Assessment and diagnosis of multiple sclerosis using optical coherence tomography with the help of artificial intelligence and convolutional neural networks in Python

Introduction: Multiple Sclerosis (MS) is a prevalent autoimmune and inflammatory disorder that leaves demyelination and neurodegenerative changes in Central Nervous System (CNS). The retina is among the body organs that are affected by MS, Optical Coherence Tomography (OCT) images can play a key role in the preliminary stages. Artificial intelligence-based methods are commonly applied in image classification and have shown promising and applicable results in MS diagnosis.Method: In total, more than 197 MS patients and more than 283 healthy patients were included in this study, and Spectralis OCT images were taken, and artificial intelligence was trained with several thousand images using data augmentation. Finally, the automatic diagnosis algorithm of MS disease was implemented, and then the diagram of network loss processes was drawn and the sensitivity, specificity and accuracy of the algorithm were evaluated.Result: The disease was successfully diagnosed by OCT images with an accuracy of 93.0. Conclusion: The proposed method showed improvements in early-stage MS diagnosis and with the potentiality to be used in either the diagnosis or prediction of the progression of other diseases that affect the CNS (e.g. Alzheimer's disease, bipolar disorder, etc.).

Assessment and diagnosis of multiple sclerosis using optical coherence tomography with the help of artificial intelligence and convolutional neural networks in Python Keywords:

convolutional neural network , multiple sclerosis , optical coherence tomography , retinal layer thickness