ArticleSensors (Basel, Switzerland)2024
Optimizing Rare Disease Gait Classification through Data Balancing and Generative AI: Insights from Hereditary Cerebellar Ataxia.
Article in Sensors (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 63 papers, 2 of them syntheses that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
63 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Explainable artificial intelligence for gait analysis: advances, pitfalls, and challenges - a systematic review.Frontiers in bioengineering and biotechnology · 2025Pooled it
- Gait assessment using wearable technologies in neurological disorders: a rapid umbrella.Neurodegenerative disease managementPooled it
- Effects of very early exercise on inflammatory markers and clinical outcomes in patients with ischaemic stroke- a randomized controlled trial.BMC neurology · 2025Trial
- A Hierarchical Machine Learning-Based Framework for Clinical Decision Support in Foot Orthosis Prescription: Algorithm Development and Validation Study.JMIR medical informatics · 2026Observational
- Minimal Sensor Configuration for Human Activity Recognition in Patients With Hip Osteoarthritis: Proof-of-Concept Study.JMIR biomedical engineering · 2026Article
- Deep Learning-Based Automated Clinical Gait Assessment From Kinematic Data in People With Stroke: Development and Validation Study.JMIR rehabilitation and assistive technologies · 2026Article
- NeuroSift for Task-Aware Quality Assurance of Multimedia Data in Remote Parkinson Disease Assessment: Machine Learning Model Development and Validation Study.Journal of medical Internet research · 2026Article
- An Indoor Accessibility Assessment Framework Based on Multimodal Sensing and Explainable Machine Learning: A Case Study of a Tactile Museum for People with Visual Impairments.Sensors (Basel, Switzerland) · 2026Article
- Handwriting as a Biomarker for Early Detection of Parkinson's and Alzheimer's Diseases: A Comprehensive Guide for Researchers.Sensors (Basel, Switzerland) · 2026Review
- Capsule-enhanced hierarchical vision transformers for rare disease classification from medical images.Scientific reports · 2026Article
- A Joint-Level Hybrid Framework for Gait Analysis Using Camera-IMU Fusion and LSTM-Based Temporal Correction.Sensors (Basel, Switzerland) · 2026Article
- Measurement fidelity, not model complexity, governs individual cognitive trajectory prediction in Parkinson's disease.NPJ digital medicine · 2026Article
- Comprehensive Evaluation of Gait Analysis and Kinematics in Adult Degenerative Scoliosis Using Wearable Motion Capture Technologies.Sensors (Basel, Switzerland) · 2026Article
- Meta-heuristic and machine learning based functional capacity prediction using gait parameters in patients with heart failure.Scientific reports · 2026Article
- Multidimensional modeling of biological aging: integrating gait, eye movement, rest-state functional connectivity, and plasma biomarkers in non-dementia older adults.The journal of prevention of Alzheimer's disease · 2026Article
- Artificial Intelligence in Rare Diseases: Workflow-Integrated Precision Kidney Care.Clinics and practice · 2026Review
- Biomechanical Modeling and Analysis of the Lower-Limb Musculoskeletal System for Hemiplegia: A Pilot Study.Sensors (Basel, Switzerland) · 2026Article
- Cross-Dataset Evaluation of an Automated Video-Based Model for Detecting Tardive Dyskinesia Using the Clinician's Tardive Inventory: Validation Study.JMIR mental health · 2026Article
- Multidimensional balance assessment and machine learning gait stability prediction in elderly women.Scientific reports · 2026Article
- Centering the marginalized: AI-driven strategies for advancing health equity in rare disease care.Patterns (New York, N.Y.) · 2026Review
3 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
12 authors.
Funding
Abstract
The interpretability of gait analysis studies in people with rare diseases, such as those with primary hereditary cerebellar ataxia (pwCA), is frequently limited by the small sample sizes and unbalanced datasets. The purpose of this study was to assess the effectiveness of data balancing and generative artificial intelligence (AI) algorithms in generating synthetic data reflecting the actual gait abnormalities of pwCA. Gait data of 30 pwCA (age: 51.6 ± 12.2 years; 13 females, 17 males) and 100 healthy subjects (age: 57.1 ± 10.4; 60 females, 40 males) were collected at the lumbar level with an inertial measurement unit. Subsampling, oversampling, synthetic minority oversampling, generative adversarial networks, and conditional tabular generative adversarial networks (ctGAN) were applied to generate datasets to be input to a random forest classifier. Consistency and explainability metrics were also calculated to assess the coherence of the generated dataset with known gait abnormalities of pwCA. ctGAN significantly improved the classification performance compared with the original dataset and traditional data augmentation methods. ctGAN are effective methods for balancing tabular datasets from populations with rare diseases, owing to their ability to improve diagnostic models with consistent explainability.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.