Learning-based inference of longitudinal image changes: Applications in embryo development, wound healing, and aging...

Area of Science:

• Medical imaging analysis
• Machine learning in healthcare
• Longitudinal data studies

Background:

• Longitudinal imaging is crucial for health studies and patient monitoring.
• Tracking relevant changes over time is a key challenge.
• Traditional methods often struggle to isolate meaningful variations from noise.

Purpose of the Study:

• To develop a machine learning method that automatically identifies and quantifies relevant changes in longitudinal imaging data.
• To ignore irrelevant variations and focus on the time-varying signal of interest.
• To improve the analysis of temporal mechanisms and support clinical decision-making.

Main Methods:

• A novel machine learning approach named Learning-based Inference of Longitudinal imAge Changes (LILAC) was developed.
• LILAC utilizes a convolutional Siamese architecture for pairwise image comparison and feature extraction.
• Temporal difference prediction is achieved through feature subtraction and a bias-free fully connected layer.

Main Results:

• LILAC achieved high accuracy (0.98) in predicting the temporal ordering of images.
• Predicted time differences showed strong correlations with actual biological changes (r=0.911 for embryo phase, r=0.875 for wound healing).
• LILAC models reduced root mean square error by over 40% compared to baseline methods when predicting clinical score changes.

Conclusions:

• LILAC effectively localizes and quantifies significant individual-level changes in longitudinal imaging data.
• The method offers valuable insights for understanding temporal biological processes.
• LILAC demonstrates potential for enhancing clinical decision support through advanced imaging analysis.