Abstract 544: Evaluating The Diagnostic Ability Of Six Different Artificial Neural Networks From The Subcellular Microenvironment To The Clinical Manifestation

Abstract

Peripheral arterial disease (PAD) is affecting a large number of patients in the USA and worldwide. The natural history of the disease leads to detrimental clinical implications including walking disability, pain and eventually limb loss. The clinical management of the disease is largely based on clinical criteria (e.g. pain, tissue loss). We have prepared muscle biopsy specimens from patients with PAD versus control subjects and utilizing electron-microscopy we captured in the subcellular level the mitochondrial architecture changes. Using the acquired data as input we tested the ability of six structurally different artificial neural networks (ANNs) to identify PAD patients. For the study we acquired human tissue biopsies from the gastrocnemius muscle. The specimens were processed and cut as ultrathin sections (70 nm) for analysis using a JEM1010 transmission electron microscope. The images were used to generate an image preprocessing database of 992 images and subsequently a custom python-based code was developed with Keras - TensorFlow platforms in order to perform the ANN analysis. We implementing six different deep artificial neural network architectures namely, VGG-19 , ResNet50 , Densenet201 , InceptionV3 , InceptionsResnetV2 and EfficientNetB7.The diagnostic accuracy was found to be respectively: VGG-19 0.81%, ResNet50 0.75%, Densenet201 0.81%, InceptionV3 0.79%, InceptionsResnetV2 0.78% and EfficientNetB7 0.87%.This study presents data for the ability of simple and more advanced ANNs to perform diagnostic predictions based on electron microscopy images of intracellular organelles. The ability to take advantage of applied artificial intelligence technologies and serve this information for clinical decision making could be a pathway for improved patient care. The presented work established insight in some of the currently available deep artificial neural networks, applied in a daily clinical problem and their ability to harness biological information. Our goal is to stimulate the development of higher computational capacity artificial intelligence systems in order to assist clinicians and support improved guidelines geared towards individualized precision medicine.