Automated detection and segmentation of focal cortical dysplasias (FCDs) with artificial intelligence: Presentation of a novel convolutional neural network and its prospective clinical validation

•An artificial neural network for FCD detection is prospectively clinically validated.•Our algorithm detected FCDs with a higher sensitivity than neuroradiologists.•We consider our algorithm useful for FCD pre-screening in everyday clinical settings. Focal cortical dysplasias (FCDs) represent one of...

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Bibliographic Details
Published in:	Epilepsy research Vol. 172; p. 106594
Main Authors:	House, Patrick M., Kopelyan, Mikhail, Braniewska, Natalia, Silski, Bartosz, Chudzinska, Agata, Holst, Brigitte, Sauvigny, Thomas, Martens, Tobias, Stodieck, Stefan, Pelzl, Sirko
Format:	Journal Article
Language:	English
Published:	Netherlands Elsevier B.V 01-05-2021
Subjects:	Algorithm Artificial Intelligence Deep learning Epilepsies, Partial - diagnostic imaging Humans Magnetic Resonance Imaging Malformations of Cortical Development - diagnostic imaging MRI Neural Networks, Computer Prospective Studies Deep learning AI 3D MRI Algorithm
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Summary:	•An artificial neural network for FCD detection is prospectively clinically validated.•Our algorithm detected FCDs with a higher sensitivity than neuroradiologists.•We consider our algorithm useful for FCD pre-screening in everyday clinical settings. Focal cortical dysplasias (FCDs) represent one of the most frequent causes of pharmaco-resistant focal epilepsies. Despite improved clinical imaging methods over the past years, FCD detection remains challenging, as FCDs vary in location, size, and shape and commonly blend into surrounding tissues without clear definable boundaries. We developed a novel convolutional neural network for FCD detection and segmentation and validated it prospectively on daily-routine MRIs. The neural network was trained on 201 T1 and FLAIR 3 T MRI volume sequences of 158 patients with mainly FCDs, regardless of type, and 7 focal PMG. Non-FCD/PMG MRIs, drawn from 100 normal MRIs and 50 MRIs with non-FCD/PMG pathologies, were added to the training. We applied the algorithm prospectively on 100 consecutive MRIs of patients with focal epilepsy from daily clinical practice. The results were compared with corresponding neuroradiological reports and morphometric MRI analyses evaluated by an experienced epileptologist. Best training results reached a sensitivity (recall) of 70.1 % and a precision of 54.3 % for detecting FCDs. Applied on the daily-routine MRIs, 7 out of 9 FCDs were detected and segmented correctly with a sensitivity of 77.8 % and a specificity of 5.5 %. The results of conventional visual analyses were 33.3 % and 94.5 %, respectively (3/9 FCDs detected); the results of morphometric analyses with overall epileptologic evaluation were both 100 % (9/9 FCDs detected) and thus served as reference. We developed a 3D convolutional neural network with autoencoder regularization for FCD detection and segmentation. Our algorithm employs the largest FCD training dataset to date with various types of FCDs and some focal PMG. It provided a higher sensitivity in detecting FCDs than conventional visual analyses. Despite its low specificity, the number of false positively predicted lesions per MRI was lower than with morphometric analysis. We consider our algorithm already useful for FCD pre-screening in everyday clinical practice.
Bibliography:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23
ISSN:	0920-1211 1872-6844
DOI:	10.1016/j.eplepsyres.2021.106594