Artificial intelligence in animal anatomy: Exploring the technologies, applications, benefits, and challenges

Area of Science:

• Veterinary anatomy
• Digital anatomy
• Computational biology

Background:

• Digital techniques are transforming animal anatomy research, education, and diagnostics.
• Computational tools offer solutions for ethical issues, specimen unavailability, and high laboratory costs.
• Modern technology aids in analyzing complex anatomical data and mimicking human reasoning.

Purpose of the Study:

• To review the applications of machine learning (ML) and deep learning (DL) in veterinary anatomical science.
• To focus on the roles of computer vision and 3D modeling in veterinary anatomy.
• To explore the impact of AI on anatomical research, education, and diagnostics.

Main Methods:

• Review of machine learning (ML) and deep learning (DL) applications in veterinary anatomy.
• Focus on computer vision and 3D modeling techniques.
• Analysis of AI algorithms for image analysis, segmentation, classification, and visualization.

Main Results:

• AI enhances imaging accuracy, analysis, and diagnosis in animal anatomy, reducing errors and time.
• Deep learning with augmented reality and 3D models improve anatomical training and learning.
• AI tools facilitate volumetric analysis, anatomical reconstruction, and evolutionary research, improving veterinary diagnostics.

Conclusions:

• AI-driven technologies have revolutionized veterinary anatomy, enhancing efficiency, precision, and accessibility.
• Key AI technologies include ML, DL, computer vision, and 3D modeling for improved anatomical visualization.
• Persistent challenges include data scarcity, heterogeneity, and quality, with future AI advancements expected to further improve veterinary anatomical science.