Jamie A O'Reilly, Hassapong Sunthornwiriya-Amon, Naradith Aparprasith, Pannapa Kittichalao, Pornnaphas Chairojwong, Thanabodee Klai-On, Edward W Lannon
Blind source separation of event-related potentials using a recurrent neural network
bioRxiv : the preprint server for biology
study aimed employ incremental digital image correlation dic method obtain displacement strain field data cornea corvis st cvs sequences access performance embedding biomechanical data machine learning models distinguish forme fruste keratoconus ffkc normal corneas 100 subjects categorized normal n 50 ffkc n 50 groups image sequences depicting horizontal crosssection human cornea air puff captured using corvis st tonometer highspeed evolution fullfield corneal displacement strain velocity strain rate reconstructed utilizing incremental dic approach maximum max average ave values fullfield displacement v shear strain xy velocity vr shear strain rate xyr determined time generating eight evolution curves denoting maxv maxxy maxvr maxxyr avev avexy avevr avexyr respectively evolution data inputted two machine learning ml models specifically nave bayes nb random forest rf models subsequently employed construct voting classifier performance models diagnosing ffkc normal corneas compared existing cvs parameters normal group ffkc group included 50 eyes ffkc group differ healthy controls age p 026 gender p 036 baseline significantly lower biop p 0001 thinner central cornea thickness cct p 0001 results demonstrated proposed voting ensemble model yielded highest performance auc 100 followed rf model auc 099 radius a2 time emerged bestperforming cvs parameters auc values 0948 0938 respectively nonetheless existing corvis st parameters outperformed ml models progressive enhancement performance ml models observed incremental time points corneal deformation study represents first instance displacement strain data following incremental dic analysis corvis st images integrated machine learning models effectively differentiate ffkc corneas normal ones achieving superior accuracy compared existing cvs parameters considering biomechanical responses inner cornea temporal pattern changes may significantly improve early detection keratoconus