A deep learning model for diagnosis of inherited retinal diseases
Bibliographic record
Abstract
To evaluate the performance of a multi-input deep learning (DL) model in detecting two common inherited retinal diseases (IRDs), i.e. retinitis pigmentosa (RP) and Stargardt disease (STGD), and differentiating them from healthy eyes. This cross-sectional study includes 391 cases, consisting of 158 subjects with RP, 62 patients with STGD, and 171 healthy individuals. The image dataset is publicly available at http://en.riovs.sbmu.ac.ir/Access-to-Inherited-Retinal-Diseases-Image-Bank . Separate networks using the same hyperparameters were trained and tested on the dataset. Two single-input MobileNetV2 networks were employed for color fundus photography (CFP) and infrared (IR) images, and a multi-input MobileNetV2 network was applied using both imaging modalities simultaneously. The single-input MobileNetV2 achieved 94.44% diagnostic accuracy using CFP, and 94.44% accuracy employing IR images, respectively. The multi-input MobileNetV2 network outperformed both single-input networks with an accuracy of 96.3%. The impact of single-input and multi-input architectures was further evaluated on state-of-the-art neural network models and machine learning algorithms. The deep learning networks utilized in this study achieved high performance for detection of IRDs. Application of a multi-input network employing both CFP and IR image inputs improves the overall performance of the model and its diagnostic accuracy.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".