Mathews B Fish
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Explore the profile of Mathews B Fish including associated specialties, affiliations and a list of published articles.
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37
Citations
719
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Recent Articles
1.
Miller R, Kavanagh P, Lemley M, Liang J, Sharir T, Einstein A, et al.
J Nucl Med
. 2025 Feb;
PMID: 39978815
We previously demonstrated that a deep learning (DL) model of myocardial perfusion SPECT imaging improved accuracy for detection of obstructive coronary artery disease (CAD). We aimed to improve the clinical...
2.
Miller R, Lemley M, Shanbhag A, Ramirez G, Liang J, Builoff V, et al.
J Nucl Med
. 2024 Oct;
65(11):1795-1801.
PMID: 39362762
The Registry of Fast Myocardial Perfusion Imaging with Next-Generation SPECT (REFINE SPECT) has been expanded to include more patients and CT attenuation correction imaging. We present the design and initial...
3.
Miller R, Bednarski B, Pieszko K, Kwiecinski J, Williams M, Shanbhag A, et al.
EBioMedicine
. 2024 Jan;
99:104930.
PMID: 38168587
Background: Myocardial perfusion imaging (MPI) is one of the most common cardiac scans and is used for diagnosis of coronary artery disease and assessment of cardiovascular risk. However, the large...
4.
Pieszko K, Shanbhag A, Singh A, Hauser M, Miller R, Liang J, et al.
NPJ Digit Med
. 2023 May;
6(1):78.
PMID: 37127660
Standard clinical interpretation of myocardial perfusion imaging (MPI) has proven prognostic value for predicting major adverse cardiovascular events (MACE). However, personalizing predictions to a specific event type and time interval...
5.
Williams M, Bednarski B, Pieszko K, Miller R, Kwiecinski J, Shanbhag A, et al.
Eur J Nucl Med Mol Imaging
. 2023 Apr;
50(9):2656-2668.
PMID: 37067586
Purpose: Patients with known coronary artery disease (CAD) comprise a heterogenous population with varied clinical and imaging characteristics. Unsupervised machine learning can identify new risk phenotypes in an unbiased fashion....
6.
Singh A, Miller R, Otaki Y, Kavanagh P, Hauser M, Tzolos E, et al.
JACC Cardiovasc Imaging
. 2022 Oct;
16(2):209-220.
PMID: 36274041
Background: Myocardial perfusion imaging (MPI) is frequently used to provide risk stratification, but methods to improve the accuracy of these predictions are needed. Objectives: The authors developed an explainable deep...
7.
Miller R, Singh A, Otaki Y, Tamarappoo B, Kavanagh P, Parekh T, et al.
Eur J Nucl Med Mol Imaging
. 2022 Oct;
50(2):387-397.
PMID: 36194270
Purpose: Artificial intelligence (AI) has high diagnostic accuracy for coronary artery disease (CAD) from myocardial perfusion imaging (MPI). However, when trained using high-risk populations (such as patients with correlating invasive...
8.
Tamarappoo B, Otaki Y, Sharir T, Hu L, Gransar H, Einstein A, et al.
Circ Cardiovasc Imaging
. 2022 Jun;
15(6):e012741.
PMID: 35727872
Background: Semiquantitative assessment of stress myocardial perfusion defect has been shown to have greater prognostic value for prediction of major adverse cardiac events (MACE) in women compared with men in...
9.
Miller R, Hauser M, Sharir T, Einstein A, Fish M, Ruddy T, et al.
J Nucl Cardiol
. 2022 Jun;
29(5):2393-2403.
PMID: 35672567
Background: Accurately predicting which patients will have abnormal perfusion on MPI based on pre-test clinical information may help physicians make test selection decisions. We developed and validated a machine learning...
10.
Miller R, Kuronuma K, Singh A, Otaki Y, Hayes S, Chareonthaitawee P, et al.
J Nucl Med
. 2022 May;
63(11):1768-1774.
PMID: 35512997
Artificial intelligence may improve accuracy of myocardial perfusion imaging (MPI) but will likely be implemented as an aid to physician interpretation rather than an autonomous tool. Deep learning (DL) has...