Evidence map›Paper›PMID 35003541›Full record

ArticleIndian journal of orthopaedics2021

Artificial Intelligence to Automatically Assess Scan Quality in Hip Ultrasound.

Abhilash Rakkundeth Hareendranathan, Baljot S Chahal, Dornoosh Zonoobi, Dulai Sukhdeep, Jacob L Jaremko

Open access · greenAbstract read
In one paragraph

Article in Indian journal of orthopaedics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed, 2 pooled it
1.5field-weighted citation impact, top 18% 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

12 citing papers in PubMed, 2 syntheses or guidelines pooled it, 19 citations in OpenAlex.

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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 at 1 institution in 1 country.

Abhilash Rakkundeth HareendranathanDepartment of Radiology and Diagnostic Imaging, University of Alberta, Edmonton, T6G 2B7 Canada.ORCID 0000-0001-6201-9258
Baljot S ChahalDepartment of Radiology and Diagnostic Imaging, University of Alberta, Edmonton, T6G 2B7 Canada.
Dornoosh ZonoobiMEDO.ai Inc, Singapore, Singapore.
Dulai SukhdeepDepartment of Radiology and Diagnostic Imaging, University of Alberta, Edmonton, T6G 2B7 Canada.
Jacob L JaremkoDepartment of Radiology and Diagnostic Imaging, University of Alberta, Edmonton, T6G 2B7 Canada.
University of Alberta · CA

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeSince it is fast, inexpensive and increasingly portable, ultrasound can be used for early detection of Developmental Dysplasia of the Hip (DDH) in infants at point-of-care. However, accurate interpretation\is highly dependent on scan quality. Poor-quality images lead to misdiagnosis, but inexperienced users may not even recognize the deficiencies in the images. Currently, users assess scan quality subjectively, based on image landmarks which are prone to human errors. Instead, we propose using Artificial Intelligence (AI) to automatically assess scan quality.

methodsWe trained separate Convolutional Neural Network (CNN) models to detect presence of each of four commonly used ultrasound landmarks in each hip image: straight horizontal iliac wing, labrum, os ischium and midportion of the femoral head. We used 100 3D ultrasound (3DUS) images for training and validated the technique on a set of 107 3DUS images also scored for landmarks by three non-expert readers and one expert radiologist.

resultsWe got AI ≥ 85% accuracy for all four landmarks (ilium = 0.89, labrum = 0.94, os ischium = 0.85, femoral head = 0.98) as a binary classifier between adequate and inadequate scan quality. Our technique also showed excellent agreement with manual assessment in terms of Intraclass Correlation Coefficient (ICC) and Cohen's kappa coefficient (

conclusionThis new technique could ensure high scan quality and facilitate more widespread use of ultrasound in population screening of DDH. GRAPHICAL ABSTRACT:

Indexed as

3D ultrasoundArtificial intelligenceConvolutional Neural NetworksDevelopmental dysplasia of the hipDysplasiaGraf criteriaHip ultrasoundRecurrent neural networkScan qualityUniversal screening

Identifiers

PMID35003541
PMCPMC8688598
OpenAlexW3194551934

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.