for diagnoses of diseases like cancer and sickle cell disease which could help to improve clinical decision-making.

The artificial neural network (ANN) based models have shown the potential to provide alternate data-driven solutions in disease diagnostics, cell sorting and overcoming AFM-related limitations. Hertzian model-based prediction of mechanical properties of biological cells, although most widely used, has shown to have limited potential in determining constitutive parameters of cells of uneven shape and non-linear nature of force-indentation curves in AFM-based cell nanoindentation. We report a new artificial neural network-aided approach, which takes into account, the variation in cell shapes and their effect on the predictions in cell mechanophenotyping. We have developed an artificial neural network (ANN) model which could predict the mechanical properties of biological cells by utilizing the force vs. indentation curve of AFM. For cells with 1 µm contact length (Platelets), we obtained a recall of 0.97 ± 0.03 and 0.99 ± 0.0 for cells with hyperelastic and linear elastic constitutive properties respectively with a prediction error of less than 10%. Also, for cells with 6-8 µm contact length (RBCs), we obtained the recall of 0.975 in predicting mechanical properties with less than 15% error. We envisage that the developed technique can be used for better estimation of cells' constitutive parameters by incorporating cell topography into account.