會議議程

Executive Summary：
Dr. Sung-Pin Fan is currently an attending physician in the Department of Neurology at National Taiwan University Hospital (NTUH). He completed his comprehensive neurology residency and fellowship training at NTUH. Dr. Fan earned his Master’s degree from the Institute of Clinical Medicine at National Taiwan University (NTU) and is currently pursuing a Doctoral degree in the Program for Precision Health and Intelligent Medicine at NTU. His research interests focus on Parkinson’s disease, Wilson’s disease, and the development of neuroimaging biomarkers, as well as the integration of machine learning in clinical neurology. Dr. Fan has authored multiple peer-reviewed articles specializing in neuroimaging and digital biomarkers.

Lecture Abstract：
Parkinson’s disease (PD) is a progressive neurodegenerative condition for which no definitive cure currently exists. However, early diagnosis significantly improves long-term outcomes. When identified in its early stages, patients can benefit from timely pharmacological initiation and proactive lifestyle modifications, which are essential for preserving functional independence and quality of life.
Despite its clinical importance, early detection remains a significant challenge. For general practitioners and physicians outside neurology, identifying the subtle motor symptoms of early-stage PD is difficult, often leading to delayed referrals. Fortunately, the evolution of mobile technology and computer science is bridging this gap. The emergence of digital biomarkers—quantifiable physiological and behavioral data collected via ubiquitous devices—offers a transformative approach to continuous neurological monitoring.
By leveraging high-resolution cameras to capture motor patterns and utilizing sophisticated algorithms for vocal analysis, these digital tools provide objective and non-invasive methods for screening. Such technologies allow for the detection of subtle symptoms that may elude traditional clinical observations, thereby mitigating diagnostic challenges for non-specialists.
In this lecture, the speaker will provide an overview of the digital biomarker landscape in movement disorders. The session will specifically highlight advancements in digital biomarkers for PD. On the other hand, the speaker will also share currently developed smartphone-based multimodal biomarkers for PD. Attendees will gain insights into how these tools are reshaping diagnostic precision and paving the way for personalized, data-driven Parkinson’s care.

Executive Summary：
Yi-Chien Liu is a Taiwanese neurologist focused on early detection of Alzheimer’s disease using speech analytics and multimodal biomarkers. He earned his MD at China Medical University (Taiwan) and PhD in geriatric behavioral neurology at Tohoku University (Japan). Recent work highlights linguistic features combined with biomarkers for screening and cholinergic white matter hyperintensities linked to dementia severity in AD patients.

Lecture Abstract：
Early detection of Alzheimer’s disease (AD) remains a critical clinical challenge, particularly in early-stage where symptoms are often subtle or misattributed to other causes. Current screening tools, such as the Montreal Cognitive Assessment (MoCA), frequently lack the sensitivity required for early-stage diagnosis. In the future, we will delve into the "voiceprints" revealed by Explainable AI (XAI), identifying key linguistic markers such as reduced semantic informativeness, increased use of vague pronouns, and subtle changes in pause duration. Looking ahead, integrating these digital markers with emerging plasma biomarkers represents the next frontier in precision screening. We envision a future tiered workflow in which voice AI serves as a cost-effective "digital vital sign" in primary care. This approach would effectively triage high-risk individuals for confirmatory plasma testing, reducing reliance on expensive PET scans for initial screening and streamlining the path to timely disease-modifying therapies.

Executive Summary：
陳右緯醫師畢業於國立台灣大學醫學系，並取得國立中央大學資訊工程研究所博士學位。曾赴美國麻州總醫院腦中風中心擔任研究員，並歷任台大醫院神經部兼任主治醫師、台大醫學院神經部兼任助理教授。現任聯新國際醫院副院長暨神經醫學中心主任，長期投入腦中風醫療領域，致力推動腦血管疾病之精準診療與急重症照護體系發展。
Dr. Chen graduated from the School of Medicine at National Taiwan University and earned his Ph.D. in Computer Science and Information Engineering from National Central University. He previously served as a research fellow at the Stroke Center of Massachusetts General Hospital in the United States, and has also held positions as an adjunct attending physician in the Department of Neurology at National Taiwan University Hospital and an adjunct assistant professor in the Department of Neurology at the National Taiwan University College of Medicine.
Dr. Chen is currently the Vice Superintendent of Landseed International Hospital and Director of the Neurological Medical Center. He has long been dedicated to the field of stroke care, promoting precision diagnosis and treatment of cerebrovascular diseases, as well as advancing the development of acute and critical care systems.

Lecture Abstract：
隨著穿戴式科技的快速發展，其在神經復健領域的應用日益受到關注，特別是在腦中風後功能恢復的監測與訓練方面展現出相當潛力。透過感測器、智慧裝置與數據分析技術，臨床醫師得以更精準地評估患者的活動能力與復健進展，同時提升復健訓練的個別化與即時回饋。本講座將介紹穿戴式科技於腦中風復健相關臨床研究成果，並分享其在臨床實務中的應用經驗與未來發展趨勢，期望促進科技與神經復健整合，提升腦中風患者長期功能恢復與生活品質。

With the rapid development of wearable technology, its application in the field of neurorehabilitation has received increasing attention, particularly in monitoring and training functional recovery after stroke. Through various sensors, smart devices, and data analytics technologies, clinicians are able to more accurately assess patients’ motor abilities and rehabilitation progress, while enhancing the personalization and real-time feedback of rehabilitation training. This lecture will introduce recent clinical research on the application of wearable technology in stroke rehabilitation and share practical experiences from clinical settings, as well as future development trends. It aims to promote the integration of technology and neurorehabilitation, ultimately improving long-term functional recovery and quality of life for patients with stroke.