Scientific Validation is in our DNA.
AI must be tried and tested in real-world conditions, and our work with eminent research groups, radiology departments, and clinical experts ensures that our solutions perform accurately and robustly even under strenuous operating conditions like multiple OEMs, field strength, resolution, quality, and imaging artefacts.
Up to 28% Enhanced Detection Rate : Improving the sensitivity of T2-FLAIR lesion detection & evolution
Manon Rival, Sabine Laurent-Chaballier, Thibault Mura, Giovanni Castelnovo, Eric Thouvenot,
Evaluation de l’apport d’un outil de segmentation automatique pour le suivi longitudinal des lésions de sclérose en plaques en irm cérébrale,
Journal of Neuroradiology,
Volume 50, Issue 2,
2023,
Page 201,
ISSN 0150-9861
Milica Mastilovic et al 2024 [under review]
H. Dehaene et al. European Society of Neuroradiology 2023 [Poster]
H. Dehaene et al 2023 [White Paper]
Up to 65% reduction in reading time : supporting radiology workflows
Manon Rival, Sabine Laurent-Chaballier, Thibault Mura, Giovanni Castelnovo, Eric Thouvenot,
Evaluation de l’apport d’un outil de segmentation automatique pour le suivi longitudinal des lésions de sclérose en plaques en irm cérébrale,
Journal of Neuroradiology,
Volume 50, Issue 2,
2023,
Page 201,
ISSN 0150-9861
Milica Mastilovic et al [under review]
Predictive AI
Lassau N, Bousaid I, Chouzenoux E, Lamarque JP, Charmettant B, Azoulay M, Cotton F, Khalil A, Lucidarme O, Pigneur F, Benaceur Y, Sadate A, Lederlin M, Laurent F, Chassagnon G, Ernst O, Ferreti G, Diascorn Y, Brillet PY, Creze M, Cassagnes L, Caramella C, Loubet A, Dallongeville A, Abassebay N, Ohana M, Banaste N, Cadi M, Behr J, Boussel L, Fournier L, Zins M, Beregi JP, Luciani A, Cotten A, Meder JF. Three artificial intelligence data challenges based on CT and MRI. Diagn Interv Imaging. 2020 Dec;101(12):783-788. doi: 10.1016/j.diii.2020.03.006. Epub 2020 Mar 31. PMID: 32245723.
Pauline Roca, Arnaud Attyé, Lucie Colas, Alan Tucholka, Pascal Rubini, Stenzel Cackowski, Juliette Ding, Jean-François Budzik, Felix Renard, Senan Doyle, Emmanuel L. Barbier, Imad Bousaid, Romain Casey, Sandra Vukusic, Nathalie Lassau, Sébastien Verclytte, and François Cotton. “Artificial intelligence to predict clinical disability status scale score in patients with multiple sclerosis using FLAIR MRI” Diagnostic and Interventional Imaging 2020.
Arnaud Attyé, Stenzel Cackowski, Alan Tucholka, Pauline Roca, Pascal Rubini, Sebastien Verclytte, Lucie Colas, Juliette Ding, Jean-François Budzik, Felix Renard, Emmanuel L Barbier, Romain Casey, Sandra Vukusic, and François Cotton. “Breaking the Clinico-Radiological Paradox in Multiple Sclerosis Using Machine Learning.” In Proceedings of the 28th Annual Meeting of International Society of Magnetic Resonance in Medicine (ISMRM), Sydney, Australia, 2020.
Trustworthy AI
B. Lambert et al. Conformal Volume Estimation under Covariate Shift. MICCAI 2024 [to appear]
Benjamin Lambert, Florence Forbes, Senan Doyle, Harmonie Dehaene, Michel Dojat,
Trustworthy clinical AI solutions: A unified review of uncertainty quantification in Deep Learning models for medical image analysis,
Artificial Intelligence in Medicine,
Volume 150,
2024,
102830,
ISSN 0933-3657
AI Performance
Benjamin Lambert and Maxime Louis and Senan Doyle and Florence Forbes and Michel Dojat and Alan Tucholka. “Leveraging 3D Information in Unsupervised Brain MRI Segmentation.” 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI) (pp. 187-190). 2021
Lison Malaureille. Comparison of automatic segmentation versus visual assessment of white matter hyperintensities and parenchymal atrophy in Alzheimer’s disease. Human health and pathology. 2018. ⟨dumas-01887452⟩ [Thesis]
Commowick, Olivier, et al. “Objective evaluation of multiple sclerosis lesion segmentation using a data management and processing infrastructure.” Nature Scientific reports 8.1 (2018): 13650.
Thomas Mistral, Pauline Roca, Christophe Maggia, Alan Tucholka, Florence Forbes, Senan Doyle, Alexandre Krainik, Damien Galanaud, Emmanuelle Schmitt, Stéphane Kremer, Adrian Kastler, Irène Troprès, Emmanuel L. Barbier, Jean-François Payen, and Michel Dojat. “Automated quantification of brain lesion volume from post-trauma MR diffusion-weighted Images.” Frontiers in Neurology, 2022.
Méreaux, Jean-Loup, Benjamin Hebant, Romain Lefaucheur, David Maltête, Nicolas Magne, Emmanuel Gérardin, and Bertrand Bourre. 2020. “Comparaison d’une analyse automatisée d’IRM par Pixyl versus imagerie de fusion dans le suivi de la sclérose en plaques.” In Journées des Jeunes Neurologues (J2N) et de la Recherche clinique, 2020.
Supporting clinical studies
Pelle, J., Briant, A.R., Branger, P. et al. Real-World Effectiveness of Natalizumab Extended Interval Dosing in a French Cohort. Neurol Ther 12, 529–542 (2023). https://doi.org/10.1007/s40120-023-00440-5
Bonnan M, Money P, Desblache P, Marasescu R, Puvilland LM, Demasles S, Dahan C, Krim E, Tucholka A, Doyle S, Barroso B. Focal cortical atrophy following transient meningeal enhancement in a progressive multiple sclerosis. Neurol Sci. 2021 May;42(5):1959-1961.
Archive
Benjamin Lambert, Florence Forbes, Senan Doyle, Alan Tucholka and Michel Dojat. “Fast Uncertainty Quantification for Deep Learning-based MR Brain Segmentation.” Explain’AI workshop, In Extraction et Gestion des Connaissances (EGC) 2022 Conference
Bonnan, M., Money, P., Desblache, P., Tucholka, A., Doyle, S., & Barroso, B. (2019). Atrophie corticale focale après une poussée méningée dans une sclérose en plaques progressive. Revue Neurologique, 175, S91.
Capet, N., Joly, H., Suply, C., Bresch, S., Mondot, L., Cohen, M. and Lebrun-Frénay, C., 2019. L’alexithymie dans la Sclérose en Plaques est associée à une atrophie de la substance blanche et des structures de substance grise centrales. Revue Neurologique, 175, p.S89.
Alexander, Robert W. “Use of PIXYL software analysis of brain MRI (with & without contrast) as valuable metric in clinical trial tracking in study of multiple sclerosis (MS) and related neurodegenerative processes.” Clinical Trials in Degenerative Diseases 2.1 (2017): 1.
Alexander Robert W. “Use of Software Analytics of Brain MRI (with & without contrast) As Objective Metric in Neurological Disorders and Degenerative Diseases” 2017, International Physical Medicine & Rehabilitation Journal
Doyle, Senan, et al. “Sub-acute and Chronic Ischemic Stroke Lesion MRI Segmentation.” International MICCAI Brainlesion Workshop. Springer, Cham, 2017.
Maggia, Christophe, et al. “Traumatic Brain Lesion Quantification based on Mean Diffusivity Changes.” International MICCAI Brainlesion Workshop. Springer, Cham, 2017.
Maggia, Christophe, et al. “Assessment of tissue injury in severe brain trauma.” BrainLes 2015. Springer, Cham, 2015.
Maggia, Christophe, et al. “Traumatic Brain Lesion Quantification based on Mean Diffusivity Changes.” International MICCAI Brainlesion Workshop. Springer, Cham, 2017.
Thomas Mistral et al. “Automatic Quantification of Brain Lesion Volume from MR Images after Severe Traumatic Brain Injury” for radiology
Menze, Bjoern H., et al. “The multimodal brain tumor image segmentation benchmark (BRATS).” IEEE transactions on medical imaging 34.10 (2014): 1993-2024.
Doyle, S., et al. “Fully automatic brain tumor segmentation from multiple MR sequences using hidden Markov fields and variational EM.” Procs. NCI-MICCAI BraTS (2013): 18-22.
