Evidence map›Paper›PMID 31422572›Full record

SynthesisNeurosurgical review2020

Machine learning applications to clinical decision support in neurosurgery: an artificial intelligence augmented systematic review.

Quinlan D Buchlak, Nazanin Esmaili, Jean-Christophe Leveque, Farrokh Farrokhi, Christine Bennett, Massimo Piccardi, Rajiv K Sethi

Abstract readSystematic Review
PubMed Publisher
In one paragraph

Synthesis in Neurosurgical review, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 99 papers, 11 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
99citing papers in PubMed, 11 pooled it
10.4field-weighted citation impact, top 1% of its field
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

99 citing papers in PubMed, 11 syntheses or guidelines pooled it, 222 citations in OpenAlex.

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  19. Artificial Intelligence in Planning for Spine Surgery.Current reviews in musculoskeletal medicine · 2025
    Review
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39 more citing papers are in PubMed but not listed here.

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

7 authors at 4 institutions in 2 countries.

Quinlan D BuchlakSchool of Medicine, The University of Notre Dame, Sydney, NSW, Australia. quinlan.buchlak1@my.nd.edu.au.ORCID http://orcid.org/0000-0001-6749-3223
Nazanin EsmailiSchool of Medicine, The University of Notre Dame, Sydney, NSW, Australia.
Jean-Christophe LevequeNeuroscience Institute, Virginia Mason Medical Center, Seattle, WA, USA.
Farrokh FarrokhiNeuroscience Institute, Virginia Mason Medical Center, Seattle, WA, USA.
Christine BennettSchool of Medicine, The University of Notre Dame, Sydney, NSW, Australia.
Massimo PiccardiUniversity of Technology Sydney, Sydney, NSW, Australia.
Rajiv K SethiNeuroscience Institute, Virginia Mason Medical Center, Seattle, WA, USA.
Virginia Mason Medical Center · USThe University of Notre Dame Australia · AUSax Institute · AUUniversity of Technology Sydney · AU

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Machine learning (ML) involves algorithms learning patterns in large, complex datasets to predict and classify. Algorithms include neural networks (NN), logistic regression (LR), and support vector machines (SVM). ML may generate substantial improvements in neurosurgery. This systematic review assessed the current state of neurosurgical ML applications and the performance of algorithms applied. Our systematic search strategy yielded 6866 results, 70 of which met inclusion criteria. Performance statistics analyzed included area under the receiver operating characteristics curve (AUC), accuracy, sensitivity, and specificity. Natural language processing (NLP) was used to model topics across the corpus and to identify keywords within surgical subspecialties. ML applications were heterogeneous. The densest cluster of studies focused on preoperative evaluation, planning, and outcome prediction in spine surgery. The main algorithms applied were NN, LR, and SVM. Input and output features varied widely and were listed to facilitate future research. The accuracy (F

Indexed as

Artificial IntelligenceDecision Support Systems, ClinicalMachine LearningDeep LearningHumansNeurosurgeryNeurosurgical ProceduresSupport Vector MachineArtificial intelligenceDeep brain stimulationDeep learningMachine learningNeurosurgeryRisk stratificationSpine surgery

Identifiers

PMID31422572
OpenAlexW2966977115

What OpenQuestion holds

Textmetadata
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.