Melinda Y. Chang, Gena Heidary, Shannon Beres, Stacy L. Pineles, Eric D. Gaier, Ryan Gise, Mark Reid, Kleanthis Avramidis, Mohammad Rostami, and Shrikanth Narayanan. Artificial Intelligence to Differentiate Pediatric Pseudopapilledema and True Papilledema on Fundus Photographs. Ophthalmology Science, pp. 100496, 2024.

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Abstract

PurposeTo develop and test an artificial intelligence (AI) model to aid in differentiating pediatric pseudopapilledema from true papilledema on fundus photographs.DesignMulti-center retrospective studySubjects851 fundus photographs from 235 children (age <18 years) with pseudopapilledema and true papilledemaMethodsFour pediatric neuro-ophthalmologists at four 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 tri-branch convolutional neural network. We performed 10-fold cross-validation and separately analyzed an external test set. The AI model’s performance was compared to two masked human expert pediatric neuro-ophthalmologists, who performed the same classification task.Main Outcome MeasuresAccuracy, sensitivity, and specificity of the AI model compared to human experts.ResultsThe area under receiver operating curve (AUC) 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 one child (with nearly resolved pseudotumor cerebri) had both eyes with papilledema incorrectly classified as pseudopapilledema.ConclusionsWhen classifying fundus photographs of pediatric papilledema and pseudopapilledema, our AI model achieved over 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.

BibTeX Entry

@article{CHANG2024100496,
title = {Artificial Intelligence to Differentiate Pediatric Pseudopapilledema and True Papilledema on Fundus Photographs},
journal = {Ophthalmology Science},
pages = {100496},
year = {2024},
issn = {2666-9145},
doi = {https://doi.org/10.1016/j.xops.2024.100496},
url = {https://www.sciencedirect.com/science/article/pii/S2666914524000320},
author = {Melinda Y. Chang and Gena Heidary and Shannon Beres and Stacy L. Pineles and Eric D. Gaier and Ryan Gise and Mark Reid and Kleanthis Avramidis and Mohammad Rostami and Shrikanth Narayanan},
keywords = {pediatric, papilledema, pseudopapilledema, artificial intelligence, fundus photographs},
abstract = {Purpose
To develop and test an artificial intelligence (AI) model to aid in differentiating pediatric pseudopapilledema from true papilledema on fundus photographs.
Design
Multi-center retrospective study
Subjects
851 fundus photographs from 235 children (age <18 years) with pseudopapilledema and true papilledema
Methods
Four pediatric neuro-ophthalmologists at four 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 tri-branch convolutional neural network. We performed 10-fold cross-validation and separately analyzed an external test set. The AI model’s performance was compared to two masked human expert pediatric neuro-ophthalmologists, who performed the same classification task.
Main Outcome Measures
Accuracy, sensitivity, and specificity of the AI model compared to human experts.
Results
The area under receiver operating curve (AUC) 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 one 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 over 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.}
}

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