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A deep convolutional neural network for Kawasaki disease diagnosis
Ellen Xu1, Shamim Nemati2, Adriana H Tremoulet3
1Department of Pediatrics, University of California San Diego and Rady Children's Hospital, San Diego, CA, USA.
Insights
A new AI tool, KD-CNN, can identify Kawasaki disease (KD) from photographs, aiding early diagnosis. This technology helps differentiate KD from other pediatric illnesses, potentially reducing severe heart complications.
Area of Science:
- Pediatric Cardiology
- Artificial Intelligence in Medicine
- Medical Imaging Analysis
Background:
- Kawasaki disease (KD) is a leading cause of acquired heart disease in children.
- KD diagnosis is challenging due to overlapping symptoms with other pediatric conditions, risking delayed treatment and severe outcomes like myocardial infarction.
- Currently, KD diagnosis relies on clinical evaluation as no definitive diagnostic test exists.
Purpose of the Study:
- To develop and evaluate a deep convolutional neural network (KD-CNN) for differentiating clinical signs of KD from other pediatric illnesses using photographs.
- To assess the performance of KD-CNN in distinguishing between children with and without clinical manifestations of KD.
Main Methods:
- Utilized a hybrid approach for dataset creation, combining crowdsourced images and publicly available internet data.
- Employed transfer learning by pretraining a VGG-16 model and subsequently fine-tuning it on the KD dataset.
- Explored data augmentation techniques to enhance model performance and generalizability with limited data.
Main Results:
- KD-CNN achieved a median Area Under the Curve (AUC) of 0.90 (IQR 0.10) across tenfold cross-validation.
- The model demonstrated a median sensitivity of 0.80 (IQR 0.18) and a median specificity of 0.85 (IQR 0.19).
- These results indicate a strong ability to distinguish between children with and without clinical signs of KD.
Conclusions:
- KD-CNN represents a novel application of convolutional neural networks in medical diagnostics.
- The AI tool shows significant potential to assist clinicians in the early and accurate differentiation of Kawasaki disease.
- Successful implementation of KD-CNN could lead to reduced morbidity and mortality associated with KD.
Abstract:
Kawasaki disease (KD), the most common cause of acquired heart disease in children, can be easily missed as it shares clinical findings with other pediatric illnesses, leading to risk of myocardial infarction or death. KD remains a clinical diagnosis for which there is no diagnostic test, yet there are classic findings on exam that can be captured in a photograph. This study aimed to develop a deep convolutional neural network, KD-CNN, to differentiate photographs of KD clinical signs from those of other pediatric illnesses. To create the dataset, we used an innovative combination of crowdsourcing images and downloading from public domains on the Internet. KD-CNN was then pretrained using transfer learning from VGG-16 and fine-tuned on the KD dataset, and methods to compensate for limited data were explored to improve model performance and generalizability. KD-CNN achieved a median AUC of 0.90 (IQR 0.10 from tenfold cross validation), with a sensitivity of 0.80 (IQR 0.18) and specificity of 0.85 (IQR 0.19) to distinguish between children with and without clinical manifestations of KD. KD-CNN is a novel application of CNN in medicine, with the potential to assist clinicians in differentiating KD from other pediatric illnesses and thus reduce KD morbidity and mortality.
