scholarly journals Diagnostic Performance of a New Artificial-Intelligence Driven Diagnostic Support Tool: Board-Exams Clinical Vignette Study (Preprint)

10.2196/32507 ◽  
2021 ◽  
Author(s):  
Niv Ben-Shabat ◽  
Arial Sloma ◽  
Tomer Weizman ◽  
David Kiderman ◽  
Howard Amital
Keyword(s):  
Artificial Intelligence ◽  
Diagnostic Performance ◽  
Clinical Vignette ◽  
Support Tool ◽  
Vignette Study ◽  
Diagnostic Support

Evaluating Diagnostic Accuracy of a New Artificial-Intelligence Driven Diagnostic Support Tool

2021 ◽  
Author(s):  
Niv Ben-Shabat ◽  
Ariel Sloma ◽  
Tomer Weizman ◽  
David Kiderman ◽  
Howard Amital
Keyword(s):  
Artificial Intelligence ◽  
Diagnostic Accuracy ◽  
Support Tool ◽  
Diagnostic Support

scholarly journals Diagnostic performance of artificial intelligence for histologic melanoma recognition compared to 18 international expert pathologists

2021 ◽  
Author(s):  
Titus J. Brinker ◽  
Max Schmitt ◽  
Eva I. Krieghoff-Henning ◽  
Raymond Barnhill ◽  
Helmut Beltraminelli ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Diagnostic Performance ◽  
International Expert

scholarly journals Detection and Diagnosis of Breast Cancer Using Artificial Intelligence Based Assessment of Maximum Intensity Projection Dynamic Contrast-Enhanced Magnetic Resonance Images

Diagnostics ◽  
2020 ◽  
Vol 10 (5) ◽  
pp. 330
Author(s):  
Mio Adachi ◽  
Tomoyuki Fujioka ◽  
Mio Mori ◽  
Kazunori Kubota ◽  
Yuka Kikuchi ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Magnetic Resonance ◽  
Diagnostic Performance ◽  
Maximum Intensity Projection ◽  
Breast Mri ◽  
Magnetic Resonance Images ◽  
Maximum Intensity ◽  
Validation Data ◽  
Dynamic Contrast Enhanced ◽  
Contrast Enhanced

We aimed to evaluate an artificial intelligence (AI) system that can detect and diagnose lesions of maximum intensity projection (MIP) in dynamic contrast-enhanced (DCE) breast magnetic resonance imaging (MRI). We retrospectively gathered MIPs of DCE breast MRI for training and validation data from 30 and 7 normal individuals, 49 and 20 benign cases, and 135 and 45 malignant cases, respectively. Breast lesions were indicated with a bounding box and labeled as benign or malignant by a radiologist, while the AI system was trained to detect and calculate possibilities of malignancy using RetinaNet. The AI system was analyzed using test sets of 13 normal, 20 benign, and 52 malignant cases. Four human readers also scored these test data with and without the assistance of the AI system for the possibility of a malignancy in each breast. Sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) were 0.926, 0.828, and 0.925 for the AI system; 0.847, 0.841, and 0.884 for human readers without AI; and 0.889, 0.823, and 0.899 for human readers with AI using a cutoff value of 2%, respectively. The AI system showed better diagnostic performance compared to the human readers (p = 0.002), and because of the increased performance of human readers with the assistance of the AI system, the AUC of human readers was significantly higher with than without the AI system (p = 0.039). Our AI system showed a high performance ability in detecting and diagnosing lesions in MIPs of DCE breast MRI and increased the diagnostic performance of human readers.

Feasibility of streamlining an interactive Bayesian-based diagnostic support tool designed for clinical practice

2016 ◽  
Author(s):  
Po-Hao Chen ◽  
Emmanuel Botzolakis ◽  
Suyash Mohan ◽  
R. N. Bryan ◽  
Tessa Cook
Keyword(s):  
Clinical Practice ◽  
Support Tool ◽  
Diagnostic Support

An Artificial Intelligence Augmented Decision Support Tool for Estimating Probability of Malignancy of Abnormal Mammograms With Biopsy Recommendation (BI-RADS 4)

2021 ◽  
Author(s):  
Chika F. Ezeana ◽  
Mamta Puppala ◽  
T. He ◽  
Tejal A. Patel ◽  
Virginia Kaklamani ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Decision Support ◽  
Decision Support Tool ◽  
Support Tool

Gender Aspects during and after the Diagnostic Odyssey in M. Fabry: Machine Learning Diagnostic Support Tool Reveals Different Answer Patterns in Diagnostic Questionnaire

2021 ◽  
Author(s):  
P. Hahn ◽  
L. Grigull ◽  
W. Lechner ◽  
F. Klawonn ◽  
C. Lampe ◽  
...  
Keyword(s):  
Machine Learning ◽  
Support Tool ◽  
Diagnostic Odyssey ◽  
Gender Aspects ◽  
Diagnostic Support

scholarly journals Diagnostic performance of an artificial intelligence-driven cardiac-structured reporting system for myocardial perfusion SPECT imaging

2018 ◽  
Vol 27 (5) ◽  
pp. 1652-1664 ◽  
Author(s):  
Ernest V. Garcia ◽  
J. Larry Klein ◽  
Valeria Moncayo ◽  
C. David Cooke ◽  
Christian Del’Aune ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Myocardial Perfusion ◽  
Diagnostic Performance ◽  
Reporting System ◽  
Myocardial Perfusion Spect ◽  
Structured Reporting ◽  
Spect Imaging ◽  
Myocardial Perfusion Spect Imaging ◽  
Perfusion Spect

scholarly journals Patient factors that influence clinicians’ decision making in self-management support: A clinical vignette study

PLoS ONE ◽  
2017 ◽  
Vol 12 (2) ◽  
pp. e0171251 ◽  
Author(s):  
Irene D. Bos-Touwen ◽  
Jaap C. A. Trappenburg ◽  
Ineke van der Wulp ◽  
Marieke J. Schuurmans ◽  
Niek J. de Wit
Keyword(s):  
Decision Making ◽  
Clinical Vignette ◽  
Self Management ◽  
Management Support ◽  
Patient Factors ◽  
Vignette Study

scholarly journals Artificial intelligence: a new clinical support tool for stress echocardiography

2018 ◽  
Vol 15 (8) ◽  
pp. 513-515 ◽  
Author(s):  
Maryam Alsharqi ◽  
Ross Upton ◽  
Angela Mumith ◽  
Paul Leeson
Keyword(s):  
Artificial Intelligence ◽  
Stress Echocardiography ◽  
Support Tool ◽  
Clinical Support ◽  
Clinical Support Tool
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