Jason M. Davies

According to our database1, Jason M. Davies authored at least 20 papers between 2014 and 2023.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

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Bibliography

2023
Investigating angiographic injection parameters for cerebral aneurysm hemodynamic characterization using patient-specific simulated angiograms.
Proceedings of the Medical Imaging 2023: Biomedical Applications in Molecular, 2023

2022
Predicting hematoma expansion after spontaneous intracranial hemorrhage through a radiomics based model.
Proceedings of the Medical Imaging 2022: Computer-Aided Diagnosis, 2022

Quantitative angiography prognosis of intracranial aneurysm treatment failure using parametric imaging and distal vessel analysis.
Proceedings of the Medical Imaging 2022: Biomedical Applications in Molecular, 2022

Initial investigation of predicting hematoma expansion for intracerebral hemorrhage using imaging biomarkers and machine learning.
Proceedings of the Medical Imaging 2022: Biomedical Applications in Molecular, 2022

Initial investigation of the use of angiographic parametric imaging for early prognosis of delayed cerebral ischemia in patients with subarachnoid hemorrhage.
Proceedings of the Medical Imaging 2022: Biomedical Applications in Molecular, 2022

Prognosis of ischemia recurrence in patients with moderate intracranial atherosclerotic disease using quantitative MRA measurements.
Proceedings of the Medical Imaging 2022: Biomedical Applications in Molecular, 2022

Leveraging patient-specific simulated angiograms to characterize cerebral aneurysm hemodynamics using computational fluid dynamics.
Proceedings of the Medical Imaging 2022: Biomedical Applications in Molecular, 2022

2021
Fast virtual coiling algorithm for intracranial aneurysms using pre-shape path planning.
Comput. Biol. Medicine, 2021

Use of a convolutional neural network to identify infarct core using computed tomography perfusion parameters.
Proceedings of the Medical Imaging 2021: Image Processing, Online, February 15-19, 2021, 2021

Clot organization on histology is associated with radiomics features that predict treatment outcomes from mechanical thrombectomy.
Proceedings of the Medical Imaging 2021: Digital Pathology, Online, February 15-19, 2021, 2021

Use of biplane quantitative angiographic imaging with ensemble neural networks to assess reperfusion status during mechanical thrombectomy.
Proceedings of the Medical Imaging 2021: Computer-Aided Diagnosis, 2021

2020
Predicting treatment outcome of intracranial aneurysms using angiographic parametric imaging and recurrent neural networks.
Proceedings of the Medical Imaging 2020: Computer-Aided Diagnosis, 2020

Quantification of flow through intracranial arteriovenous malformations using Angiographic Parametric Imaging (API).
Proceedings of the Medical Imaging 2020: Biomedical Applications in Molecular, 2020

Optimization of DSA image data input to a machine learning aneurysm identifier.
Proceedings of the Medical Imaging 2020: Biomedical Applications in Molecular, 2020

Co-registration of infarct core location with angiography during mechanical thrombectomy procedures for treatment progression monitoring.
Proceedings of the Medical Imaging 2020: Biomedical Applications in Molecular, 2020

2019
Feasibility study of deep neural networks to classify intracranial aneurysms using angiographic parametric imaging.
Proceedings of the Medical Imaging 2019: Computer-Aided Diagnosis, 2019

Initial assessment of neuro pressure gradients in carotid stenosis using 3D printed patient-specific phantoms.
Proceedings of the Medical Imaging 2019: Biomedical Applications in Molecular, 2019

2017
Association between hemodynamic modifications and clinical outcome of intracranial aneurysms treated using flow diverters.
Proceedings of the Medical Imaging 2017: Image-Guided Procedures, 2017

Computer-assisted adjuncts for aneurysmal morphologic assessment: toward more precise and accurate approaches.
Proceedings of the Medical Imaging 2017: Computer-Aided Diagnosis, 2017

2014
Research and applications: N-gram support vector machines for scalable procedure and diagnosis classification, with applications to clinical free text data from the intensive care unit.
J. Am. Medical Informatics Assoc., 2014


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