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Critical Evaluation of Artificial Intelligence as Digital Twin of Pathologist for Prostate Cancer Pathology
Adjusting the decision threshold for the secondary Gleason pattern from 5% to 10% improved the concordance level between pathologists and vPatho for tumor grading on prostatectomy specimens (kappa from 0. 44 to 0. 64).
Plexus Convolutional Neural Network (PlexusNet): A novel neural network architecture for histologic image analysis
A well-fitted PlexusNet-based model delivered comparable classification performance (AUC: 0. 963) in distinguishing prostate cancer from healthy tissues, although it was at least 23 times smaller, had a better model calibration and clinical utility than the comparison models.
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