Evidence map›Paper›PMID 32064241›Full record

ArticleFrontiers in pediatrics2020

Using Deep Learning Algorithms to Grade Hydronephrosis Severity: Toward a Clinical Adjunct.

Lauren C Smail, Kiret Dhindsa, Luis H Braga, Suzanna Becker, Ranil R Sonnadara

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Frontiers in pediatrics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
22citing 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

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

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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

5 authors.

Lauren C SmailDepartment of Psychology, Neuroscience & Behaviour, McMaster University, Hamilton, ON, Canada.
Kiret DhindsaDepartment of Surgery, McMaster University, Hamilton, ON, Canada.
Luis H BragaDivision of Urology, Department of Surgery, McMaster University, Hamilton, ON, Canada.
Suzanna BeckerDepartment of Psychology, Neuroscience & Behaviour, McMaster University, Hamilton, ON, Canada.
Ranil R SonnadaraDepartment of Psychology, Neuroscience & Behaviour, McMaster University, Hamilton, ON, Canada.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Grading hydronephrosis severity relies on subjective interpretation of renal ultrasound images. Deep learning is a data-driven algorithmic approach to classifying data, including images, presenting a promising option for grading hydronephrosis. The current study explored the potential of deep convolutional neural networks (CNN), a type of deep learning algorithm, to grade hydronephrosis ultrasound images according to the 5-point Society for Fetal Urology (SFU) classification system, and discusses its potential applications in developing decision and teaching aids for clinical practice. We developed a five-layer CNN to grade 2,420 sagittal hydronephrosis ultrasound images [191 SFU 0 (8%), 407 SFU I (17%), 666 SFU II (28%), 833 SFU III (34%), and 323 SFU IV (13%)], from 673 patients ranging from 0 to 116.29 months old (

Indexed as

deep learningdiagnostic aiddiagnostic imaginggradinghydronephrosismachine learningteaching aidultrasound

Identifiers

PMID32064241
PMCPMC7000524

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.