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Deep Learning Algorithms for Corneal Amyloid Deposition Quantitation in Familial Amyloidosis
Univ Helsinki, HiLIFE, Inst Mol Med Finland FIMM, Helsinki, Finland.
Univ Helsinki, Dept Ophthalmol, Cornea Serv, Helsinki, Finland;Helsinki Univ Hosp, Helsinki, Finland.
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Women's and Children's Health, International Maternal and Child Health (IMCH). Univ Helsinki, HiLIFE, Inst Mol Med Finland FIMM, Helsinki, Finland.
Helsinki Univ Hosp, Helsinki, Finland;Univ Helsinki, Dept Ophthalmol, Ophthalm Pathol Lab, Helsinki, Finland;Hosp Dist Helsinki, Ophthalm Pathol, Helsinki, Finland;Uusimaa Lab HUSLAB, Helsinki, Finland.
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2020 (English)In: OCULAR ONCOLOGY AND PATHOLOGY, ISSN 2296-4681, Vol. 6, no 1, p. 58-65Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
KARGER , 2020. Vol. 6, no 1, p. 58-65
Keywords [en]
Familial amyloidosis, Finnish, Meretoja syndrome, Corneal amyloidosis, Gelsolin, Machine learning
National Category
Ophthalmology
Identifiers
URN: urn:nbn:se:uu:diva-405353DOI: 10.1159/000500896ISI: 000507874200012PubMedID: 32002407OAI: oai:DiVA.org:uu-405353DiVA, id: diva2:1404162
Available from: 2020-02-28 Created: 2020-02-28 Last updated: 2020-02-28Bibliographically approved

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