Computer-aided diagnosis system outperforms scoring analysis in myocardial perfusion imaging

Johansson, Lena; Edenbrandt, Lars; Nakajima, Kénichi; Lomsky, Milan; Svensson, Sören; Trägårdh, Elin

doi:10.1007/s12350-013-9815-y

Cited by 18 publications

(17 citation statements)

References 24 publications

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“…Johansson et al presented their initial evaluation of the EXINI heart using the ANN system in comparison with PERFEX (Emory cardiac Toolbox). 11 They reported high diagnostic accuracy with EXINI's ANN system with 70% sensitivity and 93% specificity to find ischemia, and 84% sensitivity and 84% specificity to find the abnormal study. They included a broad spectrum of diseases, with disease prevalence of 30% according to their clinical routine.…”

Section: Discussionmentioning

confidence: 98%

“…Detection of ischemia was performed in 2 ways. 11 The area of possible perfusion abnormality in the stress images (stress defect) was segmented using a method used, and the Japanese Society of Nuclear Medicine working group databases were created to provide common diagnostic criteria. 9 Recently, artificial intelligence, such as the artificial neural network (ANN), has been used as a new approach in nuclear medicine.…”

Section: Ann For Detecting Ischemiamentioning

confidence: 99%

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Diagnostic Performance of Artificial Neural Network for Detecting Ischemia in Myocardial Perfusion Imaging

Nakajima

Matsuo

Wakabayashi

et al. 2015

Circ J

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Background:The purpose of this study was to apply an artificial neural network (ANN) in patients with coronary artery disease (CAD) and to characterize its diagnostic ability compared with conventional visual and quantitative methods in myocardial perfusion imaging (MPI). Methods and Results:A total of 106 patients with CAD were studied with MPI, including multiple vessel disease (49%), history of myocardial infarction (27%) and coronary intervention (30%). The ANN detected abnormal areas with a probability of stress defect and ischemia. The consensus diagnosis based on expert interpretation and coronary stenosis was used as the gold standard. The left ventricular ANN value was higher in the stress-defect group than in the no-defect group (0.92±0.11 vs. 0.25±0.32, P<0.0001) and higher in the ischemia group than in the noischemia group (0.70±0.40 vs. 0.004±0.032, P<0.0001). Receiver-operating characteristics curve analysis showed comparable diagnostic accuracy between ANN and the scoring methods (0.971 vs. 0.980 for stress defect, and 0.882 vs. 0.937 for ischemia, both P=NS). The relationship between the ANN and defect scores was non-linear, with the ANN rapidly increased in ranges of summed stress score of 2-7 and summed defect score of 2-4. Conclusions:Although the diagnostic ability of ANN was similar to that of conventional scoring methods, the ANN could provide a different viewpoint for judging abnormality, and thus is a promising method for evaluating abnormality in MPI. (Circ J 2015; 79: 1549 -1556

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Section: Discussionmentioning

confidence: 98%

Section: Ann For Detecting Ischemiamentioning

confidence: 99%

Diagnostic Performance of Artificial Neural Network for Detecting Ischemia in Myocardial Perfusion Imaging

Nakajima

Matsuo

Wakabayashi

et al. 2015

Circ J

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“…Over polar images, slice data provided inconsistent-and if present minor-benefits in terms of uncertainty (decrease only for physician 1, increasing uncertainty for physician 3). This result is not unexpected because polar maps-if technically correctly generatedcapture most of the perfusion information, and polar maps form the information source of many highly accurate artificial neural network diagnostic systems (26,27).…”

Section: Interpretive Certaintymentioning

confidence: 85%

Evaluating the Role of Routine Prone Acquisition on Visual Evaluation of SPECT Images

Güner

Çalışkan

Işık

et al. 2015

J. Nucl. Med. Technol.

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Attenuation artifacts reduce our ability to evaluate perfusion of affected myocardial segments. The aim of this study was to evaluate the impact of routine prone-position image evaluation within a stepwise visual interpretation of myocardial perfusion studies. Methods: We have included 279 consecutive patients who were referred for evaluation of myocardial ischemia. All patients underwent routine electrocardiogram-gated supine SPECT imaging and non-electrocardiogram-gated proneposition SPECT imaging. Three nuclear medicine physicians interpreted the images in the following order: polar maps, supine images, raw images, motion-frozen gated images, and prone images, using a scale of 0-4. Segments with perfusion abnormalities were noted. Results: All physicians reported lower proportions of equivocal evaluations after evaluating prone images (18.3% vs. 4.7%, 19% vs. 11.1%, and 12.2% vs. 6.1%, P , 0.0001, P 5 0.0077, and P 5 0.0125, respectively). At the prone stage, normalcy rates were 89%, 87%, and 91%. Two physicians had significantly increased normalcy rates at the prone stage (72%-89%, P 5 0.039, and 66%-87%, P 5 0.006). At the prone stage, a decision reversal to normal or probably normal was observed in 40% (29/72), 33% (17/51), and 43% (21/48). In men, apical, mid, and basal inferior walls and in women apical and mid parts of anterior walls were more likely to be attributed to attenuation. The 2 steps that increased normalcy rates for interpreters were the review of raw images and of prone images. Conclusion: Routine prone imaging increases interpretive certainty and interobserver agreement and changes the final evaluation in a substantial number of patients and significantly decreases the number of equivocal evaluations.

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“…The anterior, inferior, and anterolateral myocardial counts were calculated as mean values in the segments 1 and 7, segments 4 and 10, and segments 12 and 16 on American Heart Association classification of the polar map (10), respectively. We used cardioREPO (cREPO; FUJIFILM RI Pharma, Tokyo, Japan) software program integrated with an artificial neural network (ANN) feature for evaluation of myocardial perfusion and compensation of small-heart effect (11)(12)(13)(14). This software program automatically creates diagnosis based on the probability of perfusion abnormality using ANN trained by expert nuclear medicine physicians and cardiologists.…”

Section: Methodsmentioning

confidence: 99%

Cardiac and Respiratory Motion-induced Artifact in Myocardial Perfusion SPECT

Okuda

Nakajima

Kikuchi

et al. 2017

Annals of Nuclear Cardiology

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Background: Digital anthropomorphic phantoms have gradually gained an important role in nuclear medicine imaging. The aim of this study was to generate digital phantom models simulated with cardiac and respiratory motions and to evaluate motion-induced artifact using a quantitative software program developed with artificial intelligence (AI) technology in myocardial perfusion single-photon emission computed tomography (SPECT)

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Computer-aided diagnosis system outperforms scoring analysis in myocardial perfusion imaging

Abstract: EXINI demonstrated greater diagnostic accuracy for detection of ischemia and abnormal studies than did PERFEX. EXINI CAD also outperformed its SSS analysis.

Cited by 18 publications

References 24 publications

Diagnostic Performance of Artificial Neural Network for Detecting Ischemia in Myocardial Perfusion Imaging

Diagnostic Performance of Artificial Neural Network for Detecting Ischemia in Myocardial Perfusion Imaging

Evaluating the Role of Routine Prone Acquisition on Visual Evaluation of SPECT Images

Cardiac and Respiratory Motion-induced Artifact in Myocardial Perfusion SPECT

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