## 3 Results
### Optimal Imaging Stimulant Results
The sample size of 16 participants with confirmed depressive symptoms, while providing valuable insights, is relatively small. Larger-scale studies are needed to confirm the generalizability of the ANN model across diverse populations and to ensure that the optimized parameters are effective for a wider range of patients with varying degrees of symptom severity and cognitive processing abilities. Expanding the study to include different age groups, cultural backgrounds, and mental health conditions could provide a more comprehensive understanding of the model's applicability. Additionally, this study focused exclusively on optimizing visual stimuli parameters. Future research could explore the integration of other sensory modalities, such as auditory or tactile stimuli, to enhance the multisensory engagement of EMDR therapy. Such a multisensory approach could lead to even more robust therapeutic effects by engaging additional neural pathways involved in memory processing and emotional regulation. Moreover, integrating real-time feedback mechanisms, such as EEG or heart rate variability, could enable dynamic adjustments of stimulus parameters during therapy sessions, further personalizing the experience. Finally, while this study successfully demonstrated the use of an ANN model in EMDR therapy, exploring other machine learning models, such as convolutional neural networks (CNNs) or reinforcement learning, could provide additional insights. These models may offer different advantages in processing stimulus-response relationships and could be used to optimize therapy across a broader spectrum of mental health treatments. It is also important to note that, due to the presence of various variables in the experimental results, these findings do not always hold. To achieve more refined outcomes, further research should employ a wider variety of methods and continued experimental approaches. ### Clinical Implications and Future Directions

The findings of this study provide a strong foundation for integrating ANN-based optimization into EMDR therapy protocols, with promising implications for enhancing clinical effectiveness. By adopting a data-driven approach, clinicians may be able to deliver more precise, consistent, and effective EMDR therapy that meets the unique needs of each patient. This approach represents a paradigm shift towards personalized mental health care, where treatments are continuously refined based on objective data and patient responses. Future research should aim to build on these findings by exploring additional variables and testing ANN-driven optimization in diverse clinical settings. With continued refinement, ANN-based models could be developed into accessible software tools that assist clinicians in real time, enabling them to design and implement optimized EMDR protocols with minimal technical expertise. Ultimately, this research represents an initial step toward developing intelligent therapeutic systems that blend machine learning with clinical expertise, driving forward the future of personalized and effective mental health care. [PERSON] contributed to the study concept, design, and drafting of the manuscript. [PERSON] and [PERSON] analyzed the data and wrote and edited the manuscript. All authors have read and agreed to the published version of the manuscript. This work was supported by the Gachon University research fund of 2023 (GCU-202304760001). The study was conducted in accordance with the Declaration of Helsinki and approved by the Ethics Committee of Gachon University (protocol code 1044396-202303-HR-043-01). Informed Consent Statement: Informed consent was obtained from all subjects involved in the study. All data generated or analyzed in the study are included in the article. However, not all of it has been made publicly available as it may compromise the privacy of participants. Further inquiries can be directed to the corresponding author. The authors declare no conflicts of interest. ## References

* World Health Organization (2013) World Health Organization.