Speaker
Description
In addition to genetic testing, current diagnostic practices for rare neuromuscular diseases involve muscle biopsy analysis, often hindered by subjective interpretation and variability. Advances in artificial intelligence, particularly deep learning, offer promising solutions to automate and enhance diagnostic accuracy by identifying new quantitative, standardized features and phenotypic expressions within biopsy images. These features can be extracted from neural networks using explainable AI tools.
Backpropagation is the gold standard for training Convolutional Neural Networks (CNNs). However, its forward and backward passes are not biologically plausible given the data flow in the human brain. In December 2022, Prof. Geoffrey Hinton, one of its inventors, proposed an alternative: the Forward-Forward (FF) algorithm, which avoids backward passes by using two forward passes, preventing previous hidden layers from receiving information from subsequent ones. In 2023, we were the first to implement this algorithm on CNNs.
In this study, we applied FF-trained CNNs to analyze multiphoton microscopy images of muscle biopsies from patients with rare neuromuscular diseases, specifically Duchenne Muscular Dystrophy (DMD). The images included autofluorescence, second harmonic generation, and third harmonic generation signals (SLAM approach).
We compared the performance of the FF algorithm with backpropagation on the same CNN, reaching 91% and 98% of accuracy on the test dataset, respectively. Using explainable AI tools, specifically class activation maps, we revealed the decision-making processes of the CNNs, showing that FF and backpropagation use different image properties for DMD diagnosis. We validated and then integrated these features by introducing an attention metric, providing a unique and standardized value to quantify pathological characteristics for immediate clinical use.
Our findings demonstrate the potential of the FF algorithm in delivering new, reliable and interpretable diagnostic insights from biomedical images. This paves the way for its parallel integration with backpropagation-based networks into clinical workflows, enhancing state-of-the-art analysis pipelines by more efficiently exploiting the complex information content available from biological images.
