Pedram Hamrah
Pedram Hamrah is a clinician-scientist focusing his research on Neuropathic Corneal Pain (NCP),
Title: Diagnosis of neuropathic corneal pain: Utility of artificial intelligence for assessment of a novel biomarker via in vivo confocal microscopy
Biography
Biography: Pedram Hamrah
Abstract
Statement of the problem: The diagnosis of Neuropathic Corneal Pain (NCP) is challenging, as it is often difficult to differentiate from conventional Dry Eye Disease (DED). We have recently identified a potential novel biomarker by corneal In Vivo Confocal Microscopy (IVCM), utilizing morphological nerve changes [Figure 1]. The purpose of this study is to describe analytical and biological validation of this biomarker and to develop utilize artificial intelligence for fully automated analysis of images in a rapid and consistent fashion.
Methodology & theoretical orientation: A database of 500,000 IVCM images was used to confirm that the presence of micro-neuromas is a biomarker for NCP by comparing the sensitivity and specificity of identification of NCP patients via micro-neuromas to other IVCM parameters. Inter and intra-observer precision was assessed and descriptive statistics of the IVCM datasets was performed to determine the minimum number of images necessary for high precision of micro-neuroma detection. Biological validation of micro-neuromas was then performed, correlating IVCM results to clinical. An Artificial Intelligence (AI) program was developed and validated for automated identification of micro-neuromas to allow rapid and wide-scale adoption by clinicians.
Findings: Analytical validation confirmed that the presence of micro-neuromas was a biomarker distinguishing NCP from DED, with good inter and intra-observer precision. The AI system program had high sensitivity and specificity. Our model showed excellent discriminative ability to detect micro-neuromas (AuROC: 0.97) and the ability to generalize to data from a new institution (AuROC: 0.90).
Conclusion: The AI system had a very high AUC for detecting micro-neuromas. The deep neural network shows great promise in identifying micro-neuromas associated with NCP, allowing for the standardization of IVCM image analysis. Our study suggests that artificial intelligence can rapidly evaluate IVCM images, while maintaining a high degree of accuracy.