Technical Skill Assessment in Minimally Invasive Surgery Using Artificial Intelligence: A Systematic Review

Abstract

Objective: To review artificial intelligence (AI) based applications for the assessment of technical skills in minimally invasive surgery. Background: As technical skill assessment in surgery relies on expert opinion, it is time-consuming, costly, and often lacks objectivity. Analysis of routinely generated data by AI methods has the potential for automatic technical skill assessment in minimally invasive surgery. Methods: A systematic search of Medline, Embase, Web of Science and IEEE Xplore was performed to identify original articles reporting the use of AI in the assessment of technical skill in minimally invasive surgery. Risk of bias (RoB) and quality of included studies were analyzed according to Quality Assessment of Diagnostic Accuracy Studies criteria and the modified Joanna Briggs Institute checklists, respectively. Findings were reported according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses statement. Results: In total, 1467 articles were identified, 37 articles met eligibility criteria and were analyzed. Motion data extracted from surgical videos (49%) or kinematic data from robotic systems or sensors (46%) were the most frequent input data for AI. Most studies used deep learning (73%) and predicted technical skills using an ordinal assessment scale (73%) with good accuracies in simulated settings. However, all proposed models were in development stage, only 8% were externally validated and 16% showed a low RoB. Conclusion: AI is promising to automate technical skill assessment in minimally invasive surgery. However, models should be benchmarked on representative datasets using predefined performance metrics and tested in clinical implementation studies.

Competing Interest Statement

Pietro Mascagni: is the scientific director of Global Surgical AI Collaborative Nicolas Padoy: ANR grants ANR-10-IAHU-02 + ANR-20-CHIA-0029-01, BPI grant CONDOR Joel Lavanchy: SNF grant P500PM_206724.

Clinical Protocols

https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=267714

Funding Statement

Joel Lavanchy was funded by the Swiss National Science Foundation (grant No P500PM_206724). This work was partially supported by French state funds managed by the ANR within the Investments for the future program under Grant ANR-10-IAHU-02 (IHU Strasbourg).

Author Declarations

I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.

Yes

I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals.

Yes

I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).

Yes

I have followed all appropriate research reporting guidelines and uploaded the relevant EQUATOR Network research reporting checklist(s) and other pertinent material as supplementary files, if applicable.

Yes

Data Availability

All data produced in the present work are contained in the manuscript.

留言 (0)

沒有登入
gif