Three artificial intelligence data challenges based on CT and ultrasound
Diagn Interv Imaging . 2021 Jul 23;S2211-5684(21)00167-4. doi: 10.1016/j.diii.2021.06.005. Online ahead of print.
Nathalie Lassau, Imad Bousaid, Emilie Chouzenoux, Antoine Verdon, Corinne Balleyguier, François Bidault, Elie Mousseaux, Sana Harguem-Zayani, Loic Gaillandre, Zoubir Bensalah, Isabelle Doutriaux-Dumoulin, Michèle Monroc, Audrey Haquin, Luc Ceugnart, Florence Bachelle, Mathilde Charlot, Isabelle Thomassin-Naggara, Tiphaine Fourquet, Héloise Dapvril, Joseph Orabona, Foucauld Chamming's, Mickael El Haik, Jules Zhang-Yin, Marc-Samir Guillot, Mickaël Ohana, Thomas Caramella, Yann Diascorn, Jean-Yves Airaud, Philippe Cuingnet, Umit Gencer, Littisha Lawrance, Alain Luciani, Anne Cotten, Jean-François Meder
Purpose: The 2020 edition of these Data Challenges was organized by the French Society of Radiology (SFR), from September 28 to September 30, 2020. The goals were to propose innovative artificial intelligence solutions for the current relevant problems in radiology and to build a large database of multimodal medical images of ultrasound and computed tomography (CT) on these subjects from several French radiology centers.
Materials and methods: This year the attempt was to create data challenge objectives in line with the clinical routine of radiologists, with less preprocessing of data and annotation, leaving a large part of the preprocessing task to the participating teams. The objectives were proposed by the different organizations depending on their core areas of expertise. A dedicated platform was used to upload the medical image data, to automatically anonymize the uploaded data.
Results: Three challenges were proposed including classification of benign or malignant breast nodules on ultrasound examinations, detection and contouring of pathological neck lymph nodes from cervical CT examinations and classification of calcium score on coronary calcifications from thoracic CT examinations. A total of 2076 medical examinations were included in the database for the three challenges, in three months, by 18 different centers, of which 12% were excluded. The 39 participants were divided into six multidisciplinary teams among which the coronary calcification score challenge was solved with a concordance index > 95%, and the other two with scores of 67% (breast nodule classification) and 63% (neck lymph node calcifications).
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