Artificial intelligence enhances the accuracy of portal and hepatic vein extraction in computed tomography for virtual hepatectomy

2021 ◽  
Author(s):  
Yusuke Kazami ◽  
Junichi Kaneko ◽  
Deepak Keshwani ◽  
Ryugen Takahashi ◽  
Yoshikuni Kawaguchi ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Computed Tomography ◽  
Hepatic Vein ◽  
Virtual Hepatectomy

scholarly journals Predicting the Presence of an Accessory Hepatic Vein Using Abdominal Computed Tomography

2017 ◽  
Vol 35 (1) ◽  
pp. 21-25 ◽  
Author(s):  
Hyung Soo Kim ◽  
Chang Hee Lee ◽  
Seong Hyun Kim ◽  
Jeong Woo Kim ◽  
Cheol Min Park ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Hepatic Vein ◽  
Abdominal Computed Tomography ◽  
Accessory Hepatic Vein

scholarly journals Micro Computed Tomography Analysis of Four-Way Conversion Catalysts using Artificial Intelligence-Enabled Image Processing

2021 ◽  
Vol 27 (S1) ◽  
pp. 1028-1029
Author(s):  
Robert Palomino ◽  
Ke-Bin Low ◽  
Chunxin Ji ◽  
Ivan Petrovic ◽  
Florian Waltz ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Computed Tomography ◽  
Image Processing ◽  
Micro Computed Tomography ◽  
Tomography Analysis

Applications of Artificial Intelligence in 18F-Sodium Fluoride Positron Emission Tomography/Computed Tomography:

PET Clinics ◽  
2022 ◽  
Vol 17 (1) ◽  
pp. 115-135
Author(s):  
Sriram S. Paravastu ◽  
Navid Hasani ◽  
Faraz Farhadi ◽  
Michael T. Collins ◽  
Lars Edenbrandt ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Computed Tomography ◽  
Positron Emission Tomography ◽  
Sodium Fluoride ◽  
Emission Tomography ◽  
Positron Emission

An artificial intelligence algorithm that identifies middle turbinate pneumatisation (concha bullosa) on sinus computed tomography scans

2020 ◽  
Vol 134 (4) ◽  
pp. 328-331 ◽  
Author(s):  
P Parmar ◽  
A-R Habib ◽  
D Mendis ◽  
A Daniel ◽  
M Duvnjak ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Computed Tomography ◽  
Diagnostic Accuracy ◽  
Convolutional Neural Network ◽  
Middle Turbinate ◽  
Concha Bullosa ◽  
Computed Tomography Images ◽  
Computed Tomography Scans ◽  
Neural Network Algorithm

AbstractObjectiveConvolutional neural networks are a subclass of deep learning or artificial intelligence that are predominantly used for image analysis and classification. This proof-of-concept study attempts to train a convolutional neural network algorithm that can reliably determine if the middle turbinate is pneumatised (concha bullosa) on coronal sinus computed tomography images.MethodConsecutive high-resolution computed tomography scans of the paranasal sinuses were retrospectively collected between January 2016 and December 2018 at a tertiary rhinology hospital in Australia. The classification layer of Inception-V3 was retrained in Python using a transfer learning method to interpret the computed tomography images. Segmentation analysis was also performed in an attempt to increase diagnostic accuracy.ResultsThe trained convolutional neural network was found to have diagnostic accuracy of 81 per cent (95 per cent confidence interval: 73.0–89.0 per cent) with an area under the curve of 0.93.ConclusionA trained convolutional neural network algorithm appears to successfully identify pneumatisation of the middle turbinate with high accuracy. Further studies can be pursued to test its ability in other clinically important anatomical variants in otolaryngology and rhinology.

An artificial intelligence algorithm that differentiates anterior ethmoidal artery location on sinus computed tomography scans

2019 ◽  
Vol 134 (1) ◽  
pp. 52-55 ◽  
Author(s):  
J Huang ◽  
A-R Habib ◽  
D Mendis ◽  
J Chong ◽  
M Smith ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Computed Tomography ◽  
Neural Networks ◽  
Convolutional Neural Network ◽  
Convolutional Neural Networks ◽  
Sinus Surgery ◽  
Computed Tomography Scans ◽  
Ethmoidal Artery ◽  
Anterior Ethmoidal Artery

AbstractObjectiveDeep learning using convolutional neural networks represents a form of artificial intelligence where computers recognise patterns and make predictions based upon provided datasets. This study aimed to determine if a convolutional neural network could be trained to differentiate the location of the anterior ethmoidal artery as either adhered to the skull base or within a bone ‘mesentery’ on sinus computed tomography scans.MethodsCoronal sinus computed tomography scans were reviewed by two otolaryngology residents for anterior ethmoidal artery location and used as data for the Google Inception-V3 convolutional neural network base. The classification layer of Inception-V3 was retrained in Python (programming language software) using a transfer learning method to interpret the computed tomography images.ResultsA total of 675 images from 388 patients were used to train the convolutional neural network. A further 197 unique images were used to test the algorithm; this yielded a total accuracy of 82.7 per cent (95 per cent confidence interval = 77.7–87.8), kappa statistic of 0.62 and area under the curve of 0.86.ConclusionConvolutional neural networks demonstrate promise in identifying clinically important structures in functional endoscopic sinus surgery, such as anterior ethmoidal artery location on pre-operative sinus computed tomography.

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