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Matthew T. MacLean

Observed names: MacLean, Matthew, Matthew MacLean, Matthew Maclean, Matthew T MacLean, Matthew T. Maclean
ORCID: 0000-0002-0514-7218
Observed ORCID: 0000-0002-0514-7218
OpenAlex ID: A5022653825
Development and Validation of a Nomogram Prognostic Model for SCLC Patients
2018 · Shidan Wang, Lin Yang, et al. · Journal of Thoracic Oncology · 209
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A multiancestry genome-wide association study of unexplained chronic ALT elevation as a proxy for nonalcoholic fatty liver disease with histological and radiological validation
2022 · Marijana Vujkovic, Shweta Ramdas, et al. · Nature Genetics · 203
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Outcomes of neoadjuvant and adjuvant chemotherapy in stage 2 and 3 non-small cell lung cancer: an analysis of the National Cancer Database
2018 · Matthew MacLean, Xin Luo, et al. · Oncotarget · 39
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Quantification of abdominal fat from computed tomography using deep learning and its association with electronic health records in an academic biobank
2021 · Matthew T MacLean, Qasim Jehangir, et al. · Journal of the American Medical Informatics Association · 31
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Strategies for Implementing Machine Learning Algorithms in the Clinical Practice of Radiology
2024 · Allison Chae, Michael S. Yao, et al. · Radiology · 28
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Exome-wide association analysis of CT imaging-derived hepatic fat in a medical biobank
2022 · Joseph Park, Matthew T. MacLean, et al. · Cell Reports Medicine · 19
Deep learning approach for automated segmentation of myocardium using bone scintigraphy single-photon emission computed tomography/computed tomography in patients with suspected cardiac amyloidosis
2024 · Abhijit Bhattaru, Chaitanya Rojulpote, et al. · Journal of Nuclear Cardiology · 16
A Cloud-Based System for Automated AI Image Analysis and Reporting
2024 · Neil Chatterjee, Jeffrey Duda, et al. · Journal of Imaging Informatics in Medicine · 12
CT imaging-derived phenotypes for abdominal muscle and their association with age and sex in a medical biobank
2024 · Phuong T. Vu, Chantal Chahine, et al. · Scientific Reports · 10
Automated, calibration-free quantification of cortical bone porosity and geometry in postmenopausal osteoporosis from ultrashort echo time MRI and deep learning
2023 · Brandon C. Jones, Felix W. Wehrli, et al. · Bone · 8
Classification of Myocardial 18F-FDG PET Uptake Patterns Using Deep Learning
2021 · Nicholas Josselyn, Matthew T. MacLean, et al. · Radiology Artificial Intelligence · 5
Linking abdominal imaging traits to electronic health record phenotypes
2020 · Matthew T. MacLean, Qasim Jehangir, et al. · medRxiv · 4
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Clinical correlates of CT imaging-derived phenotypes among lean and overweight patients with hepatic steatosis
2024 · Isabel Song, Elizabeth W. Thompson, et al. · Scientific Reports · 3
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Optimizing Medical Student Clerkship Schedules Using a Novel Application of the Hungarian Algorithm
2020 · Matthew T. MacLean, Jerzy R. Lysikowski, et al. · Academic Medicine · 3
SynthA1c: Towards Clinically Interpretable Patient Representations for Diabetes Risk Stratification
2023 · Michael S. Yao, Allison Chae, et al. · Lecture notes in computer science · 2
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Hepatic and abdominal adiposity in type 2 diabetes as assessed with machine learning on computed tomography scans
2026 · Richard H. Tran, Pavan Raghupathy, et al. · Diabetes Obesity and Metabolism
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Hepatic and abdominal adiposity in type 2 diabetes as assessed with machine learning on CT scans
2025 · Richard H. Tran, Pavan Raghupathy, et al. · medRxiv
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Automated, reference-free quantification of cortical bone parameters detects impairments in postmenopausal osteoporosis
2024 · Brandon Jones, Felix Wehrli, et al. · Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition/Proceedings of the International Society for Magnetic Resonance in Medicine, Scientific Meeting and Exhibition
Hepatic Fat Inferencing Code - Exome-wide analysis of hepatic fat derived from clinical CT imaging in a medical biobank
2022 · Park, Joseph, MacLean, Matthew, et al. · Zenodo (CERN European Organization for Nuclear Research)
Automatic classification of myocardial 18-FDG uptake patterns using deep learning
2020 · Nicholas Josselyn, Matthew T. MacLean, et al. · Journal of Nuclear Medicine
Works count: 20
Citation count: 592
H-index: 9
I10-index: 9
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