A lightweight solution of industrial computed tomography with convolutional neural network

Background: Coronavirus disease 2019 (COVID-19) pneumonia and immune checkpoint inhibitor (ICI) therapy-related pneumonitis share common features. The aim of this study was to determine on chest computed tomography (CT) images whether a deep convolutional neural network algorithm is able to solve the challenge of differential diagnosis between COVID-19 pneumonia and ICI therapy-related pneumonitis. Methods: We enrolled three groups: a pneumonia-free group (n = 30), a COVID-19 group (n = 34), and a group of patients with ICI therapy-related pneumonitis (n = 21). Computed tomography images were analyzed with an artificial intelligence (AI) algorithm based on a deep convolutional neural network structure. Statistical analysis included the Mann–Whitney U test (significance threshold at p < 0.05) and the receiver operating characteristic curve (ROC curve). Results: The algorithm showed low specificity in distinguishing COVID-19 from ICI therapy-related pneumonitis (sensitivity 97.1%, specificity 14.3%, area under the curve (AUC) = 0.62). ICI therapy-related pneumonitis was identified by the AI when compared to pneumonia-free controls (sensitivity = 85.7%, specificity 100%, AUC = 0.97). Conclusions: The deep learning algorithm is not able to distinguish between COVID-19 pneumonia and ICI therapy-related pneumonitis. Awareness must be increased among clinicians about imaging similarities between COVID-19 and ICI therapy-related pneumonitis. ICI therapy-related pneumonitis can be applied as a challenge population for cross-validation to test the robustness of AI models used to analyze interstitial pneumonias of variable etiology.

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Automatic classification of pulmonary peri-fissural nodules in computed tomography using an ensemble of 2D views and a convolutional neural network out-of-the-box

Medical Image Analysis ◽

10.1016/j.media.2015.08.001 ◽

2015 ◽

Vol 26 (1) ◽

pp. 195-202 ◽

Cited By ~ 137

Author(s):

Francesco Ciompi ◽

Bartjan de Hoop ◽

Sarah J. van Riel ◽

Kaman Chung ◽

Ernst Th. Scholten ◽

...

Keyword(s):

Neural Network ◽

Computed Tomography ◽

Convolutional Neural Network ◽

Automatic Classification

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Human Organ Classifications from Computed Tomography Images Using Deep-Convolutional Neural Network

6th International Conference on the Development of Biomedical Engineering in Vietnam (BME6) - IFMBE Proceedings ◽

10.1007/978-981-10-4361-1_155 ◽

2017 ◽

pp. 917-923

Author(s):

Ho Thi Kieu Khanh ◽

Tran Cong Hung ◽

Viet-Hung Dang ◽

Nguyen Duc Thang

Keyword(s):

Neural Network ◽

Computed Tomography ◽

Convolutional Neural Network ◽

Deep Convolutional Neural Network ◽

Human Organ ◽

Computed Tomography Images

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

The Journal of Laryngology & Otology ◽

10.1017/s0022215120000444 ◽

2020 ◽

Vol 134 (4) ◽

pp. 328-331 ◽

Cited By ~ 1

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.

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

The Journal of Laryngology & Otology ◽

10.1017/s0022215119002536 ◽

2019 ◽

Vol 134 (1) ◽

pp. 52-55 ◽

Cited By ~ 3

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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