Machine learning applications to enhance patient specific care for urologic surgery.

PURPOSE

As computational power has improved over the past 20 years, the daily application of machine learning methods has become more prevalent in daily life. Additionally, there is increasing interest in the clinical application of machine learning techniques. We sought to review the current literature regarding machine learning applications for patient-specific urologic surgical care.

METHODS

We performed a broad search of the current literature via the PubMed-Medline and Google Scholar databases up to Dec 2020. The search terms "urologic surgery" as well as "artificial intelligence", "machine learning", "neural network", and "automation" were used.

RESULTS

The focus of machine learning applications for patient counseling is disease-specific. For stone disease, multiple studies focused on the prediction of stone-free rate based on preoperative characteristics of clinical and imaging data. For kidney cancer, many studies focused on advanced imaging analysis to predict renal mass pathology preoperatively. Machine learning applications in prostate cancer could provide for treatment counseling as well as prediction of disease-specific outcomes. Furthermore, for bladder cancer, the reviewed studies focus on staging via imaging, to better counsel patients towards neoadjuvant chemotherapy. Additionally, there have been many efforts on automatically segmenting and matching preoperative imaging with intraoperative anatomy.

CONCLUSION

Machine learning techniques can be implemented to assist patient-centered surgical care and increase patient engagement within their decision-making processes. As data sets improve and expand, especially with the transition to large-scale EHR usage, these tools will improve in efficacy and be utilized more frequently.