Optimizing Rare Disease Gait Classification through Data Balancing and Generative AI: Insights from Hereditary Cerebellar Ataxia

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 generat...

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Published inSensors (Basel, Switzerland) Vol. 24; no. 11; p. 3613
Main Authors Trabassi, Dante, Castiglia, Stefano Filippo, Bini, Fabiano, Marinozzi, Franco, Ajoudani, Arash, Lorenzini, Marta, Chini, Giorgia, Varrecchia, Tiwana, Ranavolo, Alberto, De Icco, Roberto, Casali, Carlo, Serrao, Mariano
Format Journal Article
LanguageEnglish
Published Switzerland MDPI AG 01.06.2024
MDPI
Subjects
Adult
Aged
Algorithms
Artificial Intelligence
Ataxia
Cerebellar ataxia
Cerebellar Ataxia - diagnosis
Cerebellar Ataxia - genetics
Cerebellar Ataxia - physiopathology
Classification
data augmentation
data balancing
Data collection
Datasets
Diseases
Female
Fourier transforms
Gait
Gait - physiology
gait analysis
Gait Analysis - methods
generative artificial intelligence
Humans
Kinematics
Male
Medical research
Medicine, Experimental
Middle Aged
Range of motion
Rare Diseases
Software
Walking
Italy
inertial measurement unit
rare diseases
cerebellar ataxia
data augmentation
conditional tabular generative artificial network
data balancing
synthetic minority oversampling technique
generative artificial network
generative artificial intelligence
gait analysis
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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.
AbstractList 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.
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.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.
Audience Academic
Author Castiglia, Stefano Filippo
Ranavolo, Alberto
Varrecchia, Tiwana
Ajoudani, Arash
Casali, Carlo
Trabassi, Dante
Lorenzini, Marta
Marinozzi, Franco
Bini, Fabiano
De Icco, Roberto
Serrao, Mariano
Chini, Giorgia
AuthorAffiliation 2 Department of Brain and Behavioral Sciences, University of Pavia, 27100 Pavia, Italy; roberto.deicco@unipv.it
3 Department of Mechanical and Aerospace Engineering, Sapienza University of Rome, 00184 Rome, Italy; fabiano.bini@uniroma1.it (F.B.); franco.marinozzi@uniroma1.it (F.M.)
5 Department of Occupational and Environmental Medicine, Epidemiology and Hygiene, INAIL, Monte Porzio Catone, 00078 Rome, Italy; g.chini@inail.it (G.C.); t.varrecchia@inail.it (T.V.); a.ranavolo@inail.it (A.R.)
6 Headache Science & Neurorehabilitation Unit, IRCCS Mondino Foundation, 27100 Pavia, Italy
7 Movement Analysis Laboratory, Policlinico Italia, 00162 Rome, Italy
1 Department of Medical and Surgical Sciences and Biotechnologies, “Sapienza” University of Rome, 04100 Latina, Italy; dante.trabassi@uniroma1.it (D.T.); carlo.casali@uniroma1.it (C.C.); mariano.serrao@uniroma1.it (M.S.)
4 Department of Advanced Robotics, Italian Institute of Technology, 16163 Genoa, Italy; arash.ajoudani@iit.it (A.A.); marta.lore
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Issue 11
Keywords inertial measurement unit
rare diseases
cerebellar ataxia
data augmentation
conditional tabular generative artificial network
data balancing
synthetic minority oversampling technique
generative artificial network
generative artificial intelligence
gait analysis
Language English
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These authors contributed equally to this work.
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SSID ssj0023338
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Snippet The interpretability of gait analysis studies in people with rare diseases, such as those with primary hereditary cerebellar ataxia (pwCA), is frequently...
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proquest
gale
pubmed
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SourceType Open Website
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Aggregation Database
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Enrichment Source
StartPage 3613
SubjectTerms Adult
Aged
Algorithms
Artificial Intelligence
Ataxia
Cerebellar ataxia
Cerebellar Ataxia - diagnosis
Cerebellar Ataxia - genetics
Cerebellar Ataxia - physiopathology
Classification
data augmentation
data balancing
Data collection
Datasets
Diseases
Female
Fourier transforms
Gait
Gait - physiology
gait analysis
Gait Analysis - methods
generative artificial intelligence
Humans
Kinematics
Male
Medical research
Medicine, Experimental
Middle Aged
Range of motion
Rare Diseases
Software
Walking
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Title Optimizing Rare Disease Gait Classification through Data Balancing and Generative AI: Insights from Hereditary Cerebellar Ataxia
URI https://www.ncbi.nlm.nih.gov/pubmed/38894404
https://www.proquest.com/docview/3067439083
https://www.proquest.com/docview/3070802002
https://pubmed.ncbi.nlm.nih.gov/PMC11175240
https://doaj.org/article/5f0ef8f78e03410992320660ccc4e683
Volume 24
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