Geometric deep learning on brain shape predicts sex and age

AbstractThe complex relationship between the shape and function of the human brain remains elusive despite extensive studies of cortical folding over many decades. The analysis of cortical gyrification presents an opportunity to advance our knowledge about this relationship, and better understand the etiology of a variety of pathologies involving diverse degrees of cortical folding abnormalities. Surface-based approaches have been shown to be particularly efficient in their ability to accurately describe the folded sheet topology of the cortical ribbon. However, the utility of these approaches has been blunted by their reliance on manually defined features in order to capture all relevant geometric properties of cortical folding. In this paper, we propose a deep-learning based method to analyze cortical folding patterns in a data-driven way that alleviates this reliance on manual feature definition. This method builds on the emerging field of geometric deep-learning and uses convolutional neural network architecture adapted to the surface representation of the cortical ribbon. MRI data from 6,410 healthy subjects obtained from 11 publicly available data repositories were used to predict age and sex via brain shape analysis. Ages ranged from 6-89 years. Both inner and outer cortical surfaces were extracted using Freesurfer and then registered into MNI space. Two gCNNs were trained, the first of which to predict subject’s self-identified sex, the second of which to predict subject’s age. Class Activation Maps (CAM) and Regression Activation Maps (RAM) were constructed to map the topographic distribution of the most influential brain regions involved in the decision process for each gCNN. Using this approach, the gCNN was able to predict a subject’s sex with an average accuracy of 87.99% and achieved a Person’s coefficient of correlation of 0.93 with an average absolute error 4.58 years when predicting a subject’s age.

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Deep Learning

10.1017/cbo9780511780295 ◽

2009 ◽

Cited By ~ 94

Author(s):

Stellan Ohlsson

Keyword(s):

Deep Learning

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Intelligence artificielle : le futur de l’Orthodontie ?

Revue d Orthopédie Dento-Faciale ◽

10.1051/odf/2019026 ◽

2019 ◽

Vol 53 (3) ◽

pp. 281-294

Author(s):

Jean-Michel Foucart ◽

Augustin Chavanne ◽

Jérôme Bourriau

Keyword(s):

Big Data ◽

Deep Learning ◽

Intelligence Artificielle ◽

Set Up

Nombreux sont les apports envisagés de l’Intelligence Artificielle (IA) en médecine. En orthodontie, plusieurs solutions automatisées sont disponibles depuis quelques années en imagerie par rayons X (analyse céphalométrique automatisée, analyse automatisée des voies aériennes) ou depuis quelques mois (analyse automatique des modèles numériques, set-up automatisé; CS Model +, Carestream Dental™). L’objectif de cette étude, en deux parties, est d’évaluer la fiabilité de l’analyse automatisée des modèles tant au niveau de leur numérisation que de leur segmentation. La comparaison des résultats d’analyse des modèles obtenus automatiquement et par l’intermédiaire de plusieurs orthodontistes démontre la fiabilité de l’analyse automatique; l’erreur de mesure oscillant, in fine, entre 0,08 et 1,04 mm, ce qui est non significatif et comparable avec les erreurs de mesures inter-observateurs rapportées dans la littérature. Ces résultats ouvrent ainsi de nouvelles perspectives quand à l’apport de l’IA en Orthodontie qui, basée sur le deep learning et le big data, devrait permettre, à moyen terme, d’évoluer vers une orthodontie plus préventive et plus prédictive.

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