Detalhe da pesquisa
1.
Machine Learning for Adrenal Gland Segmentation and Classification of Normal and Adrenal Masses at CT.
Radiology
; 306(2): e220101, 2023 Feb.
Artigo
em Inglês
| MEDLINE | ID: mdl-36125375
2.
Implementation of Clinical Artificial Intelligence in Radiology: Who Decides and How?
Radiology
; 305(3): 555-563, 2022 12.
Artigo
em Inglês
| MEDLINE | ID: mdl-35916673
3.
Use of Artificial Intelligence in Clinical Neurology.
Semin Neurol
; 42(1): 39-47, 2022 02.
Artigo
em Inglês
| MEDLINE | ID: mdl-35576929
4.
Multicenter Assessment of CT Pneumonia Analysis Prototype for Predicting Disease Severity and Patient Outcome.
J Digit Imaging
; 34(2): 320-329, 2021 Apr.
Artigo
em Inglês
| MEDLINE | ID: mdl-33634416
5.
Using DICOM Metadata for Radiological Image Series Categorization: a Feasibility Study on Large Clinical Brain MRI Datasets.
J Digit Imaging
; 33(3): 747-762, 2020 06.
Artigo
em Inglês
| MEDLINE | ID: mdl-31950302
6.
Implementation of Clinical Artificial Intelligence in Radiology: Who Decides and How?
Radiology
; 305(1): E62, 2022 Oct.
Artigo
em Inglês
| MEDLINE | ID: mdl-36154286
7.
Primary care provider perspectives on the value of opportunistic CT screening.
Clin Imaging
; 112: 110210, 2024 Jun 01.
Artigo
em Inglês
| MEDLINE | ID: mdl-38850710
8.
No code machine learning: validating the approach on use-case for classifying clavicle fractures.
Clin Imaging
; 112: 110207, 2024 May 31.
Artigo
em Inglês
| MEDLINE | ID: mdl-38838448
9.
Evaluation of an artificial intelligence model for identification of mass effect and vasogenic edema on computed tomography of the head.
AJNR Am J Neuroradiol
; 2024 May 28.
Artigo
em Inglês
| MEDLINE | ID: mdl-38806239
10.
Neuropsychiatric lupus: classification criteria in neuroimaging studies.
Can J Neurol Sci
; 40(3): 284-91, 2013 May.
Artigo
em Inglês
| MEDLINE | ID: mdl-23603162
11.
Automatic segmentation and measurement of tracheal collapsibility in tracheomalacia.
Clin Imaging
; 95: 47-51, 2023 Mar.
Artigo
em Inglês
| MEDLINE | ID: mdl-36610270
12.
Auto-Detection of Motion Artifacts on CT Pulmonary Angiograms with a Physician-Trained AI Algorithm.
Diagnostics (Basel)
; 13(4)2023 Feb 18.
Artigo
em Inglês
| MEDLINE | ID: mdl-36832266
13.
Addressing the Challenges of Implementing Artificial Intelligence Tools in Clinical Practice: Principles From Experience.
J Am Coll Radiol
; 20(3): 352-360, 2023 03.
Artigo
em Inglês
| MEDLINE | ID: mdl-36922109
14.
Radiologist-Trained AI Model for Identifying Suboptimal Chest-Radiographs.
Acad Radiol
; 30(12): 2921-2930, 2023 12.
Artigo
em Inglês
| MEDLINE | ID: mdl-37019698
15.
Head CT deep learning model is highly accurate for early infarct estimation.
Sci Rep
; 13(1): 189, 2023 01 05.
Artigo
em Inglês
| MEDLINE | ID: mdl-36604467
16.
Predictive values of AI-based triage model in suboptimal CT pulmonary angiography.
Clin Imaging
; 86: 25-30, 2022 Jun.
Artigo
em Inglês
| MEDLINE | ID: mdl-35316621
17.
Computer-assisted Reporting and Decision Support Increases Compliance with Follow-up Imaging and Hormonal Screening of Adrenal Incidentalomas.
Acad Radiol
; 29(2): 236-244, 2022 02.
Artigo
em Inglês
| MEDLINE | ID: mdl-33583714
18.
FDA-regulated AI Algorithms: Trends, Strengths, and Gaps of Validation Studies.
Acad Radiol
; 29(4): 559-566, 2022 04.
Artigo
em Inglês
| MEDLINE | ID: mdl-34969610
19.
Radiologist-Trained and -Tested (R2.2.4) Deep Learning Models for Identifying Anatomical Landmarks in Chest CT.
Diagnostics (Basel)
; 12(8)2022 Jul 30.
Artigo
em Inglês
| MEDLINE | ID: mdl-36010194
20.
Validation pipeline for machine learning algorithm assessment for multiple vendors.
PLoS One
; 17(4): e0267213, 2022.
Artigo
em Inglês
| MEDLINE | ID: mdl-35486572