Automated color Doppler ultrasound analysis of bull reproductive tissues using a machine learning-based image processing algorithm

Color Doppler ultrasound is effective for studying tissue perfusion of various organs, but current analysis methods are subjective and time-consuming. This study aims to develop and validate an algorithm for analyzing color Doppler images of the bull's testis and pampiniform plexus. For the stu...

Full description

Saved in:
Bibliographic Details
Published in:Animal reproduction science Vol. 281; p. 107997
Main Authors: Gonçalves, Joedson Dantas, Guimarães, Edilson, Arruda, Rubens Paes, Oliveira, Maria Emilia Franco, Arikawa, Leonardo Machestropa, Garcia, Alexandre Rossetto
Format: Journal Article
Language:English
Published: Netherlands Elsevier B.V 01.10.2025
Subjects:
Algorithms
Animals
Artificial intelligence
Automated analysis
Cattle - anatomy & histology
Diagnostic imaging
Doppler ultrasound
Image Processing, Computer-Assisted - methods
Machine Learning
Male
Testis - blood supply
Testis - diagnostic imaging
Ultrasonography, Doppler, Color - methods
Ultrasonography, Doppler, Color - veterinary
Vascular function
Veterinary imaging
Automated analysis
Diagnostic imaging
Artificial intelligence
Doppler ultrasound
Vascular function
Veterinary imaging
ISSN:0378-4320, 1873-2232, 1873-2232
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Color Doppler ultrasound is effective for studying tissue perfusion of various organs, but current analysis methods are subjective and time-consuming. This study aims to develop and validate an algorithm for analyzing color Doppler images of the bull's testis and pampiniform plexus. For the study, we selected 2304 color Doppler images (1152 for both the testicular parenchyma and the pampiniform plexus) that were analyzed by a conventional method (CON Group), by pixel separation and counting using Adobe Fireworks® and ImageJ®, or by an algorithm developed in Python version 3.10 (ALGO Group) that can be set to analyze up to 35 variables simultaneously. The processing speed for the ALGO Group was 270 images/0.14 sec. The coefficients of determination (R²) for the variables analyzed by the conventional method and the algorithm were found to be considerably high (0.84–0.97, p < 0.001 for testicular parenchyma images; 0.97–0.99, p < 0.001 for pampiniform plexus). The high correlations indicate that the algorithm produces results consistent with the conventional method, demonstrating its reliability. The Pearson correlation coefficients between the conventional analyses and the algorithm were significant (0.92–0.98, p < 0.001 for testicular parenchyma images; 0.98–0.99, p < 0.001 for pampiniform plexus). In addition, Bland-Altman concordance analyses showed that most points fell within the 95 % confidence interval for both techniques in the organs evaluated. Given the strong correlations demonstrated, the reduced processing time, and the reliability of the results, it can be concluded that this algorithmic approach can effectively replace conventional methods for assessing vascular function. •An algorithm for tissue perfusion analysis was developed and compared to the visual method.•The algorithm showed an extremely high correlation with the conventional method.•Up to 35 variables can be analyzed through algorithm pixel counting.•The algorithm increased color Doppler processing speed to 270 images in 0.14 s.•The algorithm can safely replace traditional color Doppler image analysis.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
ISSN:0378-4320
1873-2232
1873-2232
DOI:10.1016/j.anireprosci.2025.107997