Research Note: Prospects for early detection of breast muscle myopathies by automated image analysis

Jonathan Dayan, Noam Goldman, Orna Halevy, Zehava Uni*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

White Striping (WS), Wooden Breast (WB), and Spaghetti Meat (SM) are documented breast muscle myopathies (BMM) affecting broiler chickens’ product quality, profitability and welfare. This study evaluated the efficacy of our newly developed deep learning-based automated image analysis tool for early detection of morphometric parameters related to BMM in broiler chickens. Male chicks were utilized, and muscle samples were collected on d 14 of rearing. Histological procedures, including microscopic scoring, blood vessel count, and collagen quantification, were conducted. A previous study demonstrated our automated image analysis as a reliable tool for evaluating myofiber size, conforming with manual histological measurements. A threshold for BMM detection was established by normalizing and consolidating myofiber diameter and area into a unified metric based on automated measurements, also termed as “relative myofiber size value.” Results show that severe myopathy broilers consistently exhibited lower relative myofiber size values, effectively detecting myopathy severity. Our study, aimed as proof of concept, underscores the potential of our automated image analysis tool as an early detection method for BMM.

Original languageAmerican English
Article number103680
JournalPoultry Science
Volume103
Issue number6
DOIs
StatePublished - Jun 2024

Bibliographical note

Publisher Copyright:
© 2024 The Authors

Keywords

  • automated myopathy detection
  • breast muscle
  • broiler chicken
  • histology
  • image analysis

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