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. 2020 Dec;101(12):783-788.
doi: 10.1016/j.diii.2020.03.006. Epub 2020 Mar 31.

Three artificial intelligence data challenges based on CT and MRI

N Lassau  1 , I Bousaid  2 , E Chouzenoux  3 , J P Lamarque  4 , B Charmettant  4 , M Azoulay  2 , F Cotton  5 , A Khalil  6 , O Lucidarme  7 , F Pigneur  8 , Y Benaceur  9 , A Sadate  9 , M Lederlin  10 , F Laurent  11 , G Chassagnon  12 , O Ernst  13 , G Ferreti  14 , Y Diascorn  15 , P Y Brillet  16 , M Creze  17 , L Cassagnes  18 , C Caramella  19 , A Loubet  20 , A Dallongeville  21 , N Abassebay  22 , M Ohana  23 , N Banaste  24 , M Cadi  25 , J Behr  26 , L Boussel  27 , L Fournier  28 , M Zins  29 , J P Beregi  30 , A Luciani  8 , A Cotten  31 , J F Meder  32
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Free article

Three artificial intelligence data challenges based on CT and MRI

N Lassau et al. Diagn Interv Imaging. 2020 Dec.
Free article

Abstract

Purpose: The second edition of the artificial intelligence (AI) data challenge was organized by the French Society of Radiology with the aim to: (i), work on relevant public health issues; (ii), build large, multicentre, high quality databases; and (iii), include three-dimensional (3D) information and prognostic questions.

Materials and methods: Relevant clinical questions were proposed by French subspecialty colleges of radiology. Their feasibility was assessed by experts in the field of AI. A dedicated platform was set up for inclusion centers to safely upload their anonymized examinations in compliance with general data protection regulation. The quality of the database was checked by experts weekly with annotations performed by radiologists. Multidisciplinary teams competed between September 11th and October 13th 2019.

Results: Three questions were selected using different imaging and evaluation modalities, including: pulmonary nodule detection and classification from 3D computed tomography (CT), prediction of expanded disability status scale in multiple sclerosis using 3D magnetic resonance imaging (MRI) and segmentation of muscular surface for sarcopenia estimation from two-dimensional CT. A total of 4347 examinations were gathered of which only 6% were excluded. Three independent databases from 24 individual centers were created. A total of 143 participants were split into 20 multidisciplinary teams.

Conclusion: Three data challenges with over 1200 general data protection regulation compliant CT or MRI examinations each were organized. Future challenges should be made with more complex situations combining histopathological or genetic information to resemble real life situations faced by radiologists in routine practice.

Keywords: Artificial intelligence (AI); Computed tomography (CT); Deep learning; Machine learning; Magnetic resonance imaging (MRI).

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