In today’s world, technological advancements in the medical field have
revolutionized healthcare, dramatically enhancing diagnosis, treatment, and patient
care. For example, AI and ML algorithms can now precisely analyze and observe
medical images. The quality of life of individuals with Essential Tremors (ET) is
significantly affected, and Focused Ultrasound (FUS) has become a viable noninvasive therapeutic option. However, precision targeting and customized therapy
regimens are necessary for maximizing FUS treatment for ET. To improve the
effectiveness of FUS in ET therapy, this work investigates the integration of Artificial
Intelligence (AI) and Machine Learning (ML). In order to assess patient data, enhance
imaging precision, and customize therapy parameters, we implemented cutting-edge AI
algorithms. According to research, utilizing AI and ML in FUS can transform ET
management and provide a more efficient and personalized treatment choice. The
creation of real-time AI tools and additional validation will be the main goals of future
research. For instance, imaging processes can be performed using computer-assisted
interpretation without human intervention and in a more accurate way. The quality of
life of people affected by Essential Tremors (ET) is significantly low, and thus,
Focused Ultrasound (FUS) has emerged as a promising non-invasive treatment.
However, precision targeting and bespoke therapeutic approaches must be employed to
bridge any resultant gaps in FUS treatment for ET. This work investigates ways in
which Artificial Intelligence (AI) and Machine Learning (ML) can be employed to
enhance the efficacy of FUS treatment in ET therapy.
Keywords: Artificial Intelligence (AI), Essential Tremors (ET), Focused Ultrasound (FUS), Machine Learning (ML), Precision targeting.
