| 1. |
- Arvidsson, Ida, et al.
(författare)
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Deep learning prediction of quantitative coronary angiography values using myocardial perfusion images with a CZT camera
- 2023
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Ingår i: Journal of Nuclear Cardiology. - : Springer Science and Business Media LLC. - 1071-3581 .- 1532-6551. ; 30:1, s. 116-126
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Tidskriftsartikel (refereegranskat)abstract
- Purpose: Evaluate the prediction of quantitative coronary angiography (QCA) values from MPI, by means of deep learning. Methods: 546 patients (67% men) undergoing stress 99mTc-tetrofosmin MPI in a CZT camera in the upright and supine position were included (1092 MPIs). Patients were divided into two groups: ICA group included 271 patients who performed an ICA within 6 months of MPI and a control group with 275 patients with low pre-test probability for CAD and a normal MPI. QCA analyses were performed using radiologic software and verified by an expert reader. Left ventricular myocardium was segmented using clinical nuclear cardiology software and verified by an expert reader. A deep learning model was trained using a double cross-validation scheme such that all data could be used as test data as well. Results: Area under the receiver-operating characteristic curve for the prediction of QCA, with > 50% narrowing of the artery, by deep learning for the external test cohort: per patient 85% [95% confidence interval (CI) 84%-87%] and per vessel; LAD 74% (CI 72%-76%), RCA 85% (CI 83%-86%), LCx 81% (CI 78%-84%), and average 80% (CI 77%-83%). Conclusion: Deep learning can predict the presence of different QCA percentages of coronary artery stenosis from MPIs.
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| 2. |
- Arvidsson, Ida, et al.
(författare)
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Detection of left bundle branch block and obstructive coronary artery disease from myocardial perfusion scintigraphy using deep neural networks
- 2021
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Ingår i: Medical Imaging 2021 : Computer-Aided Diagnosis - Computer-Aided Diagnosis. - : SPIE. - 1605-7422. - 9781510640238 ; 11597
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Konferensbidrag (refereegranskat)abstract
- Myocardial perfusion scintigraphy, which is a non-invasive imaging technique, is one of the most common cardiological examinations performed today, and is used for diagnosis of coronary artery disease. Currently the analysis is performed visually by physicians, but this is both a very time consuming and a subjective approach. These are two of the motivations for why an automatic tool to support the decisions would be useful. We have developed a deep neural network which predicts the occurrence of obstructive coronary artery disease in each of the three major arteries as well as left bundle branch block. Since multiple, or none, of these could have a defect, this is treated as a multi-label classification problem. Due to the highly imbalanced labels, the training loss is weighted accordingly. The prediction is based on two polar maps, captured during stress in upright and supine position, together with additional information such as BMI and angina symptoms. The polar maps are constructed from myocardial perfusion scintigraphy examinations conducted in a dedicated Cadmium-Zinc-Telluride cardio camera (D-SPECT Spectrum Dynamics). The study includes data from 759 patients. Using 5-fold cross-validation we achieve an area under the receiver operating characteristics curve of 0.89 as average on per-vessel level for the three major arteries, 0.94 on per-patient level and 0.82 for left bundle branch block.
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| 3. |
- Arvidsson, Ida, et al.
(författare)
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Prediction of Obstructive Coronary Artery Disease from Myocardial Perfusion Scintigraphy using Deep Neural Networks
- 2021
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Ingår i: 2020 25TH INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION (ICPR). - : IEEE COMPUTER SOC. - 1051-4651. - 9781728188089 ; , s. 4442-4449
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Konferensbidrag (refereegranskat)abstract
- For diagnosis and risk assessment in patients with stable ischemic heart disease, myocardial perfusion scintigraphy is one of the most common cardiological examinations performed today. There are however many motivations for why an artificial intelligence algorithm would provide useful input to this task. For example to reduce the subjectiveness and save time for the nuclear medicine physicians working with this time consuming task. In this work we have developed a deep learning algorithm for multi-label classification based on a convolutional neural network to estimate the probability of obstructive coronary artery disease in the left anterior artery, left circumflex artery and right coronary artery. The prediction is based on data from myocardial perfusion scintigraphy studies conducted in a dedicated Cadmium-Zinc-Telluride cardio camera (D-SPECT Spectrum Dynamics). Data from 588 patients was available, with stress images in both upright and supine position, as well as a number of auxiliary parameters such as angina symptoms and age. The data was used to train and evaluate the algorithm using 5-fold cross-validation. We achieve state-of-the-art results for this task with an area under the receiver operating characteristics curve of 0.89 as average on per-vessel level and 0.95 on per-patient level.
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| 4. |
- Ochoa-Figueroa, Miguel, et al.
(författare)
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Rendimiento diagnóstico de diferentes protocolos de estrés cardiaco usados en imagen de perfusión miocárdica para el diagnóstico de enfermedad coronaria usando una cámara de cadmio-zinc-telurio con correlación con angiografía coronaria [Diagnostic performance of different cardiac stress protocols for myocardial perfusion imaging for the diagnosis of coronary artery disease using a cadmium-zinc-telluride camera with invasive coronary angiography correlation]
- 2023
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Ingår i: REVISTA ESPANOLA DE MEDICINA NUCLEAR E IMAGEN MOLECULAR. - : ELSEVIER ESPANA SLU. - 2253-654X. ; 42:5, s. 281-288
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Tidskriftsartikel (refereegranskat)abstract
- Purpose: To evaluate the diagnostic performance of three different cardiac stress protocols for myo-cardial perfusion imaging (MPI) using a cadmium-zinc-telluride (CZT) camera with invasive coronary angiography (ICA) correlation for the diagnosis of coronary artery disease in a high risk population. Methods: Retrospective study of 263 patients (96 women and 167 males, mean age 68 years) from which 119 patients performed a bicycle stress test (BST), 113 pharmacological stress test (PST) and 31 a com-bination of the two (CST) between September 2014 and December 2018. The patients then underwent myocardial perfusion imaging (MPI), followed by ICA and evaluated by means of quantitative angio-graphy software, within six months after the MPI. The mean pre-test probability score for coronary disease according to the European Society of Cardiology criteria was 36% for the whole population. The MPI was performed in a dedicated CZT cardio camera (D-SPECT Spectrum Dynamics) with a two-day protocol, according to the European Association of Nuclear Medicine guidelines.Results: No significant difference was observed between the three stress protocols in terms of diagnostic accuracy (BST 85%, PST 88% and CST 84%). The overall diagnostic accuracy of MPI to identify patients with any obstructive CAD at ICA was 86%, Sensitivity 93%, Specificity 54%, PPV 90% and NPV 63%.Conclusion: The CZT D-SPECT camera achieves overall satisfactory results in the diagnosis of CAD, obser-ving no significant differences in the diagnostic performance when the stress test was performed as a BST, PST or CST.& COPY; 2023 Sociedad Espanola de Medicina Nuclear e Imagen Molecular. Published by Elsevier Espana, S.L.U. All rights reserved.
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| 5. |
- Ochoa-Figueroa, Miguel, et al.
(författare)
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Rendimiento diagnóstico de un nuevo software de aprendizaje profundo para corrección de atenuación en la imagen de perfusión miocárdica utilizando una cámara CZT cardiodedicada. Experiencia en la práctica clínica [Diagnostic performance of a novel deep learning attenuation correction software for MPI using a cardio dedicated CZT camera: Experience in the clinical practice]
- 2024
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Ingår i: REVISTA ESPANOLA DE MEDICINA NUCLEAR E IMAGEN MOLECULAR. - : ELSEVIER ESPANA SLU. - 2253-654X. ; 43:1, s. 23-30
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Tidskriftsartikel (refereegranskat)abstract
- PurposeTo evaluate the diagnostic performance of a novel deep learning attenuation correction software (SAPCA) for myocardial perfusion imaging (MPI) using a cadmium-zinc-telluride (CZT) cardio dedicated camera with invasive coronary angiography (ICA) correlation for the diagnosis of coronary artery disease (CAD) in a high-risk population.MethodsRetrospective study of 300 patients (196 males [65%], mean age 68 years) from September 2014 to October 2019 undergoing MPI, followed by ICA and evaluated by means of quantitative angiography software, within six months after the MPI. The mean pre-test probability score for coronary disease according to the European Society of Cardiology criteria was 37% for the whole cohort. The MPI was performed in a dedicated CZT cardio camera (D-SPECT® Spectrum Dynamics) with a two-day protocol, according to the European Association of Nuclear Medicine guidelines. MPI was retrospectively evaluated with and without the SAPCA.ResultsThe overall diagnostic accuracy of MPI without SAPCA to identify patients with any obstructive CAD at ICA was 87%, Sensitivity 94%, Specificity 57%, positive predictive value 91% and negative predictive value 64%. Using SAPCA the overall diagnostic accuracy was 90%, sensitivity 91%, specificity 86%, positive predictive value 97% and negative predictive value 66%.ConclusionUse of the novel SAPCA enhances performance of the MPI using the CZT D-SPECT® camera and achieves improved results, especially avoiding artefacts and reducing the number of false positive results.
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