{'fraction_non_alphanumeric': 0.05909510618651893, 'fraction_numerical': 0.025392428439519853, 'mean_word_length': 4.9046321525885554, 'pattern_counts': {'":': 0, '<': 2, '<?xml version=': 0, '>': 1, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Utilizing Machine Learning and Automated Performance Metrics to Evaluate Robot-Assisted Radical Prostatectomy Performance and Predict Outcomes.

PURPOSE
Surgical performance is critical for clinical outcomes. We present a novel machine learning (ML) method of processing automated performance metrics (APMs) to evaluate surgical performance and predict clinical outcomes after robot-assisted radical prostatectomy (RARP).


MATERIALS AND METHODS
We trained three ML algorithms utilizing APMs directly from robot system data (training material) and hospital length of stay (LOS; training label) (≤2 days and >2 days) from 78 RARP cases, and selected the algorithm with the best performance. The selected algorithm categorized the cases as "Predicted as expected LOS (pExp-LOS)" and "Predicted as extended LOS (pExt-LOS)." We compared postoperative outcomes of the two groups (Kruskal-Wallis/Fisher's exact tests). The algorithm then predicted individual clinical outcomes, which we compared with actual outcomes (Spearman's correlation/Fisher's exact tests). Finally, we identified five most relevant APMs adopted by the algorithm during predicting.


RESULTS
The "Random Forest-50" (RF-50) algorithm had the best performance, reaching 87.2% accuracy in predicting LOS (73 cases as "pExp-LOS" and 5 cases as "pExt-LOS"). The "pExp-LOS" cases outperformed the "pExt-LOS" cases in surgery time (3.7 hours vs 4.6 hours, p = 0.007), LOS (2 days vs 4 days, p = 0.02), and Foley duration (9 days vs 14 days, p = 0.02). Patient outcomes predicted by the algorithm had significant association with the "ground truth" in surgery time (p < 0.001, r = 0.73), LOS (p = 0.05, r = 0.52), and Foley duration (p < 0.001, r = 0.45). The five most relevant APMs, adopted by the RF-50 algorithm in predicting, were largely related to camera manipulation.


CONCLUSION
To our knowledge, ours is the first study to show that APMs and ML algorithms may help assess surgical RARP performance and predict clinical outcomes. With further accrual of clinical data (oncologic and functional data), this process will become increasingly relevant and valuable in surgical assessment and training.