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Sökning: WFRF:(Kivelä Tero)

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  • Heijl, Anders, et al. (författare)
  • Nordic research in ophthalmology.
  • 2005
  • Ingår i: Acta ophthalmologica Scandinavica. - : Wiley. - 1395-3907 .- 1600-0420. ; 83:3, s. 278-88
  • Tidskriftsartikel (övrigt vetenskapligt/konstnärligt)abstract
    • Nordic ophthalmologists and vision scientists are active in many fields of eye research. This is most evident at the biannual Nordic Congress of Ophthalmology, most recently held in Malmö in June 2004. The authors here review some of the research in vision and ophthalmology presented at this meeting or published recently by Nordic scientists. This paper does not represent a comprehensive review of all Nordic research in the field, but attempts to give an overview of some of the activities underway in eye research in this part of the world.
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  • Kessel, Klaus, et al. (författare)
  • Deep Learning Algorithms for Corneal Amyloid Deposition Quantitation in Familial Amyloidosis
  • 2020
  • Ingår i: OCULAR ONCOLOGY AND PATHOLOGY. - : KARGER. - 2296-4681 .- 2296-4657. ; 6:1, s. 58-65
  • Tidskriftsartikel (refereegranskat)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.
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