FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4384027742> ?p ?o ?g. }

• W4384027742 endingPage "100626" @default.
• W4384027742 startingPage "100626" @default.
• W4384027742 abstract "IgA nephropathy (IgAN) is the most common primary glomerular disease worldwide, with heterogeneous clinical and pathological manifestations, and is a common cause of end-stage renal disease. Early detection and effective intervention measures are essential for improving the outcome of IgAN. Machine learning methods can make the pathological analysis, early detection and diagnosis, and prognosis prediction of IgAN more automated and accurate. This article discusses the application of machine learning methods in IgAN, from optimizing pathological diagnosis to discovering non-invasive specific biomarkers, predicting disease progression, and evaluating prognosis. It is the key to reducing the incidence rate and mortality of end-stage renal disease by relying on intelligent image analysis of VGG16 for accurate detection and diagnosis of IgA nephropathy to enable clinical to take effective prevention and treatment measures. A total of 452 cases of kidney disease admitted to the nephrology department of our hospital from January 2018 to February 2023 were selected, and it was ruled out that pathological diagnosis could not be made due to the small number of samples submitted for renal puncture; After excluding suspected cases of renal biopsy pathology diagnosis and patients who did not undergo immunofluorescence examination, a total of 135 confirmed IgA nephropathy patients were subjected to image analysis. The internationally recognized 5-level semi-quantitative method was used for evaluation, and traditional image processing methods were selected to segment and extract fluorescence deposition areas. Transform the input image into color space and generate a binary image using the adaptive threshold method in the two feature dimensions of color and brightness. Then, VGG16 regions were separated and merged to obtain independent sedimentary regions. VGG16 was used to add BN layers and SE visual attention to fully extract sensitive features with high inter-class similarity and significant intra-class differences in the IgA nephropathy image classification task. The contour, area, and average brightness of each sedimentary region were calculated, and automatic computer recognition of fluorescence deposition intensity and shape was obtained to improve the accuracy of IgA nephropathy image classification. The artificial intelligence image analysis based on VGG16 can achieve the interpretation of IgA nephropathy immunofluorescence results with a higher coincidence rate compared to the results of pathological diagnostic doctors. IgA reaches 88.9%, IgG reaches 85.8%, IgM reaches 83.8%, and C3 reaches 88.6%. Therefore, it can assist pathological diagnostic doctors in interpreting IgA nephropathy immunofluorescence. By fully utilizing computer and network technologies to change the pathological workflow, improve the work efficiency of pathological diagnostic doctors, reduce the misdiagnosis rate caused by fatigue during film reading, and make pathological diagnosis more accurate and objective." @default.
• W4384027742 created "2023-07-13" @default.
• W4384027742 creator A5019667772 @default.
• W4384027742 creator A5034578500 @default.
• W4384027742 creator A5040558103 @default.
• W4384027742 creator A5048086615 @default.
• W4384027742 creator A5051158020 @default.
• W4384027742 creator A5090337654 @default.
• W4384027742 date "2023-09-01" @default.
• W4384027742 modified "2023-10-12" @default.
• W4384027742 title "VGG16-based intelligent image analysis in the pathological diagnosis of IgA nephropathy" @default.
• W4384027742 cites W1493956763 @default.
• W4384027742 cites W2047513740 @default.
• W4384027742 cites W2401520370 @default.
• W4384027742 cites W2770305510 @default.
• W4384027742 cites W2905005518 @default.
• W4384027742 cites W2937346600 @default.
• W4384027742 cites W2969528126 @default.
• W4384027742 cites W2976968312 @default.
• W4384027742 cites W2984438603 @default.
• W4384027742 cites W3087619578 @default.
• W4384027742 cites W3109319544 @default.
• W4384027742 cites W4220788253 @default.
• W4384027742 cites W4221018792 @default.
• W4384027742 cites W4223519428 @default.
• W4384027742 cites W4281250032 @default.
• W4384027742 cites W4310860656 @default.
• W4384027742 cites W4362665647 @default.
• W4384027742 doi "https://doi.org/10.1016/j.jrras.2023.100626" @default.
• W4384027742 hasPublicationYear "2023" @default.
• W4384027742 type Work @default.
• W4384027742 citedByCount "0" @default.
• W4384027742 crossrefType "journal-article" @default.
• W4384027742 hasAuthorship W4384027742A5019667772 @default.
• W4384027742 hasAuthorship W4384027742A5034578500 @default.
• W4384027742 hasAuthorship W4384027742A5040558103 @default.
• W4384027742 hasAuthorship W4384027742A5048086615 @default.
• W4384027742 hasAuthorship W4384027742A5051158020 @default.
• W4384027742 hasAuthorship W4384027742A5090337654 @default.
• W4384027742 hasBestOaLocation W43840277421 @default.
• W4384027742 hasConcept C126322002 @default.
• W4384027742 hasConcept C126838900 @default.
• W4384027742 hasConcept C134018914 @default.
• W4384027742 hasConcept C142724271 @default.
• W4384027742 hasConcept C146357865 @default.
• W4384027742 hasConcept C151730666 @default.
• W4384027742 hasConcept C207886595 @default.
• W4384027742 hasConcept C2778653478 @default.
• W4384027742 hasConcept C2779134260 @default.
• W4384027742 hasConcept C2781184683 @default.
• W4384027742 hasConcept C54847362 @default.
• W4384027742 hasConcept C555293320 @default.
• W4384027742 hasConcept C71924100 @default.
• W4384027742 hasConcept C86803240 @default.
• W4384027742 hasConceptScore W4384027742C126322002 @default.
• W4384027742 hasConceptScore W4384027742C126838900 @default.
• W4384027742 hasConceptScore W4384027742C134018914 @default.
• W4384027742 hasConceptScore W4384027742C142724271 @default.
• W4384027742 hasConceptScore W4384027742C146357865 @default.
• W4384027742 hasConceptScore W4384027742C151730666 @default.
• W4384027742 hasConceptScore W4384027742C207886595 @default.
• W4384027742 hasConceptScore W4384027742C2778653478 @default.
• W4384027742 hasConceptScore W4384027742C2779134260 @default.
• W4384027742 hasConceptScore W4384027742C2781184683 @default.
• W4384027742 hasConceptScore W4384027742C54847362 @default.
• W4384027742 hasConceptScore W4384027742C555293320 @default.
• W4384027742 hasConceptScore W4384027742C71924100 @default.
• W4384027742 hasConceptScore W4384027742C86803240 @default.
• W4384027742 hasIssue "3" @default.
• W4384027742 hasLocation W43840277421 @default.
• W4384027742 hasOpenAccess W4384027742 @default.
• W4384027742 hasPrimaryLocation W43840277421 @default.
• W4384027742 hasRelatedWork W23151206 @default.
• W4384027742 hasRelatedWork W2360463358 @default.
• W4384027742 hasRelatedWork W2361183529 @default.
• W4384027742 hasRelatedWork W2364348444 @default.
• W4384027742 hasRelatedWork W2382830329 @default.
• W4384027742 hasRelatedWork W2410006919 @default.
• W4384027742 hasRelatedWork W2744071441 @default.
• W4384027742 hasRelatedWork W3142512143 @default.
• W4384027742 hasRelatedWork W341674830 @default.
• W4384027742 hasRelatedWork W4387409017 @default.
• W4384027742 hasVolume "16" @default.
• W4384027742 isParatext "false" @default.
• W4384027742 isRetracted "false" @default.
• W4384027742 workType "article" @default.