Individual medical assessment by test.

<div><p>This paper proposes the use of artificial intelligence techniques, specifically the nnU-Net convolutional neural network, to improve the identification of left ventricular walls in images of myocardial perfusion scintigraphy, with the objective of improving the diagnosis and trea...

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Main Author:	Solange Amorim Nogueira (4648687) (author)
Other Authors:	Fernanda Ambrogi B. Luz (20577055) (author), Thiago Fellipe O. Camargo (20577058) (author), Julio Cesar S. Oliveira (20577061) (author), Guilherme Carvalho Campos Neto (20577064) (author), Felipe Brazao F. Carvalhaes (20577067) (author), Marcio Rodrigues C. Reis (20577070) (author), Paulo Victor Santos (19508023) (author), Giovanna Souza Mendes (20577073) (author), Rafael Maffei Loureiro (6402290) (author), Daniel Tornieri (19508032) (author), Viviane M. Gomes Pacheco (20577076) (author), Antonio Paulo Coimbra (20577079) (author), Wesley Pacheco Calixto (18823842) (author)
Published:	2025
Subjects:	Medicine Cell Biology Science Policy Space Science Environmental Sciences not elsewhere classified Biological Sciences not elsewhere classified Chemical Sciences not elsewhere classified reflecting high agreement promoting substantial improvements myocardial perfusion scintigraphy coronary artery disease 3d slicer platform manual segmentations produced artificial intelligence techniques artificial intelligence models artificial intelligence applied real clinical scenarios left ventricular walls artificial intelligence left ventricular manual segmentation clinical environment clinical applications xlink "> significant advancement results demonstrate paper proposes new perspective model corroborates future research future applicability external validation dice coefficient diagnostic accuracy data preparation average intersection automated segmentation
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Individual medical assessment by test.

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