Evidence map›Paper›PMID 40274719›Full record

ArticleInternational journal of computer assisted radiology and surgery2025

Objective skill assessment for cataract surgery from surgical microscope video.

Rebecca Hisey, Henry Lee, Adrienne Duimering, John Liu, Vasudha Gupta, Tamas Ungi, Christine Law, Gabor Fichtinger, Matthew Holden

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Article in International journal of computer assisted radiology and surgery, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

What it found

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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

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3 · Its place in the literature

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No citing paper in PubMed yet.

4 · The record

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5 · Who and what money

Authors and funding

9 authors.

Rebecca HiseySchool of Computing, Queen's University, Kingston, ON, Canada. rebecca.hisey@queensu.ca.ORCID http://orcid.org/0000-0002-1036-5789
Henry LeeSchool of Computing, Queen's University, Kingston, ON, Canada.
Adrienne DuimeringDepartment of Ophthalmology, Queen's University, Kingston, ON, Canada.
John LiuDepartment of Ophthalmology, Queen's University, Kingston, ON, Canada.
Vasudha GuptaDepartment of Ophthalmology, Queen's University, Kingston, ON, Canada.
Tamas UngiSchool of Computing, Queen's University, Kingston, ON, Canada.
Christine LawDepartment of Ophthalmology, Queen's University, Kingston, ON, Canada.
Gabor FichtingerSchool of Computing, Queen's University, Kingston, ON, Canada.
Matthew HoldenSchool of Computer Science, Carleton University, Ottawa, ON, Canada.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveVideo offers an accessible method for automated surgical skill evaluation; however, many platforms still rely on traditional six-degree-of-freedom (6-DOF) tracking systems, which can be costly, cumbersome, and challenging to apply clinically. This study aims to demonstrate that trainee skill in cataract surgery can be assessed effectively using only object detection from monocular surgical microscope video.

methodsOne ophthalmologist and four residents performed cataract surgery on a simulated eye five times each, generating 25 recordings. Recordings included both the surgical microscope video and 6-DOF instrument tracking data. Videos were graded by two expert ophthalmologists using the ICO-OSCAR:SICS rubric. We computed motion-based metrics using both object detection from video and 6-DOF tracking. We first examined correlations between each metric and expert scores for each rubric criteria. Then, using these findings, we trained an ordinal regression model to predict scores from each tracking modality and compared correlation strengths with expert scores.

resultsMetrics from object detection generally showed stronger correlations with expert scores than 6-DOF tracking. For score prediction, 6-DOF tracking showed no significant advantage, while scores predicted from object detection achieved significantly stronger correlations with expert scores for four scoring criteria.

conclusionOur results indicate that skill assessment from monocular surgical microscope video can match, and in some cases exceed, the correlation strengths of 6-DOF tracking assessments. This finding supports the feasibility of using object detection for skill assessment without additional hardware.

Indexed as

Cataract ExtractionClinical CompetenceMicroscopyVideo RecordingEducational MeasurementHumansInternship and ResidencyCataract surgeryObject detectionVideo-based skill assessment

Identifiers

PMID40274719

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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.