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Computer-aided detection for radiological disease severity classification on chest radiograph in children with
Megan Palmer1, Ineke Derks1, H Simon Schaaf1
1Desmond Tutu TB Centre, Department of Paediatrics and Child Health, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa.
Insights
Computer-aided detection (CAD) shows promise in classifying pediatric tuberculosis (TB) severity on chest X-rays (CXRs). This technology could help more children access shorter TB treatment regimens by automating CXR interpretation.
Area of Science:
- Medical Imaging
- Artificial Intelligence in Medicine
- Pediatric Infectious Diseases
Background:
- Shorter tuberculosis (TB) treatment regimens require accurate chest X-ray (CXR) interpretation to classify disease severity in children.
- Limited access to specialist radiologists for CXR interpretation poses a challenge in many settings.
Purpose of the Study:
- To evaluate the utility of computer-aided detection (CAD) software in automating the classification of radiological disease severity in pediatric TB patients.
- To assess if CAD tools can differentiate between severe and non-severe TB on CXRs in children.
Main Methods:
- Combined three CXR datasets from children with confirmed or clinically diagnosed TB.
- Two expert readers classified CXRs as severe or non-severe based on WHO guidelines.
- Utilized CAD4TB v7.0 and qXR v3.0 software to generate CAD scores, without specific pediatric training.
- Compared CAD scores between human-classified severe and non-severe CXRs.
Main Results:
- Median CAD scores were significantly lower for non-severe CXRs compared to severe CXRs.
- The performance, measured by area under the receiver operating curve, ranged from 0.76 to 0.82.
- The difference in CAD scores was most pronounced in children over 5 years old.
Conclusions:
- Computer-aided detection (CAD) demonstrates potential as a tool for stratifying TB disease severity in children via CXR.
- CAD technology could facilitate broader access to shorter TB treatment regimens for pediatric patients.
- Further investment in pediatric-specific CAD training and development is recommended to optimize its application beyond initial screening and diagnosis.
Abstract:
Implementation of the World Health Organization's (WHO) recommended shorter 4-month treatment regimen for non-severe tuberculosis (TB) in children requires classification of disease severity on chest X-ray (CXR). Access to specialists for CXR interpretation is limited. We explored the use of computer-aided detection of CXR ("CAD") to automate CXR classification of radiological disease severity. To do this, we combined three CXR datasets from children with confirmed and clinically diagnosed TB across the disease spectrum. CXRs were independently classified as radiologically severe or non-severe by two expert human readers. Definition of radiological disease severity aligned with WHO guidelines. CAD scores were generated by CAD4TB v7.0 and qXR v3.0 software. Neither software product was specifically trained with paediatric CXRs or for disease severity classification. We compared CAD scores between CXRs classified by human readers as non-severe versus CXRs classified by human readers as severe. CXRs from 526 children were included in this analysis: median age was 2.1 years (inter-quartile range 1-4.2 years); 57% of the children had microbiologically confirmed TB. We found that median CAD scores were significantly lower for CXRs classified as non-severe versus severe by human readers; the difference was greatest in children >5 years. The area under the receiver operating curve was 0.82 and 0.78 for qXR, and 0.79 and 0.76 for CAD4TB, against the reference of 'severe' as classified by each individual human reader respectively. These results demonstrate that CAD is a promising tool for TB disease severity stratification and has the potential to support access to shorter TB treatment regimens for children. Investment in paediatric CAD training and development to optimize solutions for children beyond the TB screening and diagnosis use-case is warranted.
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