AI-Supported Ultrasound Analysis for Automated Pregnancy Detection in Cows

With AI-based ultrasound analysis, pregnancy detection in cows can be automated. The technology identifies fluid-filled areas, distinguishes artifacts, measures uterine structures, and delivers precise results. This enables farmers and veterinarians to make diagnoses more quickly, efficiently, and independently.

The Challenge: Shortage of Specialists in Fertility Diagnostics

A clear trend is emerging in livestock farming: fewer and fewer highly specialized veterinarians and experts are available for tasks such as pregnancy diagnosis. At the same time, the demand for efficient and regular fertility monitoring is increasing, in order to avoid economic losses. Ultrasound examinations, however, require in-depth expertise that is no longer available in every farm or region. This development calls for new technological approaches to ensure both the quality and speed of diagnostics.

Technological Solution: AI-Supported Ultrasound Analysis

The project is developing an innovative solution in which artificial intelligence (AI) automatically evaluates ultrasound images. Specialized algorithms detect fluid-filled regions, distinguish them from imaging artifacts, and measure relevant uterine structures. This enables pregnancy diagnosis directly during the scan – quickly, precisely, and without requiring years of specialized training. The AI models were specifically trained for agricultural ultrasound applications and can be seamlessly integrated into existing equipment of the project partners.

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Application and Benefits for Practice and Economy

Automated evaluation significantly reduces the training effort for veterinarians and makes it possible to quickly train less experienced staff. Farmers can, if they wish, carry out examinations themselves without depending on external specialists. This saves time, lowers costs, and allows for more frequent fertility monitoring within the herd. With this technology, reproductive planning in dairy farming becomes more efficient, predictable, and economically sustainable.

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Duration:

November 1, 2024 – September 30, 2025

Funding: