Journal of neuroscience methods · 2025
Integrating data mining with transcranial focused ultrasound to refine neuralgia treatment strategies.
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Khan Muhammad Attique, Alsenan Shrooq, Algamdi Shabbab Ali, Aldossary Haya, Raju K Narasimha, Baili Jamel, Saleem Muhammad Asim
Аннотация
BACKGROUND: Neuralgia and other neuropathic pain are difficult to treat owing to their complicated etiology and a wide variety of responses to treatment. The novel neuromodulation technology transcranial focused ultrasound (tFUS) has intriguing implications in targeted non-invasive brain stimulation. Patient-specific variables and neurological processes must be better understood to enhance tFUS for personalized therapy.
METHODS: In this research, a Machine Learning based Transcranial Focused Ultrasound Personalized Model (ML-tFUSPM) has been proposed to treat neuralgia by combining tFUS with data mining for personalized therapy. Data mining algorithms can examine patient demographics, pain factors, imaging data, and therapy outcomes to uncover response patterns and treatment predictors. According to these results, tFUS may be tailored to each patient by targeting brain regions involved in pain perception and control.
RESULTS: Initial studies show that data-driven models and tFUS enhance therapeutic efficacy, side effects, and accuracy. This collaborative endeavor uses data analytics and neuromodulation to customize neuralgia treatment. The new model's emphasis on targeted treatments and predictive analytics gives clinicians evidence-based tools to manage pain more effectively and personally, which might transform the industry.
COMPARATIVE ANALYSIS: The experimental results show that the proposed method has a high accuracy ratio of 97 % compared to other methods.
CONCLUSION: According to this study, computational principles and cutting-edge technology may lead to game-changing neurology and pain management advances.
METHODS: In this research, a Machine Learning based Transcranial Focused Ultrasound Personalized Model (ML-tFUSPM) has been proposed to treat neuralgia by combining tFUS with data mining for personalized therapy. Data mining algorithms can examine patient demographics, pain factors, imaging data, and therapy outcomes to uncover response patterns and treatment predictors. According to these results, tFUS may be tailored to each patient by targeting brain regions involved in pain perception and control.
RESULTS: Initial studies show that data-driven models and tFUS enhance therapeutic efficacy, side effects, and accuracy. This collaborative endeavor uses data analytics and neuromodulation to customize neuralgia treatment. The new model's emphasis on targeted treatments and predictive analytics gives clinicians evidence-based tools to manage pain more effectively and personally, which might transform the industry.
COMPARATIVE ANALYSIS: The experimental results show that the proposed method has a high accuracy ratio of 97 % compared to other methods.
CONCLUSION: According to this study, computational principles and cutting-edge technology may lead to game-changing neurology and pain management advances.