Automated diagnosis of prostate cancer using Artificial Intelligence: a systematic literature review

Abstract

Prostate cancer is one of the most preventable causes of death. Periodic testing, seconded by precursors such as living habits, heritage, and exposure, to specify materials, help healthcare providers achieve early detection, a desirable scenario that positively correlates with survival. However, the currently available diagnosing mechanisms have a great opportunity of improvement in terms of invasiveness, sensitivity and timing before patients reach advanced stages with a significant probability of metastasis. Supervised artificial intelligence enables early diagnosis and excludes patients from unpleasant biopsies. In this work, we gathered information about methodologies, techniques, metrics, and benchmarks to accomplish early prostate cancer detection, including pipelines with associated patents and knowledge transfer mechanisms intending to find the reasons precluding the solutions from being masively adopted in the standats of care