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Predicting CTS Diagnosis and Prognosis Based on Machine Learning Techniques.

Marwa Elseddik^1,2, Reham R Mostafa², Ahmed Elashry³

¹Department of the Robotics and Internet Machines, Faculty of Artificial Intelligence, Kafrelsheikh University, Kafr El Sheikh 33516, Egypt.

Diagnostics (Basel, Switzerland)

|February 11, 2023

Summary

This summary is machine-generated.

Machine learning models accurately classify carpal tunnel syndrome (CTS) severity and predict treatment outcomes. This approach aids in determining optimal interventions, potentially avoiding unnecessary surgeries for CTS patients.

Keywords:

Boston Carpal Tunnel Syndrome Questionnaire (BCTQ)carpal tunnel syndrome (CTS)machine learning (ML)nerve condition studies (NCS)ultrasonography (US)

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Area of Science:

Medical research
Biomedical engineering
Clinical informatics

Background:

Carpal tunnel syndrome (CTS) is caused by median nerve compression, necessitating accurate severity assessment for effective treatment.
Current research in machine learning (ML) for CTS is limited, particularly in classifying severity using comprehensive clinical data.
Existing ML models primarily focus on CTS diagnosis, leaving a gap in severity classification and treatment prediction.

Purpose of the Study:

To develop and evaluate novel ML models for classifying CTS severity (mild, moderate, severe).
To create an ML model predicting patient improvement after ultrasonography (US)-guided median nerve hydrodissection.
To analyze the efficacy of the hydrodissection procedure using statistical tests and inform clinical decision-making.

Main Methods:

Collected data from 80 CTS patients and 80 patients with symptom-overlapping conditions.
Utilized ML algorithms to classify CTS severity and predict post-injection improvement at 1, 3, and 6 months.
Employed statistical tests including significance, Spearman's correlation, and two-way ANOVA to assess treatment effects.

Main Results:

The CTS severity classification model achieved high performance: 0.955% accuracy, 0.963% precision, and 0.919% recall.
The predictive model for post-injection improvement showed increasing accuracy over time: 0.877% (1 month), 0.901% (3 months), and 0.912% (6 months).
The 6-month prognosis prediction demonstrated superior performance compared to 1 and 3-month predictions, indicating sustained treatment effect.

Conclusions:

ML models can effectively classify CTS severity and predict treatment outcomes, offering valuable clinical decision support.
US-guided median nerve hydrodissection shows promising results, with ML models accurately reflecting patient improvement over time.
These data-driven tools can help optimize patient management, potentially reducing the need for surgical interventions in carpal tunnel syndrome.