Pilot Clinical Validation of a Machine Learning Platform for Noninvasive Smartphone-Based Assessment of Corneal Epithelial Integrity

Andrew Y. Zhang,Jayanth S. Pratap, James R. Young, Joshua Lui, Kian Attari, Arnav A. Srivastava, Erica R. Wu, Aracely D. Moreno, Annie Miall,Jay M. Iyer,Sreekar Mantena,Jay Chandra,Vineet P. Joshi,Deborah S. Jacobs

medRxiv (Cold Spring Harbor Laboratory)(2023)

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摘要
Purpose Fluorescein staining (FS) is a standard method of assessing corneal epithelium (CE) integrity. However, the equipment and personnel required for FS may be unavailable in low-resource environments. We developed and validated a low-cost, noninvasive, and quantitative CE evaluation pipeline using a custom smartphone attachment and convolutional neural networks (CNNs). Methods A 3D-printed smartphone attachment and placido disk illumination module was attached to a OnePlus 7 Pro smartphone. 26 smartphone-acquired images were obtained from 15 subjects, comprising a dataset including healthy eyes and corneal epitheliopathies of Oxford grade I-V. A classifier CNN was trained on 8 subjects (23,173 image patches) to identify areas of suspected epithelial disruption, and validated on 7 subjects (10,883 image patches). The fraction of disrupted corneal surface area (FDSA) was computed for each subject from the model output. Results were compared with FS slit lamp photos which were independently graded by two clinicians using the Oxford scheme. Results FDSA showed promise as a non-invasive marker of CE integrity, with mean FDSA in the Oxford >II cohort being higher than the Oxford ≤II cohort ( p = 0.04 and p = 0.09 using Oxford scores from each clinician, respectively). Additionally, areas of CE disruption identified by our smartphone-based technique showed qualitative concordance with those revealed by FS. Conclusions Our technique for smartphone-based CE imaging and automated analysis is a promising low-cost, noninvasive method to quantitatively evaluate the CE. Translational Relevance This tool can be used to evaluate ocular surface disease in low-resource regions. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement Funding for this study was provided by the Seva Foundation. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: The Institutional Review Board of the L.V. Prasad Eye Institute gave ethical approval for this work, and informed consent was obtained from all subjects. I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes All data produced in the present study are available upon reasonable request to the authors.
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machine learning platform,epithelial,machine learning,smartphone-based
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