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Ocul Oncol Pathol. 2020 Jan;6(1):58-65. doi: 10.1159/000500896. Epub 2019 Jul 15.

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

Author information

1
Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland.
2
Cornea Service, Department of Ophthalmology, University of Helsinki and Helsinki University Hospital, Helsinki, Finland.
3
Department of Women's and Children's Health, International Maternal and Child Health (IMCH), Uppsala University, Uppsala, Sweden.
4
Ophthalmic Pathology Laboratory, Department of Ophthalmology, University of Helsinki and Helsinki University Hospital, Helsinki, Finland.
5
Ophthalmic Pathology, Hospital District of Helsinki and Uusimaa Laboratory (HUSLAB), Helsinki, Finland.
6
Department of Public Health Sciences, Karolinska Institutet, Stockholm, Sweden.

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-score 81. For corneal stromal area classification, sensitivity was 74%, specificity 82%, and F-score 73. There was insufficient evidence to demonstrate correlation (Spearman's rank correlation, -0.264, p = 0.091) between RAD and visual acuity (logMAR).

Conclusions:

Deep learning algorithms can achieve a high sensitivity and specificity in pixel-level classification of amyloid and corneal stromal area. Further modeling and development of algorithms to assess earlier stages of deposition from clinical images is necessary to better assess the correlation between amyloid deposition and visual acuity. The method might be applied to corneal dystrophies as well.

KEYWORDS:

Corneal amyloidosis; Familial amyloidosis, Finnish; Gelsolin; Machine learning; Meretoja syndrome

PMID:
32002407
PMCID:
PMC6984152
[Available on 2021-01-01]
DOI:
10.1159/000500896
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Conflict of interest statement

Johan Lundin is co-founder and consultant for Aiforia Technologies Oy. The authors have no additional conflicts of interest.

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