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Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

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Machine learning approaches based on fibroblast morphometry do not predict ALS.

Evan Woo¹, Kirsten Bredvik¹, Bangyan Liu¹

¹Feil Family Brain and Mind Research Institute, Weill Cornell Medicine, New York, NY, USA.

Neurobiology of Aging

|July 20, 2023

Summary

This summary is machine-generated.

Researchers explored if skin cell features could serve as biomarkers for amyotrophic lateral sclerosis (ALS). While cell morphology accurately predicted stress, it did not reliably identify ALS disease in patients.

Keywords:

Amyotrophic lateral sclerosis Biomarker Fibroblast Immunocytochemistry Live Imaging Machine learning Morphometry

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Last Updated: Jul 22, 2025

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Murine Dermal Fibroblast Isolation by FACS

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Published on: January 7, 2016

Area of Science:

Biomedical research
Neuroscience
Cell biology

Background:

Amyotrophic lateral sclerosis (ALS) is a progressive neuromuscular disease with significant unmet medical needs.
Development of reliable biomarkers is crucial for early diagnosis, treatment strategies, and clinical trial optimization in ALS.
Preliminary studies suggested potential morphometric biomarkers in ALS patient-derived fibroblasts, but required validation in larger cohorts.

Purpose of the Study:

To investigate the utility of fibroblast morphometric features as potential biomarkers for amyotrophic lateral sclerosis (ALS).
To assess the predictive power of cellular morphology and organelle characteristics in distinguishing ALS patients from healthy controls.
To evaluate the performance of machine learning models in classifying disease status based on fibroblast data.

Main Methods:

Analysis of morphometric features of key organelles (mitochondria, endoplasmic reticulum, lysosomes) and proteins (TAR DNA-binding protein 43, Ras GTPase-activating protein-binding protein 1, heat-shock protein 60) in 443 human fibroblast lines.
Imaging of fibroblasts at baseline and under various stress perturbations.
Application of machine learning algorithms to predict stress states and disease classification.

Main Results:

Machine learning models achieved high accuracy (ROC-AUC ~0.99) in predicting cellular responses to stress perturbations.
Predictive accuracy for distinguishing ALS patient groups or clinical features was limited (ROC-AUC 0.58-0.64).
Multivariate models demonstrated efficacy in classifying stress conditions but not disease status.

Conclusions:

Fibroblast morphometry can accurately predict cellular responses to different stressors.
Current morphometric analyses of patient-derived fibroblasts are insufficient for developing reliable biomarkers for amyotrophic lateral sclerosis (ALS).
Further research is needed to identify robust biomarkers for ALS diagnosis and management.