Artificial intelligence applied in identifying left ventricular walls in myocardial perfusion scintigraphy images: Pilot study.
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 treatment of coronary ar...
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2025-01-01
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| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0312257 |
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| author | Solange Amorim Nogueira Fernanda Ambrogi B Luz Thiago Fellipe O Camargo Julio Cesar S Oliveira Guilherme Carvalho Campos Neto Felipe Brazao F Carvalhaes Marcio Rodrigues C Reis Paulo Victor Santos Giovanna Souza Mendes Rafael Maffei Loureiro Daniel Tornieri Viviane M Gomes Pacheco Antonio Paulo Coimbra Wesley Pacheco Calixto |
| author_facet | Solange Amorim Nogueira Fernanda Ambrogi B Luz Thiago Fellipe O Camargo Julio Cesar S Oliveira Guilherme Carvalho Campos Neto Felipe Brazao F Carvalhaes Marcio Rodrigues C Reis Paulo Victor Santos Giovanna Souza Mendes Rafael Maffei Loureiro Daniel Tornieri Viviane M Gomes Pacheco Antonio Paulo Coimbra Wesley Pacheco Calixto |
| author_sort | Solange Amorim Nogueira |
| collection | DOAJ |
| description | 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 treatment of coronary artery disease. The methodology included data collection in a clinical environment, followed by data preparation and analysis using the 3D Slicer Platform for manual segmentation, and subsequently, the application of artificial intelligence models for automated segmentation, focusing on the efficiency of identifying the walls of the left ventricular. A total of 83 clinical routine exams were collected, each exam containing 50 slices, which is 4,150 images. The results demonstrate the efficiency of the proposed artificial intelligence model, with a Dice coefficient of 87% and an average Intersection over Union of 0.8, reflecting high agreement with the manual segmentations produced by experts and surpassing traditional interpretation methods. The internal and external validation of the model corroborates its future applicability in real clinical scenarios, offering a new perspective in the analysis of myocardial perfusion scintigraphy images. The integration of artificial intelligence into the process of analyzing myocardial perfusion scintigraphy images represents a significant advancement in diagnostic accuracy, promoting substantial improvements in the interpretation of medical images, and establishing a foundation for future research and clinical applications, such as artifact correction. |
| format | Article |
| id | doaj-art-3b04ade690ac4813b3810ccd0dd0f475 |
| institution | Kabale University |
| issn | 1932-6203 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj-art-3b04ade690ac4813b3810ccd0dd0f4752025-02-05T05:31:15ZengPublic Library of Science (PLoS)PLoS ONE1932-62032025-01-01201e031225710.1371/journal.pone.0312257Artificial intelligence applied in identifying left ventricular walls in myocardial perfusion scintigraphy images: Pilot study.Solange Amorim NogueiraFernanda Ambrogi B LuzThiago Fellipe O CamargoJulio Cesar S OliveiraGuilherme Carvalho Campos NetoFelipe Brazao F CarvalhaesMarcio Rodrigues C ReisPaulo Victor SantosGiovanna Souza MendesRafael Maffei LoureiroDaniel TornieriViviane M Gomes PachecoAntonio Paulo CoimbraWesley Pacheco CalixtoThis 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 treatment of coronary artery disease. The methodology included data collection in a clinical environment, followed by data preparation and analysis using the 3D Slicer Platform for manual segmentation, and subsequently, the application of artificial intelligence models for automated segmentation, focusing on the efficiency of identifying the walls of the left ventricular. A total of 83 clinical routine exams were collected, each exam containing 50 slices, which is 4,150 images. The results demonstrate the efficiency of the proposed artificial intelligence model, with a Dice coefficient of 87% and an average Intersection over Union of 0.8, reflecting high agreement with the manual segmentations produced by experts and surpassing traditional interpretation methods. The internal and external validation of the model corroborates its future applicability in real clinical scenarios, offering a new perspective in the analysis of myocardial perfusion scintigraphy images. The integration of artificial intelligence into the process of analyzing myocardial perfusion scintigraphy images represents a significant advancement in diagnostic accuracy, promoting substantial improvements in the interpretation of medical images, and establishing a foundation for future research and clinical applications, such as artifact correction.https://doi.org/10.1371/journal.pone.0312257 |
| spellingShingle | Solange Amorim Nogueira Fernanda Ambrogi B Luz Thiago Fellipe O Camargo Julio Cesar S Oliveira Guilherme Carvalho Campos Neto Felipe Brazao F Carvalhaes Marcio Rodrigues C Reis Paulo Victor Santos Giovanna Souza Mendes Rafael Maffei Loureiro Daniel Tornieri Viviane M Gomes Pacheco Antonio Paulo Coimbra Wesley Pacheco Calixto Artificial intelligence applied in identifying left ventricular walls in myocardial perfusion scintigraphy images: Pilot study. PLoS ONE |
| title | Artificial intelligence applied in identifying left ventricular walls in myocardial perfusion scintigraphy images: Pilot study. |
| title_full | Artificial intelligence applied in identifying left ventricular walls in myocardial perfusion scintigraphy images: Pilot study. |
| title_fullStr | Artificial intelligence applied in identifying left ventricular walls in myocardial perfusion scintigraphy images: Pilot study. |
| title_full_unstemmed | Artificial intelligence applied in identifying left ventricular walls in myocardial perfusion scintigraphy images: Pilot study. |
| title_short | Artificial intelligence applied in identifying left ventricular walls in myocardial perfusion scintigraphy images: Pilot study. |
| title_sort | artificial intelligence applied in identifying left ventricular walls in myocardial perfusion scintigraphy images pilot study |
| url | https://doi.org/10.1371/journal.pone.0312257 |
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