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Related Experiment Video

Updated: Jan 29, 2026

Usability Evaluation of Augmented Reality: A Neuro-Information-Systems Study

Usability Evaluation of Augmented Reality: A Neuro-Information-Systems Study

Published on: November 30, 2022

AI-Based Augmented Reality Microscope for Real-Time Sperm Detection and Tracking in Micro-TESE.

Mahmoud Mohamed¹, Ezaki Yuriko², Yuta Kawagoe^1,3,4

¹Laboratory for Biomaterials and Bioengineering, Institute of Science Tokyo, Tokyo 152-8550, Japan.

Bioengineering (Basel, Switzerland)

|January 28, 2026

Summary

This study introduces an augmented reality microscope system with AI to help find sperm faster during non-obstructive azoospermia (NOA) treatments. The technology significantly improves sperm detection rates, potentially reducing procedure times for male infertility.

Keywords:

Micro-TESE augmented reality microscope deep learning non-obstructive azoospermia object tracking sperm detection sperm motility analysis

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Last Updated: Jan 29, 2026

Usability Evaluation of Augmented Reality: A Neuro-Information-Systems Study

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Area of Science:

Reproductive Medicine
Medical Imaging
Artificial Intelligence

Background:

Non-obstructive azoospermia (NOA) is a severe male infertility cause.
Microdissection testicular sperm extraction (Micro-TESE) is time-consuming and labor-intensive for NOA diagnosis.
Manual sperm searching in Micro-TESE can take hours, leading to embryologist fatigue.

Purpose of the Study:

To develop and evaluate an augmented reality (AR) microscope system with AI for accelerated sperm retrieval in Micro-TESE.
To improve the efficiency and accuracy of sperm identification during NOA treatment.

Main Methods:

Integration of a deep learning model (YOLOv5) for real-time sperm detection.
Implementation of a multi-object tracker (DeepSORT) for sperm tracking and motility analysis.
Overlay of sperm data (position, motility) onto the microscope eyepiece view via AR for embryologist guidance.

Main Results:

The YOLOv5 model demonstrated superior accuracy and speed in sperm detection compared to classical methods (precision 0.81, recall 0.52).
The AR microscope system approximately doubled the sperm detection rate (66.9% vs. 30.8%) in experimental trials.
The system provided real-time visual guidance, highlighting sperm and potentially reducing Micro-TESE procedure duration.

Conclusions:

The AR microscope system with AI significantly aids embryologists in sperm retrieval for NOA.
This technology shows promise for enhancing efficiency and outcomes in assisted reproductive technology (ART).
Real-time AI-driven sperm identification can alleviate the burden of Micro-TESE procedures.