J. Ventre
7 Papers
J. Ventre is an academic researcher. The author has contributed to research in topics: Medicine & Radiography. The author has an hindex of 1, co-authored 1 publications.
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Papers
Automated detection of acute appendicular skeletal fractures in pediatric patients using deep learning
Daichi Hayashi,Andrew J. Kompel,J. Ventre,Alexis Ducarouge,Toan Nguyen,N. Regnard,Ali Guermazi +6 more
TL;DR: The BoneView™ deep learning algorithm provides high overall diagnostic performance for appendicular fracture detection in pediatric patients and consistently high across all anatomical locations and different types of fractures except for avulsion fractures.
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Using AI to Improve Radiologist Performance in Detection of Abnormalities on Chest Radiographs.
Souhail Bennani,N. Regnard,J. Ventre,Louis Lassalle,Toan Nguyen,Alexis Ducarouge,Lucas Dargent,Enora Guillo,Elodie Gouhier,Sophie-Hélène Zaimi,Emma Canniff,Cécile Malandrin,Philippe Khafagy,H. Koulakian,Marie-Pierre Revel,Guillaume Chassagnon +15 more
TL;DR: AI-assisted chest radiography interpretation resulted in absolute increases in sensitivity for all radiologists of various levels of expertise and reduced the reading times; specificity increased with AI, except in the diagnosis of pneumothorax.
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Assessment of performances of a deep learning algorithm for the detection of limbs and pelvic fractures, dislocations, focal bone lesions, and elbow effusions on trauma X-rays.
N. Regnard,Boubekeur Lanseur,J. Ventre,Alexis Ducarouge,Lauryane Clovis,Louis Lassalle,Elise Lacave,Albane Grandjean,A Lambert,Benjamin Dallaudière,Antoine Feydy +10 more
TL;DR: In this paper , the authors evaluated the performance of an AI trained to detect and localize skeletal lesions and compare them to the routine radiological interpretation by collecting all radiographic examinations with the associated radiologists' reports performed after a traumatic injury of the limbs and pelvis during 3 consecutive months (January to March 2017).
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High performance for bone age estimation with an artificial intelligence solution.
Toan Nguyen,Anne-Laure Hermann,J. Ventre,Alexis Ducarouge,Aloïs Pourchot,N. Regnard,Ali Guermazi +6 more
TL;DR: In this paper , the authors compared the performance of an artificial intelligence (AI) solution to that of a senior general radiologist for bone age assessment, and the results of the reader were then compared to those of the AI solution using mean absolute error (MAE).
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Learning from the machine: AI assistance is not an effective learning tool for resident education in chest x-ray interpretation
Guillaume Chassagnon,Nicolas Billet,Caroline Rutten,Thibault Toussaint,Quentin Cassius de Linval,Mégane Collin,Leila Lemouchi,M. Homps,Mohamed Hedjoudje,J. Ventre,Jules Gregory,Emma Canniff,N. Regnard,Souhail Bennani,Marie-Pierre Revel +14 more
TL;DR: Although the use of artificial intelligence improves radiology residents’ performance in chest X-rays interpretation, artificial intelligence cannot be used alone as a learning tool and should not replace dedicated teaching.
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