Automatic Diagnosis of Bipolar Disorder Using Optical Coherence Tomography Data and Artificial Intelligence.

Autor:	Sánchez-Morla EM; Department of Psychiatry, Hospital 12 de Octubre Research Institute (i + 12), 28041 Madrid, Spain.; Faculty of Medicine, Complutense University of Madrid, 28040 Madrid, Spain.; CIBERSAM: Biomedical Research Networking Centre in Mental Health, 28029 Madrid, Spain., Fuentes JL; Department of Ophthalmology, Miguel Servet University Hospital, 50009 Zaragoza, Spain.; Miguel Servet Ophthalmology Innovation and Research Group (GIMSO), Aragon Institute for Health Research (IIS Aragon), University of Zaragoza, 50009 Zaragoza, Spain., Miguel-Jiménez JM; Biomedical Engineering Group, Department of Electronics, University of Alcalá, 28801 Alcalá de Henares, Spain., Boquete L; Biomedical Engineering Group, Department of Electronics, University of Alcalá, 28801 Alcalá de Henares, Spain., Ortiz M; Interdisciplinary Center for Security, Reliability and Trust (SnT), University of Luxembourg, 1855 Luxembourg, Luxembourg., Orduna E; Department of Ophthalmology, Miguel Servet University Hospital, 50009 Zaragoza, Spain.; Miguel Servet Ophthalmology Innovation and Research Group (GIMSO), Aragon Institute for Health Research (IIS Aragon), University of Zaragoza, 50009 Zaragoza, Spain., Satue M; Department of Ophthalmology, Miguel Servet University Hospital, 50009 Zaragoza, Spain.; Miguel Servet Ophthalmology Innovation and Research Group (GIMSO), Aragon Institute for Health Research (IIS Aragon), University of Zaragoza, 50009 Zaragoza, Spain., Garcia-Martin E; Department of Ophthalmology, Miguel Servet University Hospital, 50009 Zaragoza, Spain.; Miguel Servet Ophthalmology Innovation and Research Group (GIMSO), Aragon Institute for Health Research (IIS Aragon), University of Zaragoza, 50009 Zaragoza, Spain.
Jazyk:	angličtina
Zdroj:	Journal of personalized medicine [J Pers Med] 2021 Aug 18; Vol. 11 (8). Date of Electronic Publication: 2021 Aug 18.
DOI:	10.3390/jpm11080803
Abstrakt:	Background: The aim of this study is to explore an objective approach that aids the diagnosis of bipolar disorder (BD), based on optical coherence tomography (OCT) data which are analyzed using artificial intelligence. Methods: Structural analyses of nine layers of the retina were analyzed in 17 type I BD patients and 42 controls, according to the areas defined by the Early Treatment Diabetic Retinopathy Study (ETDRS) chart. The most discriminating variables made up the feature vector of several automatic classifiers: Gaussian Naive Bayes, K-nearest neighbors and support vector machines. Results: BD patients presented retinal thinning affecting most layers, compared to controls. The retinal thickness of the parafoveolar area showed a high capacity to discriminate BD subjects from healthy individuals, specifically for the ganglion cell (area under the curve (AUC) = 0.82) and internal plexiform (AUC = 0.83) layers. The best classifier showed an accuracy of 0.95 for classifying BD versus controls, using as variables of the feature vector the IPL (inner nasal region) and the INL (outer nasal and inner inferior regions) thickness. Conclusions: Our patients with BD present structural alterations in the retina, and artificial intelligence seem to be a useful tool in BD diagnosis, but larger studies are needed to confirm our findings.
Databáze:	MEDLINE
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