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10.1364/OE.442321 http://scihub22266oqcxt.onion/10.1364/OE.442321 35209327!ä!35209327 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 245.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 245.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Opt+Express 2022 ; 30 (2): 1723-1736 Nephropedia Template TP {{tp|p=35209327|t=2022. COVID-19 detection from red blood cells using highly comparative time-series analysis (HCTSA) in digital holographic microscopy |pdf=|usr=}}{{35209327}} gab.com Text COVID-19 detection from red blood cells using highly comparative time-series analysis (HCTSA) in digital holographic microscopy JO: Opt Express http://www.kidney.de/pget.php?&tw=1&pmid=35209327 #FOAvip We present an automated method for COVID-19 screening based on reconstructed phase profiles of red blood cells (RBCs) and a highly comparative time-series analysis (HCTSA). Video digital holographic data -was obtained using a compact, field-portable shearing microscope to capture the temporal fluctuations and spatio-temporal dynamics of live RBCs. After numerical reconstruction of the digital holographic data, the optical volume is calculated at each timeframe of the reconstructed data to produce a time-series signal for each cell in our dataset. Over 6000 features are extracted on the time-varying optical volume sequences using the HCTSA to quantify the spatio-temporal behavior of the RBCs, then a linear support vector machine is used for classification of individual RBCs. Human subjects are then classified for COVID-19 based on the consensus of their cells' classifications. The proposed method is tested on a dataset of 1472 RBCs from 24 human subjects (10 COVID-19 positive, 14 healthy) collected at UConn Health Center. Following a cross-validation procedure, our system achieves 82.13% accuracy, with 92.72% sensitivity, and 73.21% specificity (area under the receiver operating characteristic curve: 0.8357). Furthermore, the proposed system resulted in 21 out of 24 human subjects correctly labeled. To the best of our knowledge this is the first report of a highly comparative time-series analysis using digital holographic microscopy data. Twit Text FOAVip COVID-19 detection from red blood cells using highly comparative time-series analysis (HCTSA) in digital holographic microscopy ????Opt Express http://www.kidney.de/pget.php?&tw=1&pmid=35209327 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=35209327 #FOAvip English Wikipedia <ref>{{Cite pmid|35209327}}</ref> COVID-19 detection from red blood cells using highly comparative time-series analysis (HCTSA) in digital holographic microscopy #MMPMID35209327 O'Connor T; Santaniello S; Javidi B Opt Express 2022[Jan]; 30 (2): 1723-1736 PMID35209327show ga We present an automated method for COVID-19 screening based on reconstructed phase profiles of red blood cells (RBCs) and a highly comparative time-series analysis (HCTSA). Video digital holographic data -was obtained using a compact, field-portable shearing microscope to capture the temporal fluctuations and spatio-temporal dynamics of live RBCs. After numerical reconstruction of the digital holographic data, the optical volume is calculated at each timeframe of the reconstructed data to produce a time-series signal for each cell in our dataset. Over 6000 features are extracted on the time-varying optical volume sequences using the HCTSA to quantify the spatio-temporal behavior of the RBCs, then a linear support vector machine is used for classification of individual RBCs. Human subjects are then classified for COVID-19 based on the consensus of their cells' classifications. The proposed method is tested on a dataset of 1472 RBCs from 24 human subjects (10 COVID-19 positive, 14 healthy) collected at UConn Health Center. Following a cross-validation procedure, our system achieves 82.13% accuracy, with 92.72% sensitivity, and 73.21% specificity (area under the receiver operating characteristic curve: 0.8357). Furthermore, the proposed system resulted in 21 out of 24 human subjects correctly labeled. To the best of our knowledge this is the first report of a highly comparative time-series analysis using digital holographic microscopy data. |COVID-19/blood/*diagnostic imaging[MESH] |Case-Control Studies[MESH] |Equipment Design[MESH] |Erythrocytes/*classification[MESH] |Holography/instrumentation/*methods[MESH] |Humans[MESH] |Intravital Microscopy/instrumentation/*methods[MESH] |Preliminary Data[MESH] |ROC Curve[MESH]COVID-19 detection from red blood cells using highly comparative time-(epmc).pdf DeepDyve Pubget Overpricing