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Diagnostic accuracy...
Diagnostic accuracy of an artificial neural network compared with statistical quantitation of myocardial perfusion images: a Japanese multicenter study
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Nakajima, K. (författare)
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Kudo, T. (författare)
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Nakata, T. (författare)
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Kiso, K. (författare)
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Kasai, T. (författare)
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Taniguchi, Y. (författare)
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Matsuo, S. (författare)
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Momose, M. (författare)
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Nakagawa, M. (författare)
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Sarai, M. (författare)
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Hida, S. (författare)
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Tanaka, H. (författare)
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Yokoyama, K. (författare)
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Okuda, K. (författare)
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- Edenbrandt, Lars, 1957 (författare)
- Gothenburg University,Göteborgs universitet,Institutionen för medicin, avdelningen för molekylär och klinisk medicin,Institute of Medicine, Department of Molecular and Clinical Medicine
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(creator_code:org_t)
- 2017-09-26
- 2017
- Engelska.
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Ingår i: European Journal of Nuclear Medicine and Molecular Imaging. - : Springer Science and Business Media LLC. - 1619-7070 .- 1619-7089. ; 44:13, s. 2280-2289
- Relaterad länk:
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https://link.springe...
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https://gup.ub.gu.se...
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https://doi.org/10.1...
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Abstract
Ämnesord
Stäng
- Purpose Artificial neural networks (ANN) might help to diagnose coronary artery disease. This study aimed to determine whether the diagnostic accuracy of an ANN-based diagnostic system and conventional quantitation are comparable. Methods The ANN was trained to classify potentially abnormal areas as true or false based on the nuclear cardiology expert interpretation of 1001 gated stress/rest Tc-99m-MIBI images at 12 hospitals. The diagnostic accuracy of the ANN was compared with 364 expert interpretations that served as the gold standard of abnormality for the validation study. Conventional summed stress/rest/difference scores (SSS/SRS/SDS) were calculated and compared with receiver operating characteristics (ROC) analysis. Results The ANN generated a better area under the ROC curves (AUC) than SSS (0.92 vs. 0.82, p < 0.0001), indicating better identification of stress defects. The ANN also generated a better AUC than SDS (0.90 vs. 0.75, p < 0.0001) for stress-induced ischemia. The AUC for patients with old myocardial infarction based on rest defects was 0.97 (0.91 for SRS, p = 0.0061), and that for patients with and without a history of revascularization based on stress defects was 0.94 and 0.90 (p = 0.0055 and p < 0.0001 vs. SSS, respectively). The SSS/SRS/SDS steeply increased when ANN values (probability of abnormality) were > 0.80. Conclusion The ANN was diagnostically accurate in various clinical settings, including that of patients with previous myocardial infarction and coronary revascularization. The ANN could help to diagnose coronary artery disease.
Ämnesord
- MEDICIN OCH HÄLSOVETENSKAP -- Klinisk medicin -- Kardiologi (hsv//swe)
- MEDICAL AND HEALTH SCIENCES -- Clinical Medicine -- Cardiac and Cardiovascular Systems (hsv//eng)
- MEDICIN OCH HÄLSOVETENSKAP -- Klinisk medicin -- Radiologi och bildbehandling (hsv//swe)
- MEDICAL AND HEALTH SCIENCES -- Clinical Medicine -- Radiology, Nuclear Medicine and Medical Imaging (hsv//eng)
Nyckelord
- Artificial intelligence
- Diagnostic imaging
- Coronaryarterydisease
- Nuclear cardiology
- Computer-aided
- coronary-artery-disease
- nuclear cardiology
- heart-association
- gated-spect
- quantification
- guidelines
- revascularization
- validation
- committee
- stress
- Radiology
- Nuclear Medicine & Medical Imaging
Publikations- och innehållstyp
- ref (ämneskategori)
- art (ämneskategori)
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Nakajima, K.
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Kudo, T.
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Nakata, T.
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Kiso, K.
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Kasai, T.
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Taniguchi, Y.
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visa fler...
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Matsuo, S.
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Momose, M.
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Nakagawa, M.
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Sarai, M.
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Hida, S.
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Tanaka, H.
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Yokoyama, K.
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Okuda, K.
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Edenbrandt, Lars ...
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