Evidence map›Paper›PMID 32925312›Full record

ReviewCurrent opinion in urology2020

Machine learning in the optimization of robotics in the operative field.

Runzhuo Ma, Erik B Vanstrum, Ryan Lee, Jian Chen, Andrew J Hung

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Current opinion in urology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
16citing papers in PubMed, 2 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

16 citing papers in PubMed, 2 syntheses or guidelines pooled it.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Runzhuo MaCenter for Robotic Simulation & Education, Catherine & Joseph Aresty Department of Urology, USC Institute of Urology, University of Southern California, Los Angeles, California, USA.
Erik B Vanstrum
Ryan Lee
Jian Chen
Andrew J Hung

Funding

Development of Machine Learning Algorithms to Assess and Train Vesico-Urethral Anastomosis during Robot Assisted Radical ProstatectomyK23EB026493 · NIBIB · UNIVERSITY OF SOUTHERN CALIFORNIA · PI HUNG, ANDREW · 2018 to 2020
$579k
NIBIB NIH HHS K23 EB026493
6 · The paper itself

Abstract

purpose of reviewThe increasing use of robotics in urologic surgery facilitates collection of 'big data'. Machine learning enables computers to infer patterns from large datasets. This review aims to highlight recent findings and applications of machine learning in robotic-assisted urologic surgery. RECENT

findingsMachine learning has been used in surgical performance assessment and skill training, surgical candidate selection, and autonomous surgery. Autonomous segmentation and classification of surgical data have been explored, which serves as the stepping-stone for providing real-time surgical assessment and ultimately, improve surgical safety and quality. Predictive machine learning models have been created to guide appropriate surgical candidate selection, whereas intraoperative machine learning algorithms have been designed to provide 3-D augmented reality and real-time surgical margin checks. Reinforcement-learning strategies have been utilized in autonomous robotic surgery, and the combination of expert demonstrations and trial-and-error learning by the robot itself is a promising approach towards autonomy. SUMMARY: Robot-assisted urologic surgery coupled with machine learning is a burgeoning area of study that demonstrates exciting potential. However, further validation and clinical trials are required to ensure the safety and efficacy of incorporating machine learning into surgical practice.

Indexed as

Machine LearningRobotic Surgical ProceduresUrologic Surgical ProceduresAlgorithmsClinical CompetenceFemaleFemale Urogenital DiseasesHumansMaleMale Urogenital DiseasesPatient SelectionRobotics

Identifiers

PMID32925312
PMCPMC7735438

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.