Verbal classification in determinig the intraocular pressure altitudes concerning the variation of central corneal thickness

Sliesoraitytė, Ieva; Janulevičienė, Ingrida; Lukoševičius, Arūnas; Sliesoraitienė, Viktorija

Use this url to cite publication: https://hdl.handle.net/20.500.14911/120658

Verbal classification in determinig the intraocular pressure altitudes concerning the variation of central corneal thickness

Details

Publication Type (CRIS)

Straipsnis recenzuojamoje Lietuvos tarptautinės konferencijos medžiagoje / Article in peer-reviewed Lithuanian international conference proceedings (P1e)

Publication Type (eLABa)

Straipsnis recenzuotame konferencijos darbų leidinyje / Article published in peer-reviewed conference proceedings (P1d)

Author(s)

Author	Affiliation
Sliesoraitytė, Ieva	Kauno technologijos universitetas	Kauno medicinos universiteto klinikos
Janulevičienė, Ingrida	Kauno medicinos universiteto klinikos
Lukoševičius, Arūnas	Kauno technologijos universitetas
Sliesoraitienė, Viktorija	Biomechanikos inžinerijos katedra / Department of Biomechanical Engineering (0451)

Title

Verbal classification in determinig the intraocular pressure altitudes concerning the variation of central corneal thickness

[en]

Ieva Sliesoraitytė, Ingrida Janulevičienė, Arūnas Lukoševičius, Viktorija Sliesoraitienė

Title in other language

Akispūdžio nustatymo verbalinis klasifikavimas atsižvelgiant į centrinės ragenos dalies storį

[lt]

Is part of

Biomedicininė inžinerija = Biomedical engineering : tarptautinės konferencijos pranešimų medžiaga / Kauno technologijos universitetas

Published In

Year	Start Page	End Page
2006	167	170

Publisher

Kaunas : Technologija, 2006

Extent

p. 167-170

URI

URI
https://hdl.handle.net/20.500.14911/120658

Cited references

Science / Art Area

Technologijos mokslai / Technological sciences (T)

Field of Science / Art

Elektros ir elektronikos inžinerija / Electrical and electronic engineering (T001)

Abstract (en)

Precise intraocular pressure (IOP) measurement is the problem of the main importance in daily ophthalmologist practice. Identicifacion of disturbing factors via artificial neural networks is the apropos alternative aiming to reduce measured IOP errors. The particular study base upon the construction of verbal algorithm with a purpose to minimize the measured IOP errors concerning Central corneal thickness (CCT) altitudes. To assess the relative risk for IOP error estimation artificial neural networks should be applied; i.e. for classification purpose to identify IOP error via GAT for particular subject.