Evidence map›Paper›PMID 39433583›Full record

ArticleSurgical endoscopy2024

Deep learning prediction of error and skill in robotic prostatectomy suturing.

N Sirajudeen, M Boal, D Anastasiou, J Xu, D Stoyanov, J Kelly, J W Collins, A Sridhar, E Mazomenos, N K Francis

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In one paragraph

Article in Surgical endoscopy, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
–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

7 citing papers in PubMed.

  1. Review
  2. Artificial intelligence in urology training Enhancing annotation, feedback, and evaluation in robotic, laparoscopic, and endoscopic surgery.Canadian Urological Association journal = Journal de l'Association des urologues du Canada · 2026
    Review
  3. Review
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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

10 authors.

N SirajudeenWellcome/ESPRC Centre for Interventional Surgical Sciences (WEISS), University College London (UCL), London, UK.
M BoalWellcome/ESPRC Centre for Interventional Surgical Sciences (WEISS), University College London (UCL), London, UK.
D AnastasiouWellcome/ESPRC Centre for Interventional Surgical Sciences (WEISS), University College London (UCL), London, UK.
J XuWellcome/ESPRC Centre for Interventional Surgical Sciences (WEISS), University College London (UCL), London, UK.
D StoyanovWellcome/ESPRC Centre for Interventional Surgical Sciences (WEISS), University College London (UCL), London, UK.
J KellyDivision of Surgery and Interventional Science, Research Department of Targeted Intervention, UCL, London, UK.
J W CollinsDivision of Surgery and Interventional Science, Research Department of Targeted Intervention, UCL, London, UK.
A SridharDivision of Surgery and Interventional Science, Research Department of Targeted Intervention, UCL, London, UK.
E MazomenosWellcome/ESPRC Centre for Interventional Surgical Sciences (WEISS), University College London (UCL), London, UK.
N K FrancisThe Griffin Institute, Northwick Park and St Marks Hospital, London, UK. n.francis@griffininstitute.org.uk.ORCID 0000-0001-8498-9175

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundManual objective assessment of skill and errors in minimally invasive surgery have been validated with correlation to surgical expertise and patient outcomes. However, assessment and error annotation can be subjective and are time-consuming processes, often precluding their use. Recent years have seen the development of artificial intelligence models to work towards automating the process to allow reduction of errors and truly objective assessment. This study aimed to validate surgical skill rating and error annotations in suturing gestures to inform the development and evaluation of AI models.

methodsSAR-RARP50 open data set was blindly, independently annotated at the gesture level in Robotic-Assisted Radical Prostatectomy (RARP) suturing. Manual objective assessment tools and error annotation methodology, Objective Clinical Human Reliability Analysis (OCHRA), were used as ground truth to train and test vision-based deep learning methods to estimate skill and errors. Analysis included descriptive statistics plus tool validity and reliability.

resultsFifty-four RARP videos (266 min) were analysed. Strong/excellent inter-rater reliability (range r = 0.70-0.89, p < 0.001) and very strong correlation (r = 0.92, p < 0.001) between objective assessment tools was demonstrated. Skill estimation of OSATS and M-GEARS had a Spearman's Correlation Coefficient 0.37 and 0.36, respectively, with normalised mean absolute error representing a prediction error of 17.92% (inverted "accuracy" 82.08%) and 20.6% (inverted "accuracy" 79.4%) respectively. The best performing models in error prediction achieved mean absolute precision of 37.14%, area under the curve 65.10% and Macro-F1 58.97%.

conclusionsThis is the first study to employ detailed error detection methodology and deep learning models within real robotic surgical video. This benchmark evaluation of AI models sets a foundation and promising approach for future advancements in automated technical skill assessment.

Indexed as

Clinical CompetenceDeep LearningProstatectomyRobotic Surgical ProceduresSuture TechniquesHumansMaleReproducibility of ResultsDeep learningErrorsRoboticTechnical skill

Identifiers

PMID39433583
PMCPMC11614916

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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.