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Amputation Prediction with Pedal Angiograms

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How to use: Upload the pedal angiogram (DSA) in sagittal view to predict the probability of amputation.The model utilized fully anonymized angiograms of peripheral artery disease, comprising salvaged limbs and major amputations. Additionally, a bias dataset with random non-angiographic images was included.

Uploaded Image
Author: Yury Rusinovich
Email: info@mlhs.ink
Deployment Date: 2024-07-07 14:07:00
References:
Performance and Training Settings:
Validation Accuracy: 0.9791666865348816
Validation Loss: 0.04968664422631264
Training Accuracy: 0.990338146686554
Training Loss: 0.033271677792072296
Class Counts (n): 232, 318, 422
Outcome: Amputation, Salvage, bias
Number of Epochs: 45
Initial Learning Rate: 0.001
Batch Size: 16
Image Augmentation:


Support this research. Web3 Wallet of the Author: 0x481985ac0538fabfc5cec6ad7ff29d4036ec013f


This Machine Learning model could be downloaded and retrained by adding new data on the ML in Health Science Platform in the Sustainable Image Classifier


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