Abstract Details

Title Development of an Artificial Intelligence Framework for Accurate Diagnosis of Synucleinopathies using Clinical Features

Topic Movement Disorders

Presentation(s) P6 - Poster Session 6 (5:00 PM-6:00 PM)

Poster/Presentation
Number 16-006

Objective To evaluate the utility of clinical and functional scales from large multicenter datasets in differentiating manifest synucleinopathies using AI modeling.

Background

Synucleinopathies such as Parkinson’s disease (PD), dementia with Lewy bodies (DLB), and multiple system atrophy (MSA) have overlapping features that hinder early diagnosis. Accurate early classification is essential for timely intervention and trial readiness. Harmonized datasets like Parkinson’s Progression Markers Initiative (PPMI) and Parkinson’s Disease Biomarkers Program (PDBP) enable predictive modeling with standardized clinical scales.

Design/Methods Data from PPMI (PD) and PDBP (MSA and DLB) were analyzed. An XG-boost based classification framework was developed, incorporating multiple dimensions of synucleinopathies, including Schwab and England Activities of Daily Living (ADL), Movement Disorder Society–Unified Parkinson’s Disease Rating Scale (MDS-UPDRS) Parts I–III, the Epworth Sleepiness Scale, Montreal Cognitive Assessment (MoCA), and University of Pennsylvania Smell Identification Test (UPSIT). Models were trained and cross-validated. For per-class feature analysis, a One-vs-Rest strategy was applied, computing feature importance based on gain, which reflects the improvement in accuracy provided by each feature. Scores were normalized to sum to 1, with higher values indicating greater information gain. Model performance was evaluated using sensitivity, specificity, accuracy, and area under the curve (AUC).

Results A total of 2,009 participants were included: controls (n=721), PD (n=688), DLB (n=374), and MSA (n=68). The XG-boost based model achieved high performance, with overall accuracy of 93.3%, macro-average AUC of 0.981, and macro-average F1 score of 0.851. Class-specific sensitivity and specificity were: control (0.935, 0.973), DLB (0.902, 0.969), MSA (0.550, 0.988), PD (0.980, 0.970). Feature importance analysis highlighted Schwab and England ADL, MDS-UPDRS Part III motor scores, and MDS-UPDRS Parts II and I as the strongest predictors.

Conclusions Clinical and functional measures demonstrate strong discriminative power for differentiating synucleinopathies. These findings support the use of AI approaches to enhance early diagnosis and improve patient stratification in synucleinopathy research.

Authors/Disclosures
Juan D. Martinez Lemus, MD (The University of Texas Health Science Center at Houston) PRESENTER	Dr. Martinez Lemus has nothing to disclose.
Wajih Hassan Raza	Mr. Raza has nothing to disclose.
Renjie Hu	The institution of Renjie Hu has received research support from AIM-AHEAD.
Timothy M. Ellmore, PhD (The City College of New York)	Prof. Ellmore has nothing to disclose.
Charles Green	Charles Green has received personal compensation in the range of $500-$4,999 for serving as a Consultant for University of Texas at Austin. Charles Green has received personal compensation in the range of $500-$4,999 for serving as a Consultant for Baylor College of Medicine. Charles Green has received personal compensation in the range of $500-$4,999 for serving as a Consultant for Society of Research on Nicotine and Tobacco. Charles Green has received personal compensation in the range of $500-$4,999 for serving as a Consultant for Society for Psychophysiology. The institution of Charles Green has received research support from NIH. The institution of Charles Green has received research support from DoD. The institution of Charles Green has received research support from VA. The institution of Charles Green has received research support from Michael J. Fox Foundation. The institution of Charles Green has received research support from American Association for Cancer Research.
Claudio Soto (University of Texas Medical Branch)	Claudio Soto has received personal compensation in the range of $50,000-$99,999 for serving as a Consultant for Amprion. Claudio Soto has received personal compensation in the range of $5,000-$9,999 for serving as a Consultant for Rinri therapeutics. Claudio Soto has received personal compensation in the range of $10,000-$49,999 for serving as a Consultant for Affyxell Therapeutics. Claudio Soto has stock in Amprion. The institution of Claudio Soto has received research support from NIH. The institution of Claudio Soto has received research support from Michael J. Fox Foundation. The institution of an immediate family member of Claudio Soto has received research support from NIH. Claudio Soto has received intellectual property interests from a discovery or technology relating to health care.
Chiamaka C. Onuigbo, MD	No disclosure on file
Emily Tharp, MD	Dr. Tharp has nothing to disclose.
Robert Ritter III	Mr. Ritter has nothing to disclose.
Xin Fu, PhD	Dr. Fu has nothing to disclose.
Mya C. Schiess, MD, FAAN (Univ of Texas-Houston Med School)	Dr. Schiess has nothing to disclose.

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