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Artificial Intelligence for Early Detection of Pediatric Eye Diseases Using Mobile Photos
Qin Shu1,2, Jiali Pang3, Zijia Liu4
1Department of Ophthalmology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Insights
An artificial intelligence (AI) model can accurately detect pediatric eye conditions like myopia, strabismus, and ptosis using smartphone images. This technology offers a convenient way for early diagnosis of eye diseases in children at home.
Area of Science:
- Ophthalmology
- Artificial Intelligence
- Medical Imaging
Background:
- Early identification of pediatric eye diseases is crucial but traditional methods are costly and time-consuming.
- Mobile photography offers a potential avenue for accessible, at-home screening of children's eye conditions.
Purpose of the Study:
- To develop and validate an artificial intelligence (AI) model for identifying myopia, strabismus, and ptosis in children using mobile photographs.
Main Methods:
- A deep learning model was developed using 1419 images from 476 pediatric patients.
- Model performance was evaluated using sensitivity, specificity, accuracy, AUC, PPV, NPV, and F1-score.
- GradCAM++ was used for visual analysis, with subgroup analyses for sex and age to assess generalizability.
Main Results:
- The AI model demonstrated good sensitivity for myopia (0.84), strabismus (0.73), and ptosis (0.85).
- Performance was comparable between sexes, but varied across different age groups.
- The model achieved high accuracy in identifying these common pediatric eye conditions.
Conclusions:
- An AI model utilizing smartphone images can effectively identify myopia, strabismus, and ptosis in children.
- This AI-driven approach shows promise for facilitating early and convenient detection of pediatric eye diseases in a home setting.
Importance:
Identifying pediatric eye diseases at an early stage is a worldwide issue. Traditional screening procedures depend on hospitals and ophthalmologists, which are expensive and time-consuming. Using artificial intelligence (AI) to assess children's eye conditions from mobile photographs could facilitate convenient and early identification of eye disorders in a home setting.
Objective:
To develop an AI model to identify myopia, strabismus, and ptosis using mobile photographs.
Design, Setting, And Participants:
This cross-sectional study was conducted at the Department of Ophthalmology of Shanghai Ninth People's Hospital from October 1, 2022, to September 30, 2023, and included children who were diagnosed with myopia, strabismus, or ptosis.
Main Outcomes And Measures:
A deep learning-based model was developed to identify myopia, strabismus, and ptosis. The performance of the model was assessed using sensitivity, specificity, accuracy, the area under the curve (AUC), positive predictive values (PPV), negative predictive values (NPV), positive likelihood ratios (P-LR), negative likelihood ratios (N-LR), and the F1-score. GradCAM++ was utilized to visually and analytically assess the impact of each region on the model. A sex subgroup analysis and an age subgroup analysis were performed to validate the model's generalizability.
Results:
A total of 1419 images obtained from 476 patients (225 female [47.27%]; 299 [62.82%] aged between 6 and 12 years) were used to build the model. Among them, 946 monocular images were used to identify myopia and ptosis, and 473 binocular images were used to identify strabismus. The model demonstrated good sensitivity in detecting myopia (0.84 [95% CI, 0.82-0.87]), strabismus (0.73 [95% CI, 0.70-0.77]), and ptosis (0.85 [95% CI, 0.82-0.87]). The model showed comparable performance in identifying eye disorders in both female and male children during sex subgroup analysis. There were differences in identifying eye disorders among different age subgroups.
Conclusions And Relevance:
In this cross-sectional study, the AI model demonstrated strong performance in accurately identifying myopia, strabismus, and ptosis using only smartphone images. These results suggest that such a model could facilitate the early detection of pediatric eye diseases in a convenient manner at home.
