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Gauy, Marcelo Matheus ; Berti, Larissa Cristina ; Candido, Arnaldo, Jr. ; Neto, Augusto Camargo ; Goldman, Alfredo ; Shafferman Levin, Anna Sara ; Martins, Marcus ; de Medeiros, Beatriz Raposo ; Queiroz, Marcelo ; Sabino, Ester Cerdeira ; Fernandes Svartman, Flaviane Romani ; Finger, Marcelo

• Tipo de documento: Artigo Científico
• Fonte: ARTIFICIAL INTELLIGENCE IN MEDICINE, AIME 2023; v. 13897, p. 5-pg., 2023-01-01.

Resumo

This work investigates Artificial Intelligence (AI) systems that detect respiratory insufficiency (RI) by analyzing speech audios, thus treating speech as a RI biomarker. Previous works [2,6] collected RI data (P1) from COVID-19 patients during the first phase of the pandemic and trained modern AI models, such as CNNs and Transformers, which achieved 96.5% accuracy, showing the feasibility of RI detection via AI. Here, we collect RI patient data (P2) with several causes besides COVID-19, aiming at extending AI-based RI detection. We also collected control data from hospital patients without RI. We show that the considered models, when trained on P1, do not generalize to P2, indicating that COVID-19 RI has features that may not be found in all RI types. (AU)

• 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 Aplicada
• Processo FAPESP: 20/16543-7 - Modelos Neurais Pré-Treinados para Detecção Insuficiência Respiratória em Áudios
• Beneficiário: Marcelo Matheus Gauy
• Modalidade de apoio: Bolsas no Brasil - Pós-Doutorado
• Processo FAPESP: 20/06443-5 - Estudo SPIRA: Sistema de detecção Precoce de Insuficiência Respiratória por meio de análise de Áudio
• Beneficiário: Marcelo Finger
• Modalidade de apoio: Auxílio à Pesquisa - Regular