Deep learning-based algorithm for the detection of idiopathic full thickness macular holes in spectral domain optical coherence tomography

Abstract Background Automated identification of spectral domain optical coherence tomography (SD-OCT) features can improve retina clinic workflow efficiency as they are able to detect pathologic findings. The purpose of this study was to test a deep learning (DL)-based algorithm for the identification of Idiopathic Full Thickness Macular Hole (IFTMH) features and stages of severity in SD-OCT B-scans. Methods In this cross-sectional study, subjects solely diagnosed with either IFTMH or Posterior Vitreous Detachment (PVD) were identified excluding secondary causes of macular holes, any concurrent maculopathies, or incomplete records. SD-OCT scans (512 × 128) from all subjects were acquired with CIRRUS™ HD-OCT (ZEISS, Dublin, CA) and reviewed for quality. In order to establish a ground truth classification, each SD-OCT B-scan was labeled by two trained graders and adjudicated by a retina specialist when applicable. Two test sets were built based on different gold-standard classification methods. The sensitivity, specificity and accuracy of the algorithm to identify IFTMH features in SD-OCT B-scans were determined. Spearman’s correlation was run to examine if the algorithm’s probability score was associated with the severity stages of IFTMH. Results Six hundred and one SD-OCT cube scans from 601 subjects (299 with IFTMH and 302 with PVD) were used. A total of 76,928 individual SD-OCT B-scans were labeled gradable by the algorithm and yielded an accuracy of 88.5% (test set 1, 33,024 B-scans) and 91.4% (test set 2, 43,904 B-scans) in identifying SD-OCT features of IFTMHs. A Spearman’s correlation coefficient of 0.15 was achieved between the algorithm’s probability score and the stages of the 299 (47 [15.7%] stage 2, 56 [18.7%] stage 3 and 196 [65.6%] stage 4) IFTMHs cubes studied. Conclusions The DL-based algorithm was able to accurately detect IFTMHs features on individual SD-OCT B-scans in both test sets. However, there was a low correlation between the algorithm’s probability score and IFTMH severity stages. The algorithm may serve as a clinical decision support tool that assists with the identification of IFTMHs. Further training is necessary for the algorithm to identify stages of IFTMHs

Biallelic loss of human CTNNA2, encoding αN-catenin, leads to ARP2/3 complex overactivity and disordered cortical neuronal migration.

Neuronal migration defects, including pachygyria, are among the most severe developmental brain defects in humans. Here, we identify biallelic truncating mutations in CTNNA2, encoding αN-catenin, in patients with a distinct recessive form of pachygyria. CTNNA2 was expressed in human cerebral cortex, and its loss in neurons led to defects in neurite stability and migration. The αN-catenin paralog, αE-catenin, acts as a switch regulating the balance between β-catenin and Arp2/3 actin filament activities1. Loss of αN-catenin did not affect β-catenin signaling, but recombinant αN-catenin interacted with purified actin and repressed ARP2/3 actin-branching activity. The actin-binding domain of αN-catenin or ARP2/3 inhibitors rescued the neuronal phenotype associated with CTNNA2 loss, suggesting ARP2/3 de-repression as a potential disease mechanism. Our findings identify CTNNA2 as the first catenin family member with biallelic mutations in humans, causing a new pachygyria syndrome linked to actin regulation, and uncover a key factor involved in ARP2/3 repression in neurons.11sciescopu