This study evaluates the ability of deep learning to improve the knowledge about structural and vascular retinal changes in Waldenström's Macroglobulinemia patients, using optical coherence tomography angiography.

Waldenstrom macroglobulinemia (mak-roe-glob-u-lih-NEE-me-uh) is a rare type of cancer that begins in the white blood cells.

The optical coherence tomography angiography represents a novel and noninvasive diagnostic technique that allows a detailed and quantitative analysis of retinal and choriocapillary vascular features. The study evaluates the changes in optical coherence tomography angiography features in Waldenstrom macroglobulinemia, elaborating these data with artificial intelligence.

Inclusion Criteria:

• age older than 18 years

  • diagnosis of Waldenstrom Macroglobulinemia
  • absence of previous ocular surgery and congenital eye diseases.
  • absence of errors of refraction
  • absence of lens opacities
  • absence of low-quality OCT and OCTA images

Exclusion Criteria:

• • age younger than 18 years

  • previous ocular surgery and congenital eye diseases
  • errors of refraction
  • lens opacities
  • low-quality OCT and OCTA images

The participans were older than 18 years with diagnosis of Waldenstrom Macroglobulinemia. They did not present other ophthalmological disease