Diffuse reflectance spectroscopy in dental caries detection and classification

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11110%2F20%3A10411843" target="_blank" >RIV/00216208:11110/20:10411843 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/68407700:21730/20:00347294 RIV/00179906:_____/20:10411843 RIV/00216208:11150/20:10411843 RIV/60461373:22340/20:43920945

Výsledek na webu

<a href="https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=MxP6h0glZQ" target="_blank" >https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=MxP6h0glZQ</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/s11760-020-01640-4" target="_blank" >10.1007/s11760-020-01640-4</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Diffuse reflectance spectroscopy in dental caries detection and classification

Popis výsledku v původním jazyce

Machine learning and augmented reality form very important computational tools in biomedicine, neurology and stomatology as well. The present paper is devoted to a novel method of spectroscopic detection of caries lesions that changes the optical properties of the affected tissue. This method of the diffuse reflectance spectroscopy is used in many biomedical areas even though the analysis of associated data suffers from a large variance of acquired signals&apos; shape and their properties. The proposed methodology of measured spectra analysis is based upon general methods of signal feature evaluation and the use of computational intelligence for their classification. The paper compares properties of dental feature clusters for the set of 578 tissues with different levels of their changes. Classification results of selected features by the support vector machine, Bayesian method, k-nearest neighbour method and neural network enable to distinguish the healthy tissue and caries lesions with the accuracy from 94.1 to 98.4% and the cross-validation error lower than 8.3%. These results suggest how the augmented reality and general mathematical signal processing methods can be beneficial for diagnostic purposes in dental research and possibly in the clinical practice as well.

Název v anglickém jazyce

Diffuse reflectance spectroscopy in dental caries detection and classification

Popis výsledku anglicky

Machine learning and augmented reality form very important computational tools in biomedicine, neurology and stomatology as well. The present paper is devoted to a novel method of spectroscopic detection of caries lesions that changes the optical properties of the affected tissue. This method of the diffuse reflectance spectroscopy is used in many biomedical areas even though the analysis of associated data suffers from a large variance of acquired signals&apos; shape and their properties. The proposed methodology of measured spectra analysis is based upon general methods of signal feature evaluation and the use of computational intelligence for their classification. The paper compares properties of dental feature clusters for the set of 578 tissues with different levels of their changes. Classification results of selected features by the support vector machine, Bayesian method, k-nearest neighbour method and neural network enable to distinguish the healthy tissue and caries lesions with the accuracy from 94.1 to 98.4% and the cross-validation error lower than 8.3%. These results suggest how the augmented reality and general mathematical signal processing methods can be beneficial for diagnostic purposes in dental research and possibly in the clinical practice as well.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

30208 - Dentistry, oral surgery and medicine

Projekt

<a href="/cs/project/EF17_048%2F0007441" target="_blank" >EF17_048/0007441: PERSONMED - Centrum rozvoje personalizované medicíny u věkem podmíněných onemocnění</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2020

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Signal, Image and Video Processing

ISSN

1863-1703

e-ISSN

—

Svazek periodika

14

Číslo periodika v rámci svazku

5

Stát vydavatele periodika

GB - Spojené království Velké Británie a Severního Irska

Počet stran výsledku

8

Strana od-do

1063-1070

Kód UT WoS článku

000510294800001

EID výsledku v databázi Scopus

2-s2.0-85078830904