scholarly journals Detection of Overlooked Pulmonary Metastases in Serial CT Scans through Deep Learning-based Tracking of Longitudinal Changes

Author(s):  
Junghoon Kim ◽  
Dong Yul Oh ◽  
Kyong Joon Lee
2020 ◽  
Vol 152 ◽  
pp. S949
Author(s):  
L. Bokhorst ◽  
M.H.F. Savenije ◽  
M.P.W. Intven ◽  
C.A.T. Van den Berg

Author(s):  
Vlad Vasilescu ◽  
Ana Neacsu ◽  
Emilie Chouzenoux ◽  
Jean-Christophe Pesquet ◽  
Corneliu Burileanu

2021 ◽  
Author(s):  
T Winkens ◽  
A Christl ◽  
C Kühnel ◽  
F Ndum ◽  
M Freesmeyer

2021 ◽  
pp. 000313482110635
Author(s):  
Jordan Perkins ◽  
Jacob Shreffler ◽  
Danielle Kamenec ◽  
Alexandra Bequer ◽  
Corey Ziemba ◽  
...  

Background: Many patients undergo two head computed tomography (CT) scans after mild traumatic brain injury (TBI). Radiographic progression without clinical deterioration does not usually alter management. Evidence-based guidelines offer potential for limited repeat imaging and safe discharge. This study characterizes patients who had two head CTs in the Emergency Department (ED), determines the change between initial and repeat CTs, and describes timing of repeat scans. Methods: This retrospective series includes all patients with head CTs during the same ED visit at an urban trauma center between May 1st, 2016 and April 30th, 2018. Radiographic interpretation was coded as positive, negative, or equivocal. Results: Of 241 subjects, the number of positive, negative, and equivocal initial CT results were 154, 50, and 37, respectively. On repeat CT, 190 (78.8%) interpretations were congruent with the original scan. Out of the 21.2% of repeat scans that diverged from the original read, 14 (5.8%) showed positive to negative conversion, 1 (.4%) showed positive to equivocal conversion, 2 (.88%) showed negative to positive conversion, 20 (8.3%) showed equivocal to negative conversion, and 14 (5.8%) showed equivocal to positive conversion. Average time between scans was 4.4 hours, and median length of stay was 10.2 hours. Conclusions: In this retrospective review, most repeat CT scans had no new findings. A small percentage converted to positive, rarely altering clinical management. This study demonstrates the need for continued prospective research to update clinical guidelines that could reduce admission and serial CT scanning for mild TBI.


2020 ◽  
Author(s):  
varan singhrohila ◽  
Nitin Gupta ◽  
Amit Kaul ◽  
Deepak Sharma

<div>The ongoing pandemic of COVID-19 has shown</div><div>the limitations of our current medical institutions. There</div><div>is a need for research in the field of automated diagnosis</div><div>for speeding up the process while maintaining accuracy</div><div>and reducing computational requirements. In this work, an</div><div>automatic diagnosis of COVID-19 infection from CT scans</div><div>of the patients using Deep Learning technique is proposed.</div><div>The proposed model, ReCOV-101 uses full chest CT scans to</div><div>detect varying degrees of COVID-19 infection, and requires</div><div>less computational power. Moreover, in order to improve</div><div>the detection accuracy the CT-scans were preprocessed by</div><div>employing segmentation and interpolation. The proposed</div><div>scheme is based on the residual network, taking advantage</div><div>of skip connection, allowing the model to go deeper.</div><div>Moreover, the model was trained on a single enterpriselevel</div><div>GPU such that it can easily be provided on the edge of</div><div>the network, reducing communication with the cloud often</div><div>required for processing the data. The objective of this work</div><div>is to demonstrate a less hardware-intensive approach for COVID-19 detection with excellent performance that can</div><div>be combined with medical equipment and help ease the</div><div>examination procedure. Moreover, with the proposed model</div><div>an accuracy of 94.9% was achieved.</div>


2021 ◽  
Vol 66 (3) ◽  
pp. 2923-2938
Author(s):  
Muhammad Attique Khan ◽  
Nazar Hussain ◽  
Abdul Majid ◽  
Majed Alhaisoni ◽  
Syed Ahmad Chan Bukhari ◽  
...  
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