scholarly journals O02 THE ROLE OF DEEP LEARNING IN PREDICTING COMPLEXITY AND COMPLICATIONS IN ABDOMINAL WALL RECONSTRUCTION

2021 ◽  
Vol 108 (Supplement_8) ◽  
Author(s):  
Sharbel Elhage ◽  
Sullivan Ayuso ◽  
Yizi Zhang ◽  
Eva Deerenberg ◽  
Vedra Augenstein ◽  
...  
Keyword(s):  
Deep Learning ◽  
Abdominal Wall Reconstruction ◽  
Management Tool ◽  
Ct Scans ◽  
Pulmonary Failure ◽  
Image Characteristics ◽  
Tertiary Center ◽  
Surgical Complexity ◽  
Operative Planning ◽  
Validation Set

Abstract Aim The aim of our study was to evaluate the utility of image-based deep learning models (DLMs) to predict surgical complexity and postoperative outcomes in patients undergoing AWR. Material and Methods A prospective, tertiary center, hernia database was queried for open AWR patients with adequate pre-operative CT-scans. An 8-layer convolutional neural network (CNN) analyzed image characteristics in Python utilizing the open source Tensorflow© and OpenCV frameworks. Images were analyzed and batched into a training set (80%) and validation set (20%) used to analyze the model output, which was blinded to the CNN until testing. DLMs were run to assess surgical complexity based on need for component separation, surgical site infection (SSI), and pulmonary failure. The surgical complexity DLM was validated by comparison to 6 expert AWR surgeons. Results In total, 369 patient CT scans were utilized. The surgical complexity DLM performed well (ROC=0.744;p<0.0001), and when compared to surgeon prediction on the validation set, performed better with an accuracy of 81.3% compared to 65.0% (p < 0.0001). The SSI DLM was successful with an ROC of 0.898 (p < 0.0001). The DLM for predicting pulmonary failure was less effective with an ROC of 0.545 (p = 0.03). Conclusions DLMs were able to successfully predict surgical complexity and were more accurate than expert surgeons using objective, pre-operative imaging. DLMs were also successful in predicting SSI. This breakthrough may allow for enhanced pre-operative planning, including resource utilization and possible need for tertiary center referral. AI appears to be an exciting new management tool in complex AWR.

scholarly journals Recognition of COVID-19 from CT Scans Using Two-Stage Deep-Learning-Based Approach: CNR-IEMN

Sensors ◽  
2021 ◽  
Vol 21 (17) ◽  
pp. 5878 ◽  
Author(s):  
Fares Bougourzi ◽  
Riccardo Contino ◽  
Cosimo Distante ◽  
Abdelmalik Taleb-Ahmed
Keyword(s):  
Deep Learning ◽  
Community Acquired Pneumonia ◽  
Research Topic ◽  
The Other ◽  
Ct Scans ◽  
Second Stage ◽  
Volumetric Ct ◽  
Active Research ◽  
Validation Set ◽  
Learning Architectures

Since the appearance of the COVID-19 pandemic (at the end of 2019, Wuhan, China), the recognition of COVID-19 with medical imaging has become an active research topic for the machine learning and computer vision community. This paper is based on the results obtained from the 2021 COVID-19 SPGC challenge, which aims to classify volumetric CT scans into normal, COVID-19, or community-acquired pneumonia (Cap) classes. To this end, we proposed a deep-learning-based approach (CNR-IEMN) that consists of two main stages. In the first stage, we trained four deep learning architectures with a multi-tasks strategy for slice-level classification. In the second stage, we used the previously trained models with an XG-boost classifier to classify the whole CT scan into normal, COVID-19, or Cap classes. Our approach achieved a good result on the validation set, with an overall accuracy of 87.75% and 96.36%, 52.63%, and 95.83% sensitivities for COVID-19, Cap, and normal, respectively. On the other hand, our approach achieved fifth place on the three test datasets of SPGC in the COVID-19 challenge, where our approach achieved the best result for COVID-19 sensitivity. In addition, our approach achieved second place on two of the three testing sets.

Development and Validation of Image-Based Deep Learning Models to Predict Surgical Complexity and Complications in Abdominal Wall Reconstruction

JAMA Surgery ◽  
2021 ◽  
Author(s):  
Sharbel Adib Elhage ◽  
Eva Barbara Deerenberg ◽  
Sullivan Armando Ayuso ◽  
Keith Joseph Murphy ◽  
Jenny Meng Shao ◽  
...  
Keyword(s):  
Deep Learning ◽  
Abdominal Wall ◽  
Abdominal Wall Reconstruction ◽  
Learning Models ◽  
Surgical Complexity ◽  
Development And Validation

scholarly journals Lung Segmentation and Characterization in COVID-19 Patients for Assessing Pulmonary Thromboembolism: An Approach Based on Deep Learning and Radiomics

Electronics ◽  
2021 ◽  
Vol 10 (20) ◽  
pp. 2475
Author(s):  
Vitoantonio Bevilacqua ◽  
Nicola Altini ◽  
Berardino Prencipe ◽  
Antonio Brunetti ◽  
Laura Villani ◽  
...  
Keyword(s):  
Deep Learning ◽  
Pulmonary Thromboembolism ◽  
Ct Scans ◽  
Dice Coefficient ◽  
Training Set ◽  
Preliminary Study ◽  
Validation Set ◽  
Segmentation Task ◽  
The Relationship

The COVID-19 pandemic is inevitably changing the world in a dramatic way, and the role of computed tomography (CT) scans can be pivotal for the prognosis of COVID-19 patients. Since the start of the pandemic, great care has been given to the relationship between interstitial pneumonia caused by the infection and the onset of thromboembolic phenomena. In this preliminary study, we collected n = 20 CT scans from the Polyclinic of Bari, all from patients positive with COVID-19, nine of which developed pulmonary thromboembolism (PTE). For eight CT scans, we obtained masks of the lesions caused by the infection, annotated by expert radiologists; whereas for the other four CT scans, we obtained masks of the lungs (including both healthy parenchyma and lesions). We developed a deep learning-based segmentation model that utilizes convolutional neural networks (CNNs) in order to accurately segment the lung and lesions. By considering the images from publicly available datasets, we also realized a training set composed of 32 CT scans and a validation set of 10 CT scans. The results obtained from the segmentation task are promising, allowing to reach a Dice coefficient higher than 97%, posing the basis for analysis concerning the assessment of PTE onset. We characterized the segmented region in order to individuate radiomic features that can be useful for the prognosis of PTE. Out of 919 extracted radiomic features, we found that 109 present different distributions according to the Mann–Whitney U test with corrected p-values less than 0.01. Lastly, nine uncorrelated features were retained that can be exploited to realize a prognostic signature.

PO-1718: Deep learning based auto-contouring of planning CT scans for rectal cancer

2020 ◽  
Vol 152 ◽  
pp. S949
Author(s):  
L. Bokhorst ◽  
M.H.F. Savenije ◽  
M.P.W. Intven ◽  
C.A.T. Van den Berg
Keyword(s):  
Rectal Cancer ◽  
Deep Learning ◽  
Ct Scans ◽  
Planning Ct

scholarly journals A Deep Learning Approach For Improved Segmentation Of Lesions Related To Covid-19 Chest CT Scans

2021 ◽  
Author(s):  
Vlad Vasilescu ◽  
Ana Neacsu ◽  
Emilie Chouzenoux ◽  
Jean-Christophe Pesquet ◽  
Corneliu Burileanu
Keyword(s):  
Deep Learning ◽  
Chest Ct ◽  
Ct Scans ◽  
Learning Approach

scholarly journals ONE OR TWO SCREWS IN THE FRACTURE OF THE ODONTOID PROCESS? EVALUATION USING COMPUTED TOMOGRAPHY

2015 ◽  
Vol 14 (3) ◽  
pp. 210-213
Author(s):  
Carlos Alexandre Botelho do Amaral ◽  
Edgar Taira Nakagawa ◽  
Leonardo Ternis Ferreira ◽  
José Sergio Franco ◽  
Robinson Esteves Santos Pires ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Process Evaluation ◽  
Odontoid Process ◽  
T Test ◽  
Ct Scans ◽  
Transverse Diameter ◽  
Bone Area ◽  
Men And Women ◽  
Male And Female ◽  
Operative Planning

Objective:To evaluate the bone area of the odontoid process through computed tomography and its relation with the area of one and two screws in the male and female subjects.Methods:188 CT scans of adults were analyzed. The isthmus area was selected and the transverse diameter was measured at 1.2 mm from the base of odontoid.Results:After placement of a screw, the odontoid area remains with 82% of free bone for both men and women. With two screws, 45.6% of women, had a free bone area of the odontoid process between 50% and 75% and 54.4% were above 75%. 26.6% men had percentage from 50% to 75% of free bone area and 73.4% above 75% (p=0.07). After the placement of two screws, the bone area was, in average, 77.3% in men and 75.4% in women. Using the Student t-test, the differences between the average of percentage of free bone area in men and women are significantly lower in women (p=0.0012).Conclusion:The pre-operative planning through CT can help to choose the number of screws in the odontoid process. The choice should be particularly careful when using two screws in women.

scholarly journals Deep Learning Assisted Covid-19 Detection using full CT-scans

2020 ◽  
Author(s):  
varan singhrohila ◽  
Nitin Gupta ◽  
Amit Kaul ◽  
Deepak Sharma
Keyword(s):  
Deep Learning ◽  
Ct Scans ◽  
Detection Accuracy ◽  
Residual Network ◽  
Excellent Performance ◽  
Examination Procedure ◽  
Medical Institutions ◽  
Proposed Model ◽  
Learning Technique ◽  
Intensive Approach

<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>

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