Automated assessment of cardiac morphological variation in Atlantic salmon (Salmo salar L.)

Lisa-Victoria Bernhardt, Andreas Hafver, Nafiha Usman, Edward Yi Liu, Jørgen Andreas Åm Vatn, André Ødegårdstuen, Heidi S. Mortensen, Ida Breitnes Johansen

Research output: Contribution to journalArticlepeer-review


Deviating heart shapes and poor cardiac health is a recurring concern in farmed Atlantic salmon. Morphometric
analysis has so far improved our understanding of salmonid cardiac morphology, but assessment of morphological cardiac variation is usually performed manually through measurements of lengths, ratios, and angles.
Manual assessment of heart shape is tedious, time-consuming, and not very standardized. It also requires training
and alignment of personnel to achieve reliable results. Considering these challenges, we aimed to automate this
process using a deep learning model for computer vision to measure the morphological variations of the heart.
Here we developed an algorithm for a diagnostic tool to detect variation in cardiac morphology in farmed
Atlantic salmon, which we believe can assess cardiac morphological variation in a more objective, reproducible,
and reliable manner compared to the manual process. The knowledge derived from this study may represent a
crucial step in comprehending and eventually reducing cardiac abnormalities in farmed salmonids, which is
essential for improving fish health and welfare and ensuring aquaculture's sustainable growth.
Original languageEnglish
Article number741145
Number of pages11
Publication statusPublished - 28 May 2024


  • Computer vision
  • Aquaculture
  • Morphometrics
  • Cardiac morphology
  • Atlantic salmon (Salmo salar L.)


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