Detecting Vitiligo with Machine Learning Techniques
Author Information
Author(s): Tanvir Sania, Syed Sidra Abid, Hussain Samreen, Zia Razia, Rashid Munaf, Zahid Hira
Primary Institution: Sir Syed University of Engineering and Technology, Karachi, Pakistan
Hypothesis
Can machine learning techniques improve the diagnosis of vitiligo?
Conclusion
Machine learning techniques can enhance the accuracy of vitiligo diagnosis and reduce reliance on human judgment.
Supporting Evidence
- Machine learning techniques can provide more accurate results in diagnosing vitiligo.
- Existing studies show a significant improvement in diagnostic accuracy with machine learning.
- Further research is needed to explore different datasets and feature extraction methods.
Takeaway
This study looks at how computers can help doctors find skin problems like vitiligo better than just looking at pictures.
Methodology
A systematic review of studies using machine learning techniques for vitiligo diagnosis, analyzing data from selected research articles.
Potential Biases
Some studies had a high risk of bias due to inadequate methodologies.
Limitations
The review is limited by the small number of studies and the exclusion of non-English publications.
Participant Demographics
Studies included patients aged 15 and above, with a mix of genders.
Statistical Information
P-Value
<0.05
Statistical Significance
p<0.05
Digital Object Identifier (DOI)
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