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Comparison of Artificial Intelligence based approaches to cell function prediction.

Sarala Padi¹, Petru Manescu¹, Nicholas Schaub²

¹ITL, National Institute of Standards & Technology, Gaithersburg, MD, USA.

Informatics in Medicine Unlocked

|September 1, 2020

Summary

This summary is machine-generated.

Predicting Retinal Pigment Epithelium (RPE) cell functions using AI models is crucial for stem cell therapies. Direct prediction from microscopy images shows slightly higher accuracy than indirect methods, with clear relationships between training data, segmentation accuracy, and feature error.

Keywords:

Age-related macular degeneration Cell function prediction Cell segmentation Deep learning Retinal Pigment Epithelium Cell Trans-Epithelial Resistance Vascular Endothelial Growth Factor

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Area of Science:

Biomedical Engineering
Artificial Intelligence in Medicine
Cell Biology

Background:

Clinical deployment of stem cell therapies requires predicting Retinal Pigment Epithelium (RPE) cell functions non-invasively.
Artificial Intelligence (AI) models, including Traditional Machine Learning (TML) and Deep Learning (DL), can predict cell functions from microscopy images.

Purpose of the Study:

To explore the tradeoffs between TML and DL models for RPE cell function prediction.
To understand the accuracy relationships between pixel-, cell feature-, and implant label-level predictions.
To compare direct versus indirect (segmentation/feature engineering) approaches for cell function prediction.

Main Methods:

Comparison of five TML models and two DL models for RPE cell function prediction.
Evaluation of models with and without transfer learning.
Quantification of relationships between segmentation accuracy, training samples, cell feature error, and implant label accuracy.

Main Results:

The direct approach to cell function prediction from images yielded slightly higher accuracy than indirect approaches.
A monotonic relationship exists between the number of training samples and image segmentation accuracy.
A monotonic relationship was observed between segmentation accuracy and cell feature error, but not between segmentation accuracy and RPE implant label accuracy.

Conclusions:

Direct prediction from microscopy images is a viable and slightly more accurate method for RPE cell function prediction.
Model performance is influenced by the number of training samples and intermediate feature accuracy.
Further research is needed to optimize the prediction of RPE implant labels.