VGG16-based intelligent image analysis in the pathological diagnosis of IgA nephropathy

Abstract

ObjectiveIgA 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. MethodsA 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. ResultsThe 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. ConclusionBy 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.