574 A deep learning classifier for the analysis of tiger tail banding in trichothiodystrophy

Abstract

Trichothiodystrophy (TTD) and xeroderma pigmentosum (XP) are both rare, autosomal recessive disorders of DNA repair/ transcription genes. Despite having (different) mutations in the same genes, they have markedly different phenotypes. TTD patients have multisystem developmental abnormalities involving fetal growth, and brain, eyes, bones and immune system with normal cancer risk. XP patients have normal development and a greatly increased risk of sunlight induced skin cancer. The hallmark of TTD hair is an alternating dark and light “tiger tail” banding pattern under polarized light microscopy along with defects of the hair shaft and reduced content of sulfur containing amino acids. XP patients have normal hair. Some XP/TTD complex patients may have features of both disorders making the diagnosis and evaluation of cancer risk difficult. Here we present a U-Net convolutional neural network for segmentation and classification of tiger tail hair and normal hair images from polarized light microscopy. The model was trained with 35 TTD and 35 normal hair images. The dataset was augmented using standard crops, flips, and rotations. Shears were excluded to preserve the banded features. In future studies, a third class consisting of hair images from XP/TTD complex patients will be introduced to the U-Net to evaluate the differences in banding patterns. Use of artificial intelligence deep learning may be able to assist in diagnosis of patients with different types of hair abnormalities.