scholarly journals Automated Image Analysis Software: Valuable Tools for the Future?

2017 ◽  
Vol 2 (1) ◽  
pp. e5-e6
Author(s):  
Digvijay Singh ◽  
Keyword(s):  
Image Analysis ◽  
Automated Image Analysis ◽  
Analysis Software ◽  
Image Analysis Software ◽  
The Future

scholarly journals Automated three-dimensional measurements of version, inclination, and subluxation

2019 ◽  
Vol 12 (1) ◽  
pp. 31-37
Author(s):  
Dave R Shukla ◽  
Richard J McLaughlin ◽  
Julia Lee ◽  
Ngoc Tram V Nguyen ◽  
Joaquin Sanchez-Sotelo
Keyword(s):  
Computed Tomography ◽  
Image Analysis ◽  
Three Dimensional ◽  
Automated Image Analysis ◽  
Two Dimensional ◽  
Analysis Software ◽  
Glenoid Version ◽  
Image Analysis Software ◽  
Planning Software ◽  
Dimensional Measurements

Background Preoperative planning software has been developed to measure glenoid version, glenoid inclination, and humeral head subluxation on computed tomography (CT) for shoulder arthroplasty. However, most studies analyzing the effect of glenoid positioning on outcome were done prior to the introduction of planning software. Thus, measurements obtained from the software can only be extrapolated to predict failure provided they are similar to classic measurements. The purpose of this study was to compare measurements obtained using classic manual measuring techniques and measurements generated from automated image analysis software. Methods Ninety-five two-dimensional computed tomography scans of shoulders with primary glenohumeral osteoarthritis were measured for version according to Friedman method, inclination according to Maurer method, and subluxation according to Walch method. DICOM files were loaded into an image analysis software (Blueprint, Wright Medical) and the output was compared with values obtained manually using a paired sample t-test. Results Average manual measurements included 13.8° version, 13.2° inclination, and 56.2% subluxation. Average image analysis software values included 17.4° version (3.5° difference, p < 0.0001), 9.2° inclination (3.9° difference, p < 0.001), and 74.2% for subluxation (18% difference, p < 0.0001). Conclusions Glenoid version and inclination values from the software and manual measurement on two-dimensional computed tomography were relatively similar, within approximately 4°. However, subluxation measurements differed by approximately 20%.

scholarly journals Machine Learning Takes Laboratory Automation to the Next Level

2020 ◽  
Vol 58 (4) ◽  
Author(s):  
Bradley A. Ford ◽  
Erin McElvania
Keyword(s):  
Machine Learning ◽  
Image Analysis ◽  
Clinical Microbiology ◽  
Clinical Laboratory ◽  
Automated Image Analysis ◽  
Laboratory Automation ◽  
Analysis Software ◽  
Image Analysis Software ◽  
Specimen Type ◽  
Link Type

ABSTRACT Clinical microbiology laboratories face challenges with workload and understaffing that other clinical laboratory sections have addressed with automation. In this issue of the Journal of Clinical Microbiology, M. L. Faron, B. W. Buchan, R. F. Relich, J. Clark, and N. A. Ledeboer (J Clin Microbiol 58:e01683-19, 2020, https://doi.org/10.1128/JCM.01683-19) evaluate the performance of automated image analysis software to screen urine cultures for further workup according to their total number of CFU. Urine cultures are the highest volume specimen type for most laboratories, so this software has the potential for tremendous gains in laboratory efficiency and quality due to the consistency of colony quantification.

Liver steatosis assessment: Correlations among pathology, radiology, clinical data and automated image analysis software

2013 ◽  
Vol 209 (6) ◽  
pp. 371-379 ◽  
Author(s):  
Michael J. Lee ◽  
Pelin Bagci ◽  
Jun Kong ◽  
Miriam B. Vos ◽  
Puneet Sharma ◽  
...  
Keyword(s):  
Image Analysis ◽  
Clinical Data ◽  
Liver Steatosis ◽  
Automated Image Analysis ◽  
Analysis Software ◽  
Image Analysis Software

scholarly journals Evaluation of automated image analysis software for the detection of diabetic retinopathy to reduce the ophthalmologists' workload

2014 ◽  
Vol 93 (1) ◽  
pp. e52-e56 ◽  
Author(s):  
Enrique Soto-Pedre ◽  
Amparo Navea ◽  
Saray Millan ◽  
Maria C. Hernaez-Ortega ◽  
Jesús Morales ◽  
...  
Keyword(s):  
Image Analysis ◽  
Diabetic Retinopathy ◽  
Automated Image Analysis ◽  
Analysis Software ◽  
Image Analysis Software
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