RT Journal Article
SR Electronic
T1 Combining In Vivo Corneal Confocal Microscopy with Deep Learning-based Analysis Reveals Sensory Nerve Fiber Loss in Acute SIV Infection
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2020.04.19.048926
DO 10.1101/2020.04.19.048926
A1 Megan E. McCarron
A1 Rachel L. Weinberg
A1 Jessica M. Izzi
A1 Suzanne E. Queen
A1 Stuti L. Misra
A1 Daniel B. Russakoff
A1 Jonathan D. Oakley
A1 Joseph L. Mankowski
YR 2020
UL http://biorxiv.org/content/early/2020/04/20/2020.04.19.048926.abstract
AB Purpose To characterize corneal subbasal nerve plexus morphologic features using in vivo corneal confocal microscopy (IVCM) in normal and SIV-infected macaques and to implement automated assessments using novel deep learning-based methods customized for macaque studies.Methods In vivo corneal confocal microscopy images were collected from both male and female age-matched specific-pathogen free rhesus and pigtailed macaques housed at the Johns Hopkins University breeding colony using the Heidelberg HRTIII with Rostock Corneal Module. We also obtained repeat IVCM images of 12 SIV-infected animals including pre-infection and 10 day post-SIV infection time-points. All IVCM images were analyzed using a novel deep convolutional neural network architecture developed specifically for macaque studies.Results Deep learning-based segmentation of subbasal nerves in IVCM images from macaques demonstrated that corneal nerve fiber length (CNFL) and fractal dimension measurements did not differ between species, but pigtailed macaques had significantly higher baseline corneal nerve fiber tortuosity than rhesus macaques (P = 0.005). Neither sex nor age of macaques was associated with differences in any of the assessed corneal subbasal nerve parameters. In the SIV/macaque model of HIV, acute SIV infection induced significant decreases in both corneal nerve fiber length and fractal dimension (P= 0.01 and P= 0.008 respectively).Conclusions The combination of IVCM and objective, robust, and rapid deep-learning analysis serves as a powerful noninvasive research and clinical tool to track sensory nerve damage, enabling early detection of neuropathy. Adapting the deep-learning analyses to human corneal nerve assessments will refine our ability to predict and monitor damage to small sensory nerve fibers in a number of clinical settings including HIV, multiple sclerosis, Parkinson’s disease, diabetes, and chemotherapeutic neurotoxicity.Competing Interest StatementThe authors have declared no competing interest.