Matches in SemOpenAlex for { <https://semopenalex.org/work/W3133244199> ?p ?o ?g. }
Showing items 1 to 72 of
72
with 100 items per page.
- W3133244199 abstract "Abstract Background Artificial intelligence is developing rapidly, bringing increasing numbers of intelligent products into daily life. However, it has little progress in dry eye, which is a common disease and associated with meibomian gland dysfunction(MGD). Non-invasive infrared meibography, known as an effective diagnostic tool of MGD, allows for objective observation of meibomian glands. Thus, we discuss a deep learning method to measure and assess meibomian glands of meibography. Methods We used Mask R-CNN deep learning(DL) framework. A total of 1878 meibography images were collected and manually annotated by two licensed eyelid specialists with two classes: conjunctiva and meibomian glands. The annotated pictures were used to establish a DL model. An independent test dataset contained 58 images was used to compare the accuracy and efficiency of the deep learning model with specialists. Results The DL model calculated the ratio of meibomian gland loss with precise values by achieving high accuracy in the identification of conjunctiva (validation loss < 0.35, mAP > 0.976) and meibomian glands (validation loss < 1.0, mAP > 0.92). The comparison between specialists’ annotation and the DL model evaluation showed that there is little difference between the gold standard and the model. Each image takes 480ms for the model to evaluate, almost 21 times faster than specialists. Conclusions The DL model can improve the accuracy of meibography image evaluation, help specialists to grade the meibomian glands and save their time to some extent." @default.
- W3133244199 created "2021-03-01" @default.
- W3133244199 creator A5000133634 @default.
- W3133244199 creator A5015429222 @default.
- W3133244199 creator A5018758754 @default.
- W3133244199 creator A5028718810 @default.
- W3133244199 creator A5035465674 @default.
- W3133244199 creator A5053589298 @default.
- W3133244199 creator A5058920024 @default.
- W3133244199 date "2021-02-23" @default.
- W3133244199 modified "2023-09-23" @default.
- W3133244199 title "Automatic identification of meibomian gland dysfunction with meibography images using deep learning" @default.
- W3133244199 doi "https://doi.org/10.21203/rs.3.rs-181617/v1" @default.
- W3133244199 hasPublicationYear "2021" @default.
- W3133244199 type Work @default.
- W3133244199 sameAs 3133244199 @default.
- W3133244199 citedByCount "1" @default.
- W3133244199 countsByYear W31332441992022 @default.
- W3133244199 crossrefType "posted-content" @default.
- W3133244199 hasAuthorship W3133244199A5000133634 @default.
- W3133244199 hasAuthorship W3133244199A5015429222 @default.
- W3133244199 hasAuthorship W3133244199A5018758754 @default.
- W3133244199 hasAuthorship W3133244199A5028718810 @default.
- W3133244199 hasAuthorship W3133244199A5035465674 @default.
- W3133244199 hasAuthorship W3133244199A5053589298 @default.
- W3133244199 hasAuthorship W3133244199A5058920024 @default.
- W3133244199 hasBestOaLocation W31332441991 @default.
- W3133244199 hasConcept C108583219 @default.
- W3133244199 hasConcept C116834253 @default.
- W3133244199 hasConcept C118487528 @default.
- W3133244199 hasConcept C119857082 @default.
- W3133244199 hasConcept C126838900 @default.
- W3133244199 hasConcept C153180895 @default.
- W3133244199 hasConcept C154945302 @default.
- W3133244199 hasConcept C2777165675 @default.
- W3133244199 hasConcept C2781302119 @default.
- W3133244199 hasConcept C40993552 @default.
- W3133244199 hasConcept C41008148 @default.
- W3133244199 hasConcept C59822182 @default.
- W3133244199 hasConcept C71924100 @default.
- W3133244199 hasConcept C86803240 @default.
- W3133244199 hasConceptScore W3133244199C108583219 @default.
- W3133244199 hasConceptScore W3133244199C116834253 @default.
- W3133244199 hasConceptScore W3133244199C118487528 @default.
- W3133244199 hasConceptScore W3133244199C119857082 @default.
- W3133244199 hasConceptScore W3133244199C126838900 @default.
- W3133244199 hasConceptScore W3133244199C153180895 @default.
- W3133244199 hasConceptScore W3133244199C154945302 @default.
- W3133244199 hasConceptScore W3133244199C2777165675 @default.
- W3133244199 hasConceptScore W3133244199C2781302119 @default.
- W3133244199 hasConceptScore W3133244199C40993552 @default.
- W3133244199 hasConceptScore W3133244199C41008148 @default.
- W3133244199 hasConceptScore W3133244199C59822182 @default.
- W3133244199 hasConceptScore W3133244199C71924100 @default.
- W3133244199 hasConceptScore W3133244199C86803240 @default.
- W3133244199 hasLocation W31332441991 @default.
- W3133244199 hasOpenAccess W3133244199 @default.
- W3133244199 hasPrimaryLocation W31332441991 @default.
- W3133244199 hasRelatedWork W2773120646 @default.
- W3133244199 hasRelatedWork W3014300295 @default.
- W3133244199 hasRelatedWork W3164822677 @default.
- W3133244199 hasRelatedWork W4223943233 @default.
- W3133244199 hasRelatedWork W4225161397 @default.
- W3133244199 hasRelatedWork W4250304930 @default.
- W3133244199 hasRelatedWork W4309045103 @default.
- W3133244199 hasRelatedWork W4312200629 @default.
- W3133244199 hasRelatedWork W4360585206 @default.
- W3133244199 hasRelatedWork W4364306694 @default.
- W3133244199 isParatext "false" @default.
- W3133244199 isRetracted "false" @default.
- W3133244199 magId "3133244199" @default.
- W3133244199 workType "article" @default.