A deep‐learning model using automated performance metrics and clinical features to predict urinary continence recovery after robot‐assisted radical prostatectomy

Hung, Andrew J.; Chen, Jian; Ghodoussipour, Saum; Oh, Paul; Liu, Zequn; Nguyen, Jessica; Purushotham, Sanjay; Gill, Inderbir S.; Lu, Yan

doi:10.1111/bju.14735

Cited by 108 publications

(98 citation statements)

References 27 publications

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“…Other than patient factors, surgeon's technical skills also impact patient surgical outcomes. Using objective surgical performance measurements derived directly from the surgical robot, Hung et al [28,29] developed and validated ML and deep-learning algorithms to predict patient length of hospital stay and urinary continence recovery after robotic radical prostatectomy ( Table 2). The ML algorithm achieved 87.2% accuracy in predicting length of hospital stay, and the deeplearning model had a C-index of 0.6 in predicting urinary continence.…”

Section: Prostate Cancermentioning

confidence: 99%

Current status of artificial intelligence applications in urology and their potential to influence clinical practice

et al. 2019

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Objective To investigate the applications of artificial intelligence (AI) in diagnosis, treatment and outcome predictionin urologic diseases and evaluate its advantages over traditional models and methods. Materials and methods A literature search was performed after PROSPERO registration (CRD42018103701) and in compliance with Preferred Reported Items for Systematic Reviews and Meta‐Analyses (PRISMA) methods. Articles between 1994 and 2018 using the search terms “urology”, “artificial intelligence”, “machine learning” were included and categorized by the application of AI in urology. Review articles, editorial comments, articles with no full‐text access, and nonurologic studies were excluded. Results Initial search yielded 231 articles, but after excluding duplicates and following full‐text review and examination of article references, only 111 articles were included in the final analysis. AI applications in urology include: utilizing radiomic imaging or ultrasonic echo data to improve or automate cancer detection or outcome prediction, utilizing digitized tissue specimen images to automate detection of cancer on pathology slides, and combining patient clinical data, biomarkers, or gene expression to assist disease diagnosis or outcome prediction. Some studies employed AI to plan brachytherapy and radiation treatments while others used video based or robotic automated performance metrics to objectively evaluate surgical skill. Compared to conventional statistical analysis, 71.8% of studies concluded that AI is superior in diagnosis and outcome prediction. Conclusion AI has been widely adopted in urology. Compared to conventional statistics AI approaches are more accurate in prediction and more explorative for analyzing large data cohorts. With an increasing library of patient data accessible to clinicians, AI may help facilitate evidence‐based and individualized patient care.

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Section: Prostate Cancermentioning

confidence: 99%

Current status of artificial intelligence applications in urology and their potential to influence clinical practice

et al. 2019

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“…In their practice, sepsis mortality decreased by 53%, and the 30‐day readmission rate dropped from 19.08% to 13.21%. Recently, in urology, ML was used to predict urinary continence recovery [27] and early biochemical recurrence after robot‐assisted prostatectomy [28], and to detect low‐ and high‐grade clear‐cell RCC [29].…”

Section: Discussionmentioning

confidence: 99%

Predicting intra‐operative and postoperative consequential events using machine‐learning techniques in patients undergoing robot‐assisted partial nephrectomy: a Vattikuti Collective Quality Initiative database study

Bhandari

Nallabasannagari²,

Reddiboina³

et al. 2020

BJU International

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Objective To predict intra‐operative (IOEs) and postoperative events (POEs) consequential to the derailment of the ideal clinical course of patient recovery. Materials and Methods The Vattikuti Collective Quality Initiative is a multi‐institutional dataset of patients who underwent robot‐assisted partial nephectomy for kidney tumours. Machine‐learning (ML) models were constructed to predict IOEs and POEs using logistic regression, random forest and neural networks. The models to predict IOEs used patient demographics and preoperative data. In addition to these, intra‐operative data were used to predict POEs. Performance on the test dataset was assessed using area under the receiver‐operating characteristic curve (AUC‐ROC) and area under the precision‐recall curve (PR‐AUC). Results The rates of IOEs and POEs were 5.62% and 20.98%, respectively. Models for predicting IOEs were constructed using data from 1690 patients and 38 variables; the best model had an AUC‐ROC of 0.858 (95% confidence interval [CI] 0.762, 0.936) and a PR‐AUC of 0.590 (95% CI 0.400, 0.759). Models for predicting POEs were trained using data from 1406 patients and 59 variables; the best model had an AUC‐ROC of 0.875 (95% CI 0.834, 0.913) and a PR‐AUC 0.706 (95% CI, 0.610, 0.790). Conclusions The performance of the ML models in the present study was encouraging. Further validation in a multi‐institutional clinical setting with larger datasets would be necessary to establish their clinical value. ML models can be used to predict significant events during and after surgery with good accuracy, paving the way for application in clinical practice to predict and intervene at an opportune time to avert complications and improve patient outcomes.

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“…In addition to urological surgery [89], it is also used in hepatobiliary [90][91][92], otolaryngology [93,94] or orthopedic surgery [95]. Hung [54][55][56] has developed an interesting tool combining robotics and AI. By recording the surgeon's movements while operating, AI provide automated performance metrics and determine global movement features [96][97][98].…”

Section: Discussionmentioning

confidence: 99%

“…This technology allowed the surgeon to identify anatomic structures while performing surgery. In three works, Hung et al [54][55][56] developed an objective method to assess surgical performance, and used parameters from this method as training data for ML algorithms. In their first work [54], the authors created "dVLogger" to record automated performance metrics (APM) for expert and novice urological surgeons.…”

Section: Surgerymentioning

confidence: 99%

“…The authors illustrated this higher efficiency with a 3D instrument-path tracing that demonstrates lesser instrument-path length with higher velocity on dominant and non-dominant instrument in the group of experts. Based on these APM data, a ML model was built to predict clinical outcomes with over 85% accuracy, such as length of hospital stay (LoS) and Foley catheter duration [55] or urinary continence [56], after RARP. Ranasinghe et al [57] used DeepSurv, a NLP algorithm, to compare RARP and open radical prostatectomy (ORP) outcomes from individualized patient-reported information.…”

Section: Surgerymentioning

confidence: 99%

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The Application of Artificial Intelligence in Prostate Cancer Management—What Improvements Can Be Expected? A Systematic Review

et al. 2020

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Artificial Intelligence (AI) is progressively remodeling our daily life. A large amount of information from “big data” now enables machines to perform predictions and improve our healthcare system. AI has the potential to reshape prostate cancer (PCa) management thanks to growing applications in the field. The purpose of this review is to provide a global overview of AI in PCa for urologists, pathologists, radiotherapists, and oncologists to consider future changes in their daily practice. A systematic review was performed, based on PubMed MEDLINE, Google Scholar, and DBLP databases for original studies published in English from January 2009 to January 2019 relevant to PCa, AI, Machine Learning, Artificial Neural Networks, Convolutional Neural Networks, and Natural-Language Processing. Only articles with full text accessible were considered. A total of 1008 articles were reviewed, and 48 articles were included. AI has potential applications in all fields of PCa management: analysis of genetic predispositions, diagnosis in imaging, and pathology to detect PCa or to differentiate between significant and non-significant PCa. AI also applies to PCa treatment, whether surgical intervention or radiotherapy, skills training, or assessment, to improve treatment modalities and outcome prediction. AI in PCa management has the potential to provide a useful role by predicting PCa more accurately, using a multiomic approach and risk-stratifying patients to provide personalized medicine.

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A deep‐learning model using automated performance metrics and clinical features to predict urinary continence recovery after robot‐assisted radical prostatectomy

Cited by 108 publications

References 27 publications

Current status of artificial intelligence applications in urology and their potential to influence clinical practice

Current status of artificial intelligence applications in urology and their potential to influence clinical practice

Predicting intra‐operative and postoperative consequential events using machine‐learning techniques in patients undergoing robot‐assisted partial nephrectomy: a Vattikuti Collective Quality Initiative database study

The Application of Artificial Intelligence in Prostate Cancer Management—What Improvements Can Be Expected? A Systematic Review

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