Screening for Cervical Myelopathy using Machine Learning Analysis of Drawing Behavior in Scientific Reports

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A new screening method for cervical myelopathy (CM) has been developed using machine learning to analyze drawing behavior. The study, which was published in Scientific Reports, used a tablet device and stylus pen to record participants’ drawing time and pressure while tracing spiral, square, and triangular waves. The machine learning algorithm demonstrated high accuracy in distinguishing CM from non-CM with a sensitivity and specificity of 76% each and an area under the curve (AUC) of 0.80. The study also found that the method had a higher sensitivity than conventional physical tests, indicating its usefulness as a screening tool.

Previous studies have focused on handwriting in neurological diseases, including those related to Parkinson’s and Alzheimer’s diseases. However, this study is the first to report on methods that use machine learning to diagnose writing disorders in CM. The method presented in this report requires no special equipment other than a commercially available tablet device and stylus pen, making it ideal for use not only in hospital settings but also in out-of-hospital settings, such as at home.

The study has some limitations, including the fact that other diseases that affect writing movements were not analyzed. Additionally, the participants in the CM group were only pre-operative patients, and the sensitivity of the method needs improvement to be used outside of the hospital. However, the study’s strengths include the fact that it can be updated sequentially to improve accuracy over time, and the addition of features related to sensation and strength could increase its accuracy in the future.

Overall, this novel classification method provides the basis for a CM screening system that could be used to facilitate the early detection and treatment of CM. By integrating features related to drawing behavior, the study obtained a model with high classification accuracy that could be used to develop disease screening systems in and out of hospital settings.

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Frequently Asked Questions (FAQs) Related to the Above News

What is cervical myelopathy?

Cervical myelopathy (CM) is a neurological condition that affects the spinal cord in the neck, causing symptoms such as weakness, numbness, and loss of coordination.

What is the screening method for CM developed in this study?

The screening method developed in this study uses machine learning to analyze drawing behavior recorded using a tablet device and stylus pen. Participants traced spiral, square, and triangular waves, and the machine learning algorithm analyzed their drawing time and pressure.

How accurate is the machine learning algorithm in distinguishing CM from non-CM?

The machine learning algorithm demonstrated a sensitivity and specificity of 76% each and an area under the curve (AUC) of 0.80 in distinguishing CM from non-CM.

How does the sensitivity of the screening method compare to conventional physical tests?

The study found that the screening method had a higher sensitivity than conventional physical tests for CM, indicating its usefulness as a screening tool.

What are the limitations of the study's screening method?

The limitations of the study's screening method include the fact that it has not been analyzed in relation to other diseases that affect writing movements, and the participants in the CM group were only pre-operative patients. The sensitivity of the method also needs improvement to be used outside of the hospital.

What are the strengths of the study's screening method?

The strengths of the study's screening method include the fact that it can be updated sequentially to improve accuracy over time, and the addition of features related to sensation and strength could increase its accuracy in the future.

Is the study's screening method practical for use outside of the hospital setting?

Yes, the study's screening method is practical for use outside of the hospital setting, as it only requires a commercially available tablet device and stylus pen.

Please note that the FAQs provided on this page are based on the news article published. While we strive to provide accurate and up-to-date information, it is always recommended to consult relevant authorities or professionals before making any decisions or taking action based on the FAQs or the news article.

Kunal Joshi
Kunal Joshi
Meet Kunal, our insightful writer and manager for the Machine Learning category. Kunal's expertise in machine learning algorithms and applications allows him to provide a deep understanding of this dynamic field. Through his articles, he explores the latest trends, algorithms, and real-world applications of machine learning, making it accessible to all.

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