Abstract:4þ3, it increased from 68.1% to 70.8%. In general, there was an increment of 7-12% across the board.CONCLUSIONS: Our machine learning model has the potential to not only automate but also vastly enhance the utility to detect PCa early and aid in clinical decision making. Given the accuracy of our current model, we are hopeful of producing an automated software workflow that will aid in identifying tumor areas, determining their severity, and influencing treatment decisions.
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