Possible applications of AI
Aside from CAD4TB, other paediatric products are being developed. “In the near future, we can expect some products on pneumonia detection in children, but also detection of lung metastases,” Schalekamp said. “These products are already available for the adult population and could be easily transferred to the paediatric population.”Researchers in South Africa developed CAD4Kids, an algorithm aimed at detecting pneumonia, which still carries a high mortality rate in infants worldwide. The algorithm was trained on a dataset of chest x-rays from children under five years old. The system achieved reasonable sensitivity (76 percent) and specificity (80 percent), but the analysis revealed the system was still inferior to a reference observer, the expert reported.
An international group led by Merck scientists used a World Health Organization dataset of radiographs from 431 children to develop an AI algorithm for detecting severe pneumonia in children. Schalekamp said that the algorithm reached an impressive area under the curve (AUC) of 0.977 and was within the range of interobserver reliability of the reference observers in the study.
There is also a lot of research on the detection and tracking of bronchopathy (cystic fibrosis). Researchers from the University of Bordeaux and the University of Cincinnati in Ohio developed an AI algorithm to help evaluate cystic fibrosis on CT imaging in children based on a dataset of images from patients between the ages of four and 54. “They were able to demonstrate that patients on treatment on follow-up CTs had lower amounts of specific biomarkers, which was easily picked up by this AI system,” Schalekamp said.
For him, another interesting application would be the assessment of neonatal radiographs, the grading of idiopathic respiratory stress syndrome (IRDS) in premature babies, and the detection of lines and tubes in radiographs.
Schalekamp concluded that AI in thoracic radiology is well developed, but no specific applications are available for the paediatric population and that “more research and data is needed.”
More information on the the “AI for Radology” implemention guide here.
Profile:
Dr Steven Schalekamp, PhD, of the Department of Medical Imaging at Radboud university medical center (UMC) in Nijmegen, the Netherlands.
Source: Healthcare in Europe
