Foundations of Lesion Detection Using Machine Learning in Clinical Neuroimaging.

Manoj Mannil, Nicolin Hainc, Risto Grkovski, Sebastian Winklhofer

Abstract read

In one paragraph

Article in Acta neurochirurgica. Supplement, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it

0cells of the map it votes in

0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

4 authors.

Manoj MannilClinic of Radiology, University Hospital Münster, Münster, Germany.

Nicolin HaincDepartment of Medical Imaging, Division of Neuroradiology, Toronto Western Hospital, University Health Network, Toronto, ON, Canada.

Risto GrkovskiDepartment of Neuroradiology, Clinical Neuroscience Center, University Hospital Zürich, University of Zurich, Zurich, Switzerland.

Sebastian WinklhoferDepartment of Neuroradiology, Clinical Neuroscience Center, University Hospital Zürich, University of Zurich, Zurich, Switzerland. Sebastian.winklhofer@usz.ch.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This chapter describes technical considerations and current and future clinical applications of lesion detection using machine learning in the clinical setting. Lesion detection is central to neuroradiology and precedes all further processes which include but are not limited to lesion characterization, quantification, longitudinal disease assessment, prognosis, and prediction of treatment response. A number of machine learning algorithms focusing on lesion detection have been developed or are currently under development which may either support or extend the imaging process. Examples include machine learning applications in stroke, aneurysms, multiple sclerosis, neuro-oncology, neurodegeneration, and epilepsy.

Indexed as

Artificial IntelligenceStrokeAlgorithmsHumansMachine LearningNeuroimagingArtificial intelligenceLesion detectionMachine learningNeuroimagingNeuroradiology

Identifiers

PMID34862541

DOI10.1016/j.biopsych.2020.01.016

What OpenQuestion holds

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Registered trials

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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.