Predicting donor heart function in a heartbeat

2020 ◽  
Vol 12 (538) ◽  
pp. eabb5667
Author(s):  
Steven P. Keller
Keyword(s):  
Machine Learning ◽  
Cardiac Function ◽  
Ex Vivo ◽  
Heart Function ◽  
Donor Heart ◽  
Learning Methods ◽  
Ex Vivo Perfusion ◽  
Machine Learning Methods

Machine learning methods can assess contractility of donor hearts maintained via ex vivo perfusion to predict posttransplant cardiac function.

A NOVEL EXTENSIVE EX-VIVO OCT DATABASE FROM MURINE MODELS OF COLORECTAL CANCER

2021 ◽  
Vol 108 (Supplement_3) ◽  
Author(s):  
J Bote ◽  
J F Ortega-Morán ◽  
C L Saratxaga ◽  
B Pagador ◽  
A Picón ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Machine Learning ◽  
Structural Information ◽  
Ex Vivo ◽  
Ground Truth ◽  
Colon Polyps ◽  
Learning Methods ◽  
Non Invasive ◽  
Machine Learning Methods ◽  
In Situ Methods

Abstract INTRODUCTION New non-invasive technologies for improving early diagnosis of colorectal cancer (CRC) are demanded by clinicians. Optical Coherence Tomography (OCT) provides sub-surface structural information and offers diagnosis capabilities of colon polyps, further improved by machine learning methods. Databases of OCT images are necessary to facilitate algorithms development and testing. MATERIALS AND METHODS A database has been acquired from rat colonic samples with a Thorlabs OCT system with 930nm centre wavelength that provides 1.2KHz A-scan rate, 7μm axial resolution in air, 4μm lateral resolution, 1.7mm imaging depth in air, 6mm x 6mm FOV, and 107dB sensitivity. The colon from anaesthetised animals has been excised and samples have been extracted and preserved for ex-vivo analysis with the OCT equipment. RESULTS This database consists of OCT 3D volumes (C-scans) and 2D images (B-scans) of murine samples from: 1) healthy tissue, for ground-truth comparison (18 samples; 66 C-scans; 17,478 B-scans); 2) hyperplastic polyps, obtained from an induced colorectal hyperplastic murine model (47 samples; 153 C-scans; 42,450 B-scans); 3) neoplastic polyps (adenomatous and adenocarcinomatous), obtained from clinically validated Pirc F344/NTac-Apcam1137 rat model (232 samples; 564 C-scans; 158,557 B-scans); and 4) unknown tissue (polyp adjacent, presumably healthy) (98 samples; 157 C-scans; 42,070 B-scans). CONCLUSIONS A novel extensive ex-vivo OCT database of murine CRC model has been obtained and will be openly published for the research community. It can be used for classification/segmentation machine learning methods, for correlation between OCT features and histopathological structures, and for developing new non-invasive in-situ methods of diagnosis of colorectal cancer.

scholarly journals Impact of Myocardial Load on the Preservation of Donor Heart Function during Ex Vivo Perfusion

2016 ◽  
Vol 35 (4) ◽  
pp. S186 ◽  
Author(s):  
C.W. White ◽  
S. Shan ◽  
S. Hatami ◽  
V. Gurtu ◽  
A. Kinnaird ◽  
...  
Keyword(s):  
Ex Vivo ◽  
Heart Function ◽  
Donor Heart ◽  
Ex Vivo Perfusion

scholarly journals EX VIVO PERFUSION IN A LOADED STATE IMPROVES THE PRESERVATION OF DONOR HEART FUNCTION

2015 ◽  
Vol 31 (10) ◽  
pp. S202 ◽  
Author(s):  
C.W. White ◽  
S. Shan ◽  
S. Hatami ◽  
V. Gurtu ◽  
A. Kinnaird ◽  
...  
Keyword(s):  
Ex Vivo ◽  
Heart Function ◽  
Donor Heart ◽  
Ex Vivo Perfusion ◽  
Loaded State

Application of machine learning methods for automatic interpretation of open hole logging data

2020 ◽  
pp. 44-47
Author(s):  
M.A. Basyrov ◽  
◽  
A.V. Akinshin ◽  
I.R. Makhmutov ◽  
Yu.D. Kantemirov ◽  
...  
Keyword(s):  
Machine Learning ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Automatic Interpretation ◽  
Open Hole
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