Comparison of Artificial Intelligence based approaches to cell function prediction. (2020)

• Record Type: Journal Article
• Authors: Padi, Sarala; Manescu, Petru; Schaub, Nicholas; Hotaling, Nathan; Simon, Carl; Bharti, Kapil; Bajcsy, Peter
• Is Part Of: Informatics in medicine unlocked. Volume 18(2020)
• Publication Date: 2020
• Subjects: Cell segmentation -- Cell function prediction -- Retinal Pigment Epithelium Cell -- Deep learning -- Age-related macular degeneration -- Trans-Epithelial Resistance -- Vascular Endothelial Growth Factor; Medical informatics -- Periodicals; 610.285
• DOI: 10.1016/j.imu.2019.100270
• Languages: English
• ISSNs: 2352-9148

Abstract

Abstract: Predicting Retinal Pigment Epithelium (RPE) cell functions in stem cell implants using non-invasive bright field microscopy imaging is a critical task for clinical deployment of stem cell therapies. Such cell function predictions can be carried out using Artificial Intelligence (AI) based models. In this paper we used Traditional Machine Learning (TML) and Deep Learning (DL) based AI models for cell function prediction tasks. TML models depend on feature engineering and DL models perform feature engineering automatically but have higher modeling complexity. This work aims at exploring the tradeoffs between three approaches using TML and DL based models for RPE cell function prediction from microscopy images and at understanding the accuracy relationship between pixel-, cell feature-, and implant label-level accuracies of models. Among the three compared approaches to cell function prediction, the direct approach to cell function prediction from images is slightly more accurate in comparison to indirect approaches using intermediate segmentation and/or feature engineering steps. We also evaluated accuracy variations with respect to model selections (five TML models and two DL models) and model configurations (with and without transfer learning). Finally, we quantified the relationships between segmentation accuracy and the number of samples used for training a model, segmentation accuracy and cell feature error, and cell feature error and accuracy of implant labels. We concluded that for the RPE cell data set, there is a monotonic relationship between the number of training samples and image segmentation accuracy, and between segmentation accuracy and cell feature error, but there is no such a relationship between segmentation accuracy and accuracy of RPE implant labels. Graphical abstract: Image 1 Highlights: Investigated three methodologies to predict cell functions of RPE cell implants using non-invasive bright-field microscopy imaging. Elucidated the trade-offs between Traditional Machine Learning (TML) and Deep Learning (DL) based approaches for RPE cell function prediction. Showed that directly predicting the cell function of RPE cell implants from microscopy images is a qualitatively most suitable approach. Demonstrated that there is a monotonic relationship between segmentation accuracy and cell feature error but there is no such a relationship between segmentation and cell function prediction accuracy.