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Published on: February 16, 2020
Optimising computer aided detection to identify intra-thoracic tuberculosis on chest x-ray in South African children
Megan Palmer1, James A Seddon1,2, Marieke M van der Zalm1
1Faculty of Medicine and Health Sciences, Department of Paediatrics and Child Health, Demond Tutu TB Centre, Stellenbosch University, Cape Town, South Africa.
Insights
Computer-aided detection (CAD) for paediatric tuberculosis shows promise. Fine-tuning an adult CAD system significantly improved its ability to detect tuberculosis on children's chest x-rays, enhancing diagnostic accuracy.
Area of Science:
- Medical Imaging
- Paediatric Infectious Diseases
- Artificial Intelligence in Healthcare
Background:
- Diagnostic tools for paediatric tuberculosis are limited, often relying on clinical algorithms and chest x-rays.
- Computer-aided detection (CAD) systems have shown potential for tuberculosis detection in adult chest x-rays.
Purpose of the Study:
- To evaluate and optimize the performance of an adult CAD system (CAD4TB) for identifying tuberculosis in paediatric chest x-rays.
- To assess the impact of fine-tuning the CAD system using a paediatric dataset.
Main Methods:
- Chest x-rays from 620 children under 13 with presumptive tuberculosis were evaluated.
- An adult CAD system (CAD4TB) was applied to chest x-rays, with performance measured against expert radiological references.
- The CAD system was fine-tuned using a training set of paediatric chest x-rays and its performance compared to the original model.
Main Results:
- The original CAD4TB model had an area under the receiver operating characteristic curve (AUC) of 0.58.
- After fine-tuning with paediatric data, the AUC improved significantly to 0.72 (p = 0.0016).
Conclusions:
- Fine-tuning an adult CAD system significantly improves its performance for detecting tuberculosis in paediatric chest x-rays.
- CAD shows potential as an additional diagnostic tool for paediatric tuberculosis.
- Further research with larger, diverse datasets is recommended to evaluate CAD's role in paediatric tuberculosis diagnosis and treatment algorithms.
Abstract:
Diagnostic tools for paediatric tuberculosis remain limited, with heavy reliance on clinical algorithms which include chest x-ray. Computer aided detection (CAD) for tuberculosis on chest x-ray has shown promise in adults. We aimed to measure and optimise the performance of an adult CAD system, CAD4TB, to identify tuberculosis on chest x-rays from children with presumptive tuberculosis. Chest x-rays from 620 children <13 years enrolled in a prospective observational diagnostic study in South Africa, were evaluated. All chest x-rays were read by a panel of expert readers who attributed each with a radiological reference of either 'tuberculosis' or 'not tuberculosis'. Of the 525 chest x-rays included in this analysis, 80 (40 with a reference of 'tuberculosis' and 40 with 'not tuberculosis') were allocated to an independent test set. The remainder made up the training set. The performance of CAD4TB to identify 'tuberculosis' versus 'not tuberculosis' on chest x-ray against the radiological reference read was calculated. The CAD4TB software was then fine-tuned using the paediatric training set. We compared the performance of the fine-tuned model to the original model. Our findings were that the area under the receiver operating characteristic curve (AUC) of the original CAD4TB model, prior to fine-tuning, was 0.58. After fine-tuning there was an improvement in the AUC to 0.72 (p = 0.0016). In this first-ever description of the use of CAD to identify tuberculosis on chest x-ray in children, we demonstrate a significant improvement in the performance of CAD4TB after fine-tuning with a set of well-characterised paediatric chest x-rays. CAD has the potential to be a useful additional diagnostic tool for paediatric tuberculosis. We recommend replicating the methods we describe using a larger chest x-ray dataset from a more diverse population and evaluating the potential role of CAD to replace a human-read chest x-ray within treatment-decision algorithms for paediatric tuberculosis.
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