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Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

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Machine Learning Use for Prognostic Purposes in Multiple Sclerosis.

Ruggiero Seccia¹, Silvia Romano², Marco Salvetti^2,3

¹Department of Computer, Control and Management Engineering "Antonio Ruberti", Sapienza University of Rome, 00185 Rome, Italy.

Life (Basel, Switzerland)

|February 10, 2021

Summary

This summary is machine-generated.

Predicting multiple sclerosis (MS) progression is challenging. Machine learning models show promise for personalizing MS treatment by forecasting disease course, though clinically usable tools are still under development.

Keywords:

disease progression machine learning multiple sclerosis prognostication

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Area of Science:

Neurology
Medical Informatics

Background:

Multiple sclerosis (MS) exhibits a relapsing-remitting phase that can evolve into a secondary progressive form.
Predicting individual MS disease course remains difficult, hindering personalized treatment strategies.
Effective MS therapies exist but carry potential adverse effects, necessitating risk-stratified treatment approaches.

Purpose of the Study:

To review recent studies employing machine learning (ML) for predicting multiple sclerosis disease course.
To explore the potential of ML algorithms in identifying prognostic factors for MS progression.
To assess the current state and future prospects of ML-based prognostic models in MS.

Main Methods:

Review of recent scientific literature focusing on ML applications in MS prognosis.
Analysis of studies utilizing clinical data, alone or combined with other data types, for prognostic model development.
Comparison of various ML algorithms applied to predict MS disease trajectory.

Main Results:

Several studies have explored ML algorithms to predict MS disease course using diverse datasets.
No ML-based prognostic model is currently validated for clinical use in individual MS patients.
Knowledge in this field is accumulating, indicating potential for future robust predictive tools.

Conclusions:

Machine learning approaches are being investigated to improve the prediction of multiple sclerosis progression.
Accurate prognostic models could enable tailored therapeutic interventions, optimizing treatment efficacy and safety.
Further research and validation are needed to develop clinically applicable ML tools for MS management.