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| Naji, M. |
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| Motta, Antonella |
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| Aletan, Dirar |
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| Mohamed, Tarek |
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| Ertürk, Emre |
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| Taccardi, Nicola |
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| Kononenko, Denys |
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| Petrov, R. H. | Madrid |
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| Alshaaer, Mazen | Brussels |
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| Bih, L. |
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| Casati, R. |
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| Muller, Hermance |
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| Kočí, Jan | Prague |
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| Šuljagić, Marija |
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| Kalteremidou, Kalliopi-Artemi | Brussels |
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| Azam, Siraj |
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| Ospanova, Alyiya |
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| Blanpain, Bart |
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| Ali, M. A. |
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| Popa, V. |
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| Rančić, M. |
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| Ollier, Nadège |
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| Azevedo, Nuno Monteiro |
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| Landes, Michael |
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| Rignanese, Gian-Marco |
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Morris, Michael
Trinity College Dublin
in Cooperation with on an Cooperation-Score of 37%
Topics
Publications (9/9 displayed)
- 2023Detection of pneumothorax on ultrasound using artificial intelligencecitations
- 2023Synthesis, Characterisation, and Functionalisation of Charged Two‐Dimensional MoS2citations
- 2023Polyaniline wrapped graphene quantum dot decorated strontium titanate for robust high-performance flexible symmetric supercapacitorscitations
- 2019Optimizing Polymer Brush Coverage To Develop Highly Coherent Sub-5 nm Oxide Films by Ion Inclusioncitations
- 2019Surface characterization of poly-2-vinylpyridine-A polymer for area selective deposition techniquescitations
- 2014Rapid, Brushless Self-assembly of a PS-b-PDMS Block Copolymer for Nanolithographycitations
- 2013Directed self-assembly of PS-b-PMMA block copolymer using HSQ lines for translational alignment
- 2013Supercritical-fluid synthesis of FeF2 and CoF2 Li-ion conversion materials
- 2013Sub-15nm silicon lines fabrication via PS-b-PDMS block copolymer lithography
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article
Detection of pneumothorax on ultrasound using artificial intelligence
Abstract
<jats:sec><jats:title>BACKGROUND</jats:title><jats:p>Ultrasound (US) for the detection of pneumothorax shows excellent sensitivity in the hands of skilled providers. Artificial intelligence may facilitate the movement of US for pneumothorax into the prehospital setting. The large amount of training data required for conventional neural network methodologies has limited their use in US so far.</jats:p></jats:sec><jats:sec><jats:title>METHODS</jats:title><jats:p>A limited training database was supplied by Defense Advanced Research Projects Agency of 30 patients, 15 cases with pneumothorax and 15 cases without. There were two US videos per patient, of which we were allowed to choose one to train on, so that a limited set of 30 videos were used. Images were annotated for ribs and pleural interface. The software performed anatomic reconstruction to identify the region of interest bounding the pleura. Three neural networks were created to analyze images on a pixel-by-pixel fashion with direct voting determining the outcome. Independent verification and validation was performed on a data set gathered by the Department of Defense.</jats:p></jats:sec><jats:sec><jats:title>RESULTS</jats:title><jats:p>Anatomic reconstruction with the identification of ribs and pleura was able to be accomplished on all images. On independent verification and validation against the Department of Defense testing data, our program concurred with the SME 80% of the time and achieved a 86% sensitivity (18/21) for pneumothorax and a 75% specificity for the absence of pneumothorax (18/24). Some of the mistakes by our artificial intelligence can be explained by chest wall motion, hepatization of the underlying lung, or being equivocal cases.</jats:p></jats:sec><jats:sec><jats:title>CONCLUSION</jats:title><jats:p>Using learning with limited labeling techniques, pneumothorax was identified on US with an accuracy of 80%. Several potential improvements are controlling for chest wall motion and the use of longer videos.</jats:p></jats:sec><jats:sec><jats:title>LEVEL OF EVIDENCE</jats:title><jats:p>Diagnostic Tests; Level III.</jats:p></jats:sec>