Automated Diagnostics: Advances in the Diagnosis of Intestinal Parasitic Infections in Humans and Animals

Autor(es)

Inacio, Sandra Valeria ^[1] ; Gomes, Jancarlo Ferreira ^{[2, 3]} ; Falcao, Alexandre Xavier ^[3] ; Martins dos Santos, Bianca ^[2] ; Soares, Felipe Augusto ^[2] ; Nery Loiola, Saulo Hudson ^[2] ; Rosa, Stefani Laryssa ^[2] ; Nagase Suzuki, Celso Tetsuo ^[3] ; Bresciani, Katia Denise Saraiva ^[1]

Afiliação do(s) autor(es)

^[1] Sao Paulo State Univ UNESP, Sch Vet Med, Aracatuba - Brazil

^[2] Univ Campinas UNICAMP, Sch Med Sci, Campinas - Brazil

^[3] Univ Campinas UNICAMP, Inst Comp IC, Campinas - Brazil

• Tipo de documento: Artigo de Revisão
• Fonte: FRONTIERS IN VETERINARY SCIENCE; v. 8, NOV 23 2021.
• Citações Web of Science: 0

Resumo

The increasingly close proximity between people and animals is of great concern for public health, given the risk of exposure to infectious diseases transmitted through animals, which are carriers of more than 60 zoonotic agents. These diseases, which are included in the list of Neglected Tropical Diseases, cause losses in countries with tropical and subtropical climates, and in regions with temperate climates. Indeed, they affect more than a billion people around the world, a large proportion of which are infected by one or more parasitic helminths, causing annual losses of billions of dollars. Several studies are being conducted in search for differentiated, more sensitive diagnostics with fewer errors. These studies, which involve the automated examination of intestinal parasites, still face challenges that must be overcome in order to ensure the proper identification of parasites. This includes a protocol that allows for elimination of most of the debris in samples, satisfactory staining of parasite structures, and a robust image database. Our objective here is therefore to offer a critical description of the techniques currently in use for the automated diagnosis of intestinal parasites in fecal samples, as well as advances in these techniques. (AU)

• 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