Machine learning-based comparison of transperineal vs. transrectal biopsy for prostate cancer diagnosis: evaluating procedural effectiveness

These findings highlight the potential of ML to enhance the diagnostic accuracy of prostate cancer detection irrespective of the biopsy method. However, TP biopsy data showed marginally higher accuracy, possibly because of the lower risk of contamination. While ML holds great promise for transforming prostate cancer care, further research is needed to address limitations. Collaboration between clinicians, data scientists, and researchers is crucial to ensure the clinical relevance and interpretability of ML models.