000151183 001__ 151183
000151183 005__ 20251017144612.0
000151183 0247_ $$2doi$$a10.3390/jpm11080803
000151183 0248_ $$2sideral$$a125868
000151183 037__ $$aART-2021-125868
000151183 041__ $$aeng
000151183 100__ $$aSánchez-Morla E.M.
000151183 245__ $$aAutomatic diagnosis of bipolar disorder using optical coherence tomography data and artificial intelligence
000151183 260__ $$c2021
000151183 5060_ $$aAccess copy available to the general public$$fUnrestricted
000151183 5203_ $$aBackground: 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.
000151183 536__ $$9info:eu-repo/grantAgreement/ES/ISCIII-FEDER/PI17-01726$$9info:eu-repo/grantAgreement/ES/ISCIII-FEDER/PI20-00437$$9info:eu-repo/grantAgreement/ES/MINECO FEDER/RD16-0008-029
000151183 540__ $$9info:eu-repo/semantics/openAccess$$aby$$uhttps://creativecommons.org/licenses/by/4.0/deed.es
000151183 590__ $$a3.508$$b2021
000151183 591__ $$aMEDICINE, GENERAL & INTERNAL$$b69 / 172 = 0.401$$c2021$$dQ2$$eT2
000151183 591__ $$aHEALTH CARE SCIENCES & SERVICES$$b42 / 110 = 0.382$$c2021$$dQ2$$eT2
000151183 592__ $$a0.757$$b2021
000151183 593__ $$aMedicine (miscellaneous)$$c2021$$dQ2
000151183 594__ $$a1.8$$b2021
000151183 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000151183 700__ $$aFuentes J.L.
000151183 700__ $$aMiguel-Jiménez J.M.
000151183 700__ $$aBoquete L.
000151183 700__ $$aOrtiz M.
000151183 700__ $$0(orcid)0000-0003-2710-1875$$aOrduna E.$$uUniversidad de Zaragoza
000151183 700__ $$0(orcid)0000-0002-1609-5994$$aSatue M.$$uUniversidad de Zaragoza
000151183 700__ $$0(orcid)0000-0001-6258-2489$$aGarcia-Martin E.$$uUniversidad de Zaragoza
000151183 7102_ $$11013$$2646$$aUniversidad de Zaragoza$$bDpto. Cirugía$$cÁrea Oftalmología
000151183 7102_ $$12002$$2647$$aUniversidad de Zaragoza$$bDpto. Física Aplicada$$cÁrea Óptica
000151183 773__ $$g11, 8 (2021), 803 [15 pp.]$$pJ. pers. med.$$tJournal of Personalized Medicine$$x2075-4426
000151183 8564_ $$s1000030$$uhttps://zaguan.unizar.es/record/151183/files/texto_completo.pdf$$yVersión publicada
000151183 8564_ $$s2690874$$uhttps://zaguan.unizar.es/record/151183/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000151183 909CO $$ooai:zaguan.unizar.es:151183$$particulos$$pdriver
000151183 951__ $$a2025-10-17-14:17:52
000151183 980__ $$aARTICLE