2026 International Congress » Artificial Intelligence (AI) and Machine Learning

A biomarker of Huntington’s disease progression derived from deep neural speech representations

C. Le Moine, AC. Bachoud-Lévi, R. Massart, R. Louiset (Creteil, France)

A Deep Learning Model-based Screening System for Nystagmus in Central Vertigo

H. Takeda, K. Eguchi, S. Shirai, H. Yaguchi, K. Fujiwara, I. Yabe (Sapporo, Japan)

A Diffusion MRI Neurodegeneration Index for Lewy Body Dementia & Alzheimer’s disease

S. Chiu, R. Chen, W. Wang, J. Desimone, A. Barmpoutis, M. Armstrong, D. Vaillancourt (Scottsdale, USA)

Accessible Assessment of Ataxia Severity Across Diverse Populations: Integrating Large Datasets, Machine-Learning, and Online Testing

T. Gilad, A. Lithwick Algon, S. Yamnitsky, P. Ponger, J. Hausdorff, W. Saban (Tel Aviv, Israel)

AI guided presurgical risk stratification for deep brain stimulation surgery

J. Purks, J. Dwarampudi, R. Hu, T. Banerjee, J. Wong (Gainesville, USA)

An AI-Based Wearable Device Application To Assess Motor Skills Anomalies Associated with Parkinson’s Disease

A. Ganu (San Jose, USA)

An International Consensus Framework for Computer Vision-ready acquisition and reporting: Toward Measurement-grade video in Neurology

J. Alty, G. Amprimo, J. Shin, M. Wuehr, M. Novotny, M. Merello, M. de Koning-Tijssen,, H. Haberfehlner, S. Relton, D. Wong, J. Taeger, A. Zwergal, S. Walther, B. Taati, R. Roemmich, C. Ferraris, M. Mckeown, A. Gupta, R. Li, M. Friedrich (Hobart, Australia)

Artificial Intelligence-Based Voice Analysis for Early Detection of Parkinson’s Disease: A Systematic Review

O. Uwishema (Kigali, Rwanda)

Automated CRST Spiral Drawing Scoring Using a Multimodal Vision-language model: Development and Validation in a Large Essential Tremor Cohort

C. Reddy, A. Zoana, M. Lotia, N. Reddy, A. Ahmed, J. Ledoux, P. Nonat, R. Guevarra (Orlando, USA)

Automated Facial Movement Analysis for Differentiating True Hemifacial Spasm from Mimicking Facial Movements: A Pilot Study

S. Maytharakcheep, A. Saeng-Xuto, P. Songthung, D. Surangsrirat, P. Vateekul, R. Bhidayasiri, O. Phokaewvarangkul (Bangkok, Thailand)

Automatic Detection of Freezing of Gait from Simple, 2D Video Recordings in Complex Clinical Environments

A. Shakolnikov, O. Perlman, J. Hausdorff (Tel Aviv-Yafo, Israel)

Beyond Movement Amplitude: Interaction‑ and Asymmetry‑Aware Facial Blendshape Analysis in Parkinsonian Hypomimia

B. Koyak, T. Menzel, A. Spottke, J. Faber, M. Reuter, U. Wüllner, M. Botsch, A. Aziz (Dortmund, Germany)

Brain Atlas-Based Machine Learning for Parkinsonian Disorders Classification and Evaluation of Classification Performance Across Disease Duration

S. Kim, S. Oh, H. Yoo, W. Kim, J. Youn, JW. Cho (Seoul, Republic of Korea)

Classification of Fallers in Parkinson’s Disease Through Machine Learning Based Feature Analysis

SM. Kim, MK. Kim, MJ. Chung, JW. Cho, HJ. Yoo, JY. Youn (Seoul, Republic of Korea)

Deep Learning-Enhanced Multimodal MRI for Real-World Differentiation of Parkinsonian Disorders

S. Hartono, H. Ulfah, Q. Sun, C. Liu, D. Patidar, P. Seow, P. Chai, Q. Lyu, R. Chen, E. Tan, L. Chan (Singapore, Singapore)

Detecting Early and Prodromal Parkinson’s Disease in the Elderly Using Multimodality Digital Biomarkers: A Pilot Study

SP. Fan, WS. Lim, KP. Lin, YL. Chiu, CH. Lin (Taipei City, Taiwan)

Differences in Sleep and Activity Digital Phenotypes from Wrist Accelerometry in Parkinson’s Disease, At‑Risk Individuals, and Healthy Controls

I. Gerasimou, A. Moustaklis, L. Hadjileontiadis, S. Hadjidimitriou (Thessaloniki, Greece)

Digital Health, Telemedicine, and Artificial Intelligence in Parkinson’s Disease Management During COVID-19: A Narrative Review

H. Harifi (Kenitra, Morocco)

Enhancing Neurology Resident Communication of Functional Movement Disorder Using an Artificial Intelligence (AI)-Based Patient Actor

S. Sharma, M. Tanumiharja, K. Fu (Los Angeles, USA)

External Validation of a Machine Learning Classifier for Motor Progression in Parkinson’s Disease

A. Vijayakumari, R. Popov, O. Hogue, H. Fernandez, B. Walter (Cleveland, USA)

Frequency and Phenotypic Characterization of Genetic Causes of Sporadic Late-Onset Cerebellar Ataxia: insights from a Cohort of 315 patients

M. Barrat, T. Wirth (Dijon, France)

Fusion Learning with Attention-VAE Imputation for Mixed-Reality Multisensor Screening of Parkinson’s Disease

J. Krzywdziak, J. Stępień, W. Szecówka, M. Dudek, M. żbik, M. Baran, N. Bozetine, J. Sikora, M. Wójcik-Pędziwiatr, M. Rudzińska-Bar, D. Hemmerling (Warsow, Poland)

Handwriting Based Screening of Parkinson’s Disease Using Image Analysis of Drawing Tasks

A. Shih, L. Cruz-Mondragon, V. Santini, S. Panchawagh (Winston-Salem, USA)

Inter-Rater Variability in Motor Assessment Scoring: Measurement Implications for Understanding Movement Disorders in Aging

R. Singh, E. Nishat, R. Nasir, B. Bridges (St John's, Canada)

Kinematic Discriminative Component (KDC) a New Biomarker to Measure Parkinson’s Disease Severity

M. Perales, T. Sil, F. Lange, M. Reich, R. Peach (Wuerzburg, Germany)

Large Language Models for Clinical Feature Suggestion from Parkinson’s Disease Clinical Notes

GY. Lee, HY. Kwon, S. Jo, M. Choi, N. Kim, SJ. Chung (Seoul, Republic of Korea)

Linear-Time RWKV Multimodal Fusion of Smartphone Audio and Wearable Signals for Mild Cognitive Impairment Screening

D. Hemmerling, JH. Yun, J. Krzywdziak, B. Eljasiak, A. Pruszek, L. Lazarski, M. Grzeszczyk, T. Brzózka, W. Szecówka, W. średniawa, H. Lee, W. Baek, S. Yoon, M. Matuszewski (Seoul, Republic of Korea)

Machine Learning for Early Detection of Huntington’s Disease: Integrating Non-Motor Symptoms and Gender-Specific Differences

S. Rajput, P. Tiwari, S. Sinha (Delhi, India)

Machine Learning-Based Frequency-Domain Analysis of Gait Acceleration for Huntington’s Disease Detection

R. Kumar, S. Choudhary, M. Singh (Pharmacology, India)

Machine Learning-Driven Analysis of Epigenetic Signatures in Juvenile Huntington’s Disease: Investigating Histone Modification Dynamics

R. Kumar, S. Choudhary, M. Singh (Pharmacology, India)

Markerless Kinematic Analysis of Gait and Turning in Parkinson’s Disease

A. Tahara, A. Chinaglia, R. Monteiro, L. Santos, P. Santiago (Ribeirão Preto, Brazil)

Objective detection of morning bradykinesia in Parkinson’s Disease using a wearable inertial sensor

N. Caballol, A. ávila, A. Planas-Ballvé, A. Peral, P. Lombardo, A. Pérez-Soriano, A. Bayés, P. Quispe, S. Belmonte, J. Hernández-Vara, D. Cerdan, I. Cabo, N. López-Ariztegui, T. Delgado, J. Herreros-Rodríguez, D. Santos-García, C. Cores-Bartolomé, G. Jarasunas-Cardozo, J. Casanova-Mollà, C. Pérez-López (Sant Joan Despí, Spain)

Prediction of Cognitive Decline After STN-DBS in Parkinson’s Disease Using Preoperative Electroencephalography and a Deep Learning Model

K. Iwami, K. Eguchi, S. Shirai, H. Yaguchi, I. Yabe (Sapporo, Japan)

Prospective Evaluation of a Video-Based Motor Assessment Workflow: Feasibility, Usability, and Data Quality

B. Bridges, O. Hares, E. Nishat, R. Nasir, R. Singh (St. John's, Canada)

Real-World Mobile Voice Classification for Parkinson’s Disease Using Convolutional Neural Networks and Mel-Spectrogram Features

N. Leabthong, J. Sringean, T. Laosombut, N. Jeh-Voh1, Z. Win, P. Rattanajun, S. Phumphid, C. Anan, J. Meesri, S. Wekhinhiran, O. Phokaewvarangkul, P. Panyakaew, S. Maytharakcheep, P. Jagota, R. Bhidayasiri (Bangkok, Thailand)

Retrospective Video-Based Motor Assessment: Feasibility and Inter-Rater Variability in Remote Scoring

O. Hares, R. Singh, E. Nishat, R. Nasir, B. Bridges (St John's, Canada)

Safer AI Dosing Recommendations in Parkinson’s Disease Through Uncertainty-Adaptive Prediction

R. Diaz-Rincon, L. Liang, A. Ramirez-Zamora, B. Shickel (Gainesville, USA)

Smartphone Video-Based Finger Tapping Analysis Using Machine Learning for Parkinson’s Disease Classification

M. Acharya, D. Bose, N. Ghosh, A. Mukherjee (Kharagpur, India)

Towards Deep Learning for Personalized Parkinson’s Disease Tremor Segmentation and Severity Classification Using Wearable Sensors

J. Jaegerman, C. Lucasius, J. Ambrad, C. Gorodetsky (Toronto, Canada)

Translating Real-World Parkinsonian Facial Kinematics onto Diverse Digital Avatars

C. Gundler, A. Wiederhold, M. Pötter-Nerger (Hamburg, Germany)

Uncovering Sleep Neural Signatures of Parkinson’s Disease and REM Sleep Behavior Disorder from Polysomnography through Interpretable Deep Learning

S. Kim, J. Jeong, YJ. Jung (Daejoen, Republic of Korea)

Video-Based Gait Analysis for Objective Assessment of Motor Severity and Surgical Outcome in Parkinson’s Disease: A Scalable Alternative to Subjective Rating

Y. Samanci, E. Yildirim, B. Samanci, G. Kenangil, AF. Cangi, A. Zirh (Istanbul, Turkey)

Voice-Based Screening of Parkinsonian Disorders Using Acoustic Feature Analysis and Machine Learning

L. Cruz-Mondragon, A. Shih, V. Santini, S. Panchawagh (Winston Salem, USA)