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    Area of Science:

    • Nephrology
    • Pathology
    • Artificial Intelligence in Medicine

    Background:

    • Kidney biopsy diagnosis of Lupus Nephritis (LN) suffers from low inter-observer agreement, leading to potential misdiagnosis and adverse patient outcomes.
    • Existing Computer Aided Diagnosis (CAD) systems have limitations in accurately classifying Lupus Glomerulonephritis (LGN) kidney scores.
    • Physician perception of CAD system strengths and weaknesses negatively impacts patient outcomes.

    Purpose of the Study:

    • To develop and evaluate an Uncertainty-Guided Bayesian Classification (UGBC) scheme for accurate LGN classification.
    • To address the challenge of low inter-observer agreement in kidney biopsy diagnoses for LN.
    • To improve upon existing CAD systems for nephrohistopathological applications.

    Main Methods:

    • Implemented an augmented Uncertainty-Guided Bayesian Classification (UGBC) scheme.
    • Utilized a high throughput, bulk labeling strategy for data annotation, leveraging Deep Neural Network's (DNN) noise resistance.
    • Applied the UGBC scheme to both glomerular-level (26,634 images) and kidney-level (87 mouse kidney sections) classification tasks.

    Main Results:

    • Achieved 94.5% weighted accuracy for glomerular-level LGN classification.
    • Attained 96.6% weighted accuracy for kidney-level LGN classification.
    • Demonstrated significant improvement over standard Convolutional Neural Network (CNN) architecture, with an 11.8% increase in glomerular-level and 3.5% increase in kidney-level accuracy.

    Conclusions:

    • The proposed UGBC scheme offers a robust and accurate method for classifying Lupus Glomerulonephritis.
    • This AI-driven approach has the potential to enhance diagnostic accuracy and improve patient outcomes in LN management.
    • The UGBC scheme represents a significant advancement in applying AI to nephrohistopathology.