Evidence map›Paper›PMID 34397576›Full record

ReviewCurrent opinion in ophthalmology2021

Artificial intelligence and ophthalmic surgery.

Kapil Mishra, Theodore Leng

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Current opinion in ophthalmology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed, 1 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

10 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Review
  3. Review
  4. Article
  5. Article
  6. Review
  7. Article
  8. Review
  9. Robotic Assistance for Intraocular Microsurgery: Challenges and Perspectives.Proceedings of the IEEE. Institute of Electrical and Electronics Engineers · 2022
    Article
  10. Review
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

2 authors.

Kapil MishraDepartment of Ophthalmology, Byers Eye Institute at Stanford, Stanford University School of Medicine, Palo Alto, California, USA.
Theodore Leng

Funding

Stanford Vision Research CoreP30EY026877 · NEI · STANFORD UNIVERSITY · PI Jeffrey L Goldberg · 2017 to 2026
$8.0M
NEI NIH HHS P30 EY026877
6 · The paper itself

Abstract

purpose of reviewArtificial intelligence and deep learning have become important tools in extracting data from ophthalmic surgery to evaluate, teach, and aid the surgeon in all phases of surgical management. The purpose of this review is to highlight the ever-increasing intersection of computer vision, machine learning, and ophthalmic microsurgery. RECENT

findingsDeep learning algorithms are being applied to help evaluate and teach surgical trainees. Artificial intelligence tools are improving real-time surgical instrument tracking, phase segmentation, as well as enhancing the safety of robotic-assisted vitreoretinal surgery. SUMMARY: Similar to strides appreciated in ophthalmic medical disease, artificial intelligence will continue to become an important part of surgical management of ocular conditions. Machine learning applications will help push the boundaries of what surgeons can accomplish to improve patient outcomes.

Indexed as

Artificial IntelligenceOphthalmologic Surgical ProceduresAlgorithmsClinical CompetenceDeep LearningHumansMachine LearningMicrosurgeryRobotic Surgical Procedures

Identifiers

PMID34397576
PMCPMC10655904

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.