Standardization of backfat thickness measurements across A-mode and B-mode ultrasound systems in pigs
Received: May 19, 2026; Revised: Jul 05, 2026; Accepted: Aug 23, 2026
Published Online: Sep 29, 2026
Abstract
Backfat thickness is a key carcass trait used for economic valuation and genetic selection in pigs. However, field datasets are often generated using heterogeneous ultrasound devices, making direct comparison and integration difficult. This study evaluated the practical compatibility between an A-mode ultrasound device (PIGLOG 105) and a B-mode ultrasound device (ExaGo) for backfat thickness measurement and developed calibration/conversion models to standardize measurements to carcass reference scales. A total of 69 Yorkshire pigs from a domestic GGP farm were evaluated during July–August 2024. A-mode ultrasound measurements were collected 1–2 days prior to harvest at the 10–11th thoracic region. B-mode images were acquired at the identical location in two scan orientations: vertical (cross-sectional) and parallel (longitudinal). Images were stored in JPEG format and quantified using both Python-based analysis and ImageJ. Reference measurements included manual backfat thickness on hot carcasses (immediately post-harvest) and cold carcasses (after 24h chilling), VCS2000 outputs, and a direct measurement on the loin cut surface at the same anatomical level. Associations were assessed using Pearson correlation and simple linear regression with a zero intercept, and systematic bias was tested using paired t-tests. B-mode measurements showed strong linear associations with reference values (R²≈0.94~0.98), with vertical scanning generally outperforming parallel scanning; the best B-mode performance was observed for Python-vertical vs cold carcass (R²=0.984). Despite high linearity, paired comparisons indicated significant systematic bias for VCS2000 and carcass references (p<0.001), implying that raw B-mode values require calibration before pooling with reference-scale data. A-mode values were stably convertible to B-mode across four combinations (Python/ImageJ × vertical/parallel; R²=0.943~0.98, RMSE = 0.233~0.362). Direct A-mode prediction of reference measurements also demonstrated high explanatory power (R²=0.947~0.97; RMSE=0.301~0.6), but raw A-mode differed significantly from all references (mean differences 0.276~1.245cm; p<0.001). After applying regression-based calibration, mean differences were no longer significant (p=0.142~0.528). In conclusion, A-mode and B-mode are not directly interchangeable as raw data, but become compatible after applying the proposed conversion and calibration models, enabling practical standardization of field ultrasound measurements to carcass reference scales.
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