2025 International Congress » Artificial Intelligence (AI) and Machine Learning

A Novel Quantitative Assessment to Evaluate Functional Impairment in Parkinson’s Disease

V. Vanchinathan, M. Farah, A. Singh, C. Zhang, N. Osei-Owusu, R. Desai, R. Palani, J. Hamkins, M. Zwernemann, S. Grill, A. Pantelyat, J. Brašić (Baltimore, USA)

Affordable Aspect of Artificial Intelligence Applied to Tremor’s Analysis Features Extraction Approach based on Archimede’s Spiral Processing

M. Abid, A. Rekik, R. Guizani, S. Laatoui, I. Rekik, S. Ben Amor (Sousse, Tunisia)

AI-Based Detection of Tremor and Dystonia Using Deep Learning Models: A Novel Approach

A. Mansour, Y. Hassan, H. Afify, A. Naguib (Cairo, Egypt)

AI-Powered Seamless Webcam Assessment for Parkinson’s Disease

J. Lapeña-Motilva, K. Coutinho-García, E. Rangel, JC. Martínez-ávila, D. Pérez-Martinez, M. Hernández Gonzalez-Monje, N. Malpica-González, A. Sánchez-Ferro (Madrid, Spain)

Analysis of OptoGait walking data using machine learning models – Using classification algorithms to assess the risk of falling in Parkinson’s patients

T. Böök, R. Ortiz, J. Honkaniemi (Tampere, Finland)

Application of Multi-Camera Videography for Detecting PD-related Gait Deficiencies

R. Griesenauer, S. Dhulipalla, R. Trosch, W. Dauer (Boston, USA)

Archimedes spiral based non-linear regression machine learning model for predicting tremor’s severity

A. Rekik, S. Laatoui, M. Abid, R. Guizani, I. Rekik, S. Ben Amor (Sousse, Tunisia)

Automatic Intelligibility Rating in Parkinson’s Disease: A Multilingual Approach

T. Thies, F. Dörr, A. König, N. Linz, M. Barbe, J. Orozco-Arroyave, J. Rusz, J. Tröger (Saarbrücken, Germany)

Characterization of Autonomic Dysfunction Profiles in Early Parkinson’s disease

Q. Yuan, F. Sarmento, V. Lavu, A. Madamangalam, J. Wong (Gainesville, USA)

Clustering and Identification of Parkinson’s Disease Severity Subtypes Using Multimodal Data and Machine Learning Approaches

H. Park, C. Youm, H. Choi, J. Hwang, M. Kim (Busan, Republic of Korea)

Deep Learning-Driven Discovery of Mitochondrial Genomic Instability in Parkinson’s Disease: A Single-Cell Multi-Omics Approach

R. Fajar, A. Ureng (Sleman, Indonesia)

Detection of Early Stage Parkinson’s Disease Using Convolutional Neural Network Models and Wearable Sensors from the Six Minute Walk Test

H. Choi, C. Youm, H. Park, J. Hwang, M. Kim (Busan, Republic of Korea)

Detection of novel acoustic biomarkers among Parkinson’s disease patients via an explainable machine learning model

K. Tsutsumi, P. Chang, S. Isfahani (San Diego, USA)

Dietary and Environmental Risk factors of Parkinson’s Disease: An integrated study combining Network Pharmacology and Molecular Docking Analyses

D. Ghosh, P. Basu, S. Mondal, S. Pal (Kolkata, India)

Dopaminometer: A Smartphone-Based Application to Predict Striatal Dopaminergic Deficit in Prodromal and Manifest Parkinson’s disease

K. Gunter, K. Groenewald, J. Klein, T. Aubourg, J. Razzaque, C. Lo, P. Ratti, L. van Hillegondsberg, J. Welch, A. Nastasa, K. Bradley, D. Mcgowan, B. Orso, P. Mattioli, M. Pardini, S. Raffa, F. Massa, D. Arnaldi, S. Arora, M. Hu (Oxford, United Kingdom)

Evaluating the Efficacy of Virtual Reality Simulations in Enhancing Balance Recovery Among Older Adults: A Systematic Review Using Statistical Modeling

M. Ali, N. Bekhit, F. Sakr, A. Hafez, M. Mohamed, O. Awadalla, A. Nagah, M. M. Elsayed (Mansoura, Egypt)

Exploring Deep Learning-Based Facial Analysis for Detecting Parkinsonism

M. Kameyama, Y. Umeda-Kameyama, H. Umegaki, R. Sengoku, K. Iijima, T. Matsubara, Y. Osakada, Y. Izumi (Tokyo, Japan)

Exploring the Impact of Delayed Neural Feedback on Motor Performance: A Meta-Analysis of Robotic Balance Training Interventions

M. Elsayed, A. Abdelsalhin, M. Mustafa, N. Elmestkawy, O. Hafez, N. Abdeltawab, M. Alomari, M. M. Elsayed (Mansoura, Egypt)

Feasibility of AI-Assisted Screening for Early Parkinson’s in Resource-Limited Settings

F. Zaier, M. Zribi (Tunis, Tunisia)

From Explainable AI to Biomarkers: Identifying Disease-Related Brain Regions in Spinocerebellar Ataxia Type 3

P. Wegner, M. Ferreira, J. Theisen, T. Klockgether, J. Faber (Bonn, Germany)

Harnessing Machine Learning to Predict Balance Recovery: A Systematic Review of Training Protocols in Neurological Disorders

M. Elsayed, D. W. Ismail, H. Elshazly, Y. M.HUSSEINY, S. Elrobeigi, Y. Hamdi, M. M. Elsayed (Mansoura, Egypt)

Improved Decision-Making for In-Hospital Medication Management in Parkinson’s Disease

R. Diaz-Rincon, M. Liang, A. Ramirez-Zamora, B. Shickel (Gainesvile, USA)

Integration of Multivariate Time Series Analysis in Assessing Balance Control: A Comprehensive Review of Current Research

M. Ali, D. W. Ismail, H. Elshazly, Y. M.HUSSEINY, S. Elrobeigi, Y. Hamdi, M. Abouelseoud, H. Abdelbar, H. Khabiry, M. M. Elsayed (Giza, Egypt)

Interpretable Video Analysis for Movement Disorders, A Case Study of Essential Tremor vs. Cortical Myoclonus

R. Martínez-García-Peña, L. Koens, M. Tijssen, G. Azzopardi (Groningen, Netherlands)

Joint Prediction of Motor and Non-motor Deep Brain Stimulation Outcomes using Quantitative Susceptibility Mapping

A. Roberts, S. Akkus, M. Spadaccia, C. Tozlu, D. Romano, P. Spincemaille, Y. Wang, B. Kopell (Ithaca, USA)

Machine Learning Analysis of DTI-Derived Free Water Reveals Distinct Parkinson’s Disease Subtypes: A Two-Year Longitudinal Study

A. Vijayakumari, H. Fernandez, B. Walter (Cleveland, USA)

Machine Learning Model using Eye Movements for the Differential Diagnosis of iPD and Atypical Parkinsonian Syndromes

A. Sekar, D. Kaski (London, United Kingdom)

Multimodal and Agentic Simulated AI Patients for Bridging the gap in Parkinson Disease Education

L. Okar, M. Fani (St. Louis, USA)

Parkinson’s disease motor phenotypes delineated by pallidal and subthalamic neurophysiology and machine learning

V. Lavu, P. Coutinho, J. Hilliard, K. Foote, C. de Hemptinne, J. Wong, K. Johnson (Gainesville, USA)

Predicting Parkinson’s Disease Motor Progression Using Clinical and Digital Data

T. Aubourg, K. Gunter, C. Lo, J. Welch, K. Groenewald, J. Klein, J. Razzaque, L. van Hillegondsberg, PL. Ratti, A. Nastasa, G. Auld, R. Mccomish, A. King, K. Chowdhury, N. Vijiaratnam, C. Girges, T. Foltynie, S. Arora, M. Hu (Sheffield, United Kingdom)

Quantifying Bradykinesia in Real-world Practice: A Clinician-friendly Video Analysis Tool for Parkinson’s Disease

Z. Xu, Y. Tang, J. Wang (Shanghai, China)

Resolution Dependence of Machine Learning for Differential Diagnosis of Parkinsonian Eye Movements

O. Bredemeyer, S. Patel, J. Fitzgerald, C. Antoniades (Oxford, United Kingdom)

Towards automated deep brain stimulation programming using neurophysiology and artificial intelligence

V. Lavu, J. Cagle, T. de Araujo, K. Johnson, C. de Hemptinne, J. Wong (Gainesville, USA)

Traditional Deep Brain Stimulation Programming versus Automated Image-Guided Algorithm in Patients with Parkinson’s Disease

H. Maghzi, C. Kim, S. Worthge, C. Malatt, M. Tagliati (Los Angeles, USA)

Unsupervised Analysis and Clustering of 3D Parkinsonian Gait

S. Lyu, KD. Kim, T. Dunn (Durham, USA)

Using Chronic DBS Brain Sensing Data to Develop an AI-based Engine for Clinical Insights

E. Fehrmann, A. Nourmohammadi, R. Molina, C. Zarns, M. Case, A. Becker, R. Raike (Minneapolis, USA)

Using large language models to assess dynamics in semantic memory search of patients with Parkinson’s disease

F. Toro Hernández, R. Cabral-Carvalho, N. Mendes Pellegrino, G. Paris-Colombo, A. Bontempo, A. Sena, H. Salmazo-Silva, M. Carthery-Goulart (São Paulo, Brazil)

Using R and Python for Kinematic Analysis in Robotic-Assisted Balance Training: A Review of Methodologies and Outcomes

M. Ali, Z. Hegazy, K. Ahmed, O. Sabry, S. Elsenbawy, G. Abozeid, M. M. Elsayed (Mansoura, Egypt)

Wearable-based Virtual Motor Exam enables remote tracking of motor symptom progression in early-stage Parkinson’s disease

KC. Ho, C. Serrano Amenos, S. Li, K. Kowahl, E. Rainaldi, L. Evers, M. Meinders, W. Marks, R. Kapur, B. Bloem, S. Shin (Dallas, USA)