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Pediatr Radiol. 2020 Jan 13. doi: 10.1007/s00247-019-04593-0. [Epub ahead of print]

Computer-aided diagnosis for World Health Organization-defined chest radiograph primary-endpoint pneumonia in children.

Author information

1
Department of Radiology, Faculty of Health Sciences, University of Witwatersrand, Johannesburg, 2000, South Africa. Nasreen.Mahomed@wits.ac.za.
2
Medical Research Council, Respiratory and Meningeal Pathogens Research Unit, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa. Nasreen.Mahomed@wits.ac.za.
3
Diagnostic Image Analysis Group, Department of Radiology and Nuclear Medicine, Radboudumc, Nijmegen, the Netherlands.
4
Radboud UMC, Thirona, Nijmegen, the Netherlands.
5
Medical Research Council, Respiratory and Meningeal Pathogens Research Unit, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa.
6
Departments of Paediatrics and Child Health, Chris Hani Baragwanath Academic Hospital, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa.
7
Departments of Science/National Research Foundation: Vaccine Preventable Diseases, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa.
8
Department of Radiology, Faculty of Health Sciences, University of Witwatersrand, Johannesburg, 2000, South Africa.

Abstract

BACKGROUND:

The chest radiograph is the most common imaging modality to assess childhood pneumonia. It has been used in epidemiological and vaccine efficacy/effectiveness studies on childhood pneumonia.

OBJECTIVE:

To develop computer-aided diagnosis (CAD4Kids) for chest radiography in children and to evaluate its accuracy in identifying World Health Organization (WHO)-defined chest radiograph primary-endpoint pneumonia compared to a consensus interpretation.

MATERIALS AND METHODS:

Chest radiographs were independently evaluated by three radiologists based on WHO criteria. Automatic lung field segmentation was followed by manual inspection and correction, training, feature extraction and classification. Radiographs were filtered with Gaussian derivatives on multiple scales, extracting texture features to classify each pixel in the lung region. To obtain an image score, the 95th percentile score of the pixels was used. Training and testing were done in 10-fold cross validation.

RESULTS:

The radiologist majority consensus reading of 858 interpretable chest radiographs included 333 (39%) categorised as primary-endpoint pneumonia, 208 (24%) as other infiltrate only and 317 (37%) as no primary-endpoint pneumonia or other infiltrate. Compared to the reference radiologist consensus reading, CAD4Kids had an area under the receiver operator characteristic (ROC) curve of 0.850 (95% confidence interval [CI] 0.823-0.876), with a sensitivity of 76% and specificity of 80% for identifying primary-endpoint pneumonia on chest radiograph. Furthermore, the ROC curve was 0.810 (95% CI 0.772-0.846) for CAD4Kids identifying primary-endpoint pneumonia compared to other infiltrate only.

CONCLUSION:

Further development of the CAD4Kids software and validation in multicentre studies are important for future research on computer-aided diagnosis and artificial intelligence in paediatric radiology.

KEYWORDS:

Accuracy; Children; Computer-aided diagnosis; Pneumonia; Primary-endpoint; Radiography; World Health Organization

PMID:
31930429
DOI:
10.1007/s00247-019-04593-0

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