Tabular data augmentation for video-based detection of hypomimia in Parkinson's disease

Autor(es)

Oliveira, Guilherme C. ; Ngo, Quoc C. ; Passos, Leandro A. ; Papa, Joao P. ; Jodas, Danilo S. ; Kumar, Dinesh

• Tipo de documento: Artigo Científico
• Fonte: COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE; v. 240, p. 8-pg., 2023-10-01.

Resumo

Background and Objective: This paper presents a method for the computerized detection of hypomimia in people with Parkinson's disease (PD). It overcomes the difficulty of the small and unbalanced size of available datasets. Methods: A public dataset consisting of features of the video recordings of people with PD with four facial expressions was used. Synthetic data was generated using a Conditional Generative Adversarial Network (CGAN) for training augmentation. After training the model, Test-Time Augmentation was performed. The classification was conducted using the original test set to prevent bias in the results. Results: The employment of CGAN followed by Test-Time Augmentation led to an accuracy of classification of the videos of 83%, specificity of 82%, and sensitivity of 85% in the test set that the prevalence of PD was around 7% and where real data was used for testing. This is a significant improvement compared with other similar studies. The results show that while the technique was able to detect people with PD, there were a number of false positives. Hence this is suitable for applications such as population screening or assisting clinicians, but at this stage is not suitable for diagnosis. Conclusions: This work has the potential for assisting neurologists to perform online diagnose and monitoring their patients. However, it is essential to test this for different ethnicity and to test its repeatability. (AU)

• Processo FAPESP: 19/00585-5 - Redes neurais adversariais evolutivas aplicadas ao diagnóstico assistido por computador de retinopatia diabética
• Beneficiário: João Paulo Papa
• Modalidade de apoio: Auxílio à Pesquisa - Regular
• Processo FAPESP: 18/15597-6 - Aplicação e investigação de métodos de aprendizado não-supervisionado em tarefas de recuperação e classificação
• Beneficiário: Daniel Carlos Guimarães Pedronette
• Modalidade de apoio: Auxílio à Pesquisa - Jovens Pesquisadores - Fase 2
• Processo FAPESP: 14/12236-1 - AnImaLS: Anotação de Imagem em Larga Escala: o que máquinas e especialistas podem aprender interagindo?
• Beneficiário: Alexandre Xavier Falcão
• Modalidade de apoio: Auxílio à Pesquisa - Temático
• Processo FAPESP: 19/07665-4 - Centro de Inteligência Artificial
• Beneficiário: Fabio Gagliardi Cozman
• Modalidade de apoio: Auxílio à Pesquisa - Programa eScience e Data Science - Centros de Pesquisa em Engenharia
• Processo FAPESP: 13/07375-0 - CeMEAI - Centro de Ciências Matemáticas Aplicadas à Indústria
• Beneficiário: Francisco Louzada Neto
• Modalidade de apoio: Auxílio à Pesquisa - Centros de Pesquisa, Inovação e Difusão - CEPIDs
• Processo FAPESP: 19/02205-5 - Aprendizado adversarial em processamento de linguagem natural
• Beneficiário: Gustavo Henrique de Rosa
• Modalidade de apoio: Bolsas no Brasil - Doutorado