Deep Learning Algorithms for Corneal Amyloid Deposition Quantitation in Familial Amyloidosis.

ABSTRACT:

Objectives

The aim of this study was to train and validate deep learning algorithms to quantitate relative amyloid deposition (RAD; mean amyloid deposited area per stromal area) in corneal sections from patients with familial amyloidosis, Finnish (FAF), and assess its relationship with visual acuity.

Methods

Corneal specimens were obtained from 42 patients undergoing penetrating keratoplasty, stained with Congo red, and digitally scanned. Areas of amyloid deposits and areas of stromal tissue were labeled on a pixel level for training and validation. The algorithms were used to quantify RAD in each cornea, and the association of RAD with visual acuity was assessed.

Results

In the validation of the amyloid area classification, sensitivity was 86%, specificity 92%, and F

SUBMITTER: Kessel K 

PROVIDER: S-EPMC6984152 | biostudies-literature | 2020 Jan

REPOSITORIES: biostudies-literature