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Artificial Intelligence to Differentiate Pediatric Pseudopapilledema and True Papilledema on Fundus Photographs
Melinda Y Chang1,2, Gena Heidary3,4, Shannon Beres5
1Division of Ophthalmology, Children's Hospital Los Angeles, Los Angeles, California.
Insights
An artificial intelligence (AI) model demonstrated high sensitivity in detecting pediatric papilledema from fundus photographs, outperforming human experts. This AI tool shows promise for triaging children needing further evaluation for serious neurological conditions.
Area of Science:
- Ophthalmology
- Artificial Intelligence
- Medical Imaging
Background:
- Differentiating true papilledema from pseudopapilledema in children is crucial for timely diagnosis and management of serious underlying conditions.
- Fundus photography is a key diagnostic tool, but interpretation can be challenging.
Purpose of the Study:
- To develop and validate an artificial intelligence (AI) model for distinguishing pediatric pseudopapilledema from true papilledema using fundus photographs.
Main Methods:
- A retrospective study involving 851 fundus photographs from 235 children.
- An AI model utilizing a DenseNet backbone and a tribranch convolutional neural network was trained and validated.
- Model performance was compared against two masked pediatric neuro-ophthalmologists using 10-fold cross-validation and an external test set.
Main Results:
- The AI model achieved an area under the receiver operating curve of 0.81 on the external test set.
- AI model sensitivity was 90.4%, significantly higher than human experts (P = 0.0002).
- AI demonstrated superior sensitivity in detecting mild papilledema compared to human experts.
Conclusions:
- The AI model shows high sensitivity (>90%) in detecting pediatric papilledema, exceeding human expert performance.
- The AI's high sensitivity and low false-negative rate suggest its utility in triaging children with suspected papilledema.
- This AI tool can aid in identifying children who require further work-up for potentially serious neurological conditions.
Purpose:
To develop and test an artificial intelligence (AI) model to aid in differentiating pediatric pseudopapilledema from true papilledema on fundus photographs.
Design:
Multicenter retrospective study.
Subjects:
A total of 851 fundus photographs from 235 children (age < 18 years) with pseudopapilledema and true papilledema.
Methods:
Four pediatric neuro-ophthalmologists at 4 different institutions contributed fundus photographs of children with confirmed diagnoses of papilledema or pseudopapilledema. An AI model to classify fundus photographs as papilledema or pseudopapilledema was developed using a DenseNet backbone and a tribranch convolutional neural network. We performed 10-fold cross-validation and separately analyzed an external test set. The AI model's performance was compared with 2 masked human expert pediatric neuro-ophthalmologists, who performed the same classification task.
Main Outcome Measures:
Accuracy, sensitivity, and specificity of the AI model compared with human experts.
Results:
The area under receiver operating curve of the AI model was 0.77 for the cross-validation set and 0.81 for the external test set. The accuracy of the AI model was 70.0% for the cross-validation set and 73.9% for the external test set. The sensitivity of the AI model was 73.4% for the cross-validation set and 90.4% for the external test set. The AI model's accuracy was significantly higher than human experts on the cross validation set (P < 0.002), and the model's sensitivity was significantly higher on the external test set (P = 0.0002). The specificity of the AI model and human experts was similar (56.4%-67.3%). Moreover, the AI model was significantly more sensitive at detecting mild papilledema than human experts, whereas AI and humans performed similarly on photographs of moderate-to-severe papilledema. On review of the external test set, only 1 child (with nearly resolved pseudotumor cerebri) had both eyes with papilledema incorrectly classified as pseudopapilledema.
Conclusions:
When classifying fundus photographs of pediatric papilledema and pseudopapilledema, our AI model achieved > 90% sensitivity at detecting papilledema, superior to human experts. Due to the high sensitivity and low false negative rate, AI may be useful to triage children with suspected papilledema requiring work-up to evaluate for serious underlying neurologic conditions.
Financial Disclosures:
Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.
