Surgical Instrument Tracking Through Synchronized Audio-Image Learning

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Solution Overview

Problem

Existing methods for identifying medical devices in surgical environments are hindered by manual input requirements, reliance on predefined databases, and limitations in voice control, leading to inefficiencies and errors in instrument tracking.

Innovation Solution

A learning algorithm is trained using synchronized audio and image data to identify medical devices, allowing for automated identification and prediction of device usage based on audio and visual cues.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If manual input is used to identify medical devices, then the system can track instruments, but the workflow is hindered and efficiency is reduced

Engineering Contradiction:

Improveinstrument tracking accuracyVSAvoidworkflow efficiency

Core Design Contradiction:

ReliabilityVSProductivity

Solution Approach 1:

The patent replaces manual mechanical input methods with automated acoustic field-based identification. Audio sensors capture device sounds (e.g., packaging opening, device activation) and automatically identify instruments without manual intervention, eliminating the trade-off between tracking reliability and workflow efficiency

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The system enables medical devices to self-identify through their inherent acoustic signatures. Devices automatically provide identification information through their operational sounds, eliminating the need for manual input while maintaining accurate tracking throughout the procedure

Inventive Principle:

Principle #25Self-service

2Extent of automation

If predefined databases of instrument shapes or marker patterns are used, then identification can be automated, but the system lacks adaptability to situational actions

Engineering Contradiction:

Improveidentification automationVSAvoidsituational adaptability

Core Design Contradiction:

Extent of automationVSAdaptability or versatility

Solution Approach 1:

The patent implements a dynamic identification system that adapts to different surgical situations in real-time. The acoustic identification system learns and adapts to various device sounds and surgical contexts during the procedure, providing both automation and situational flexibility that static databases cannot achieve

Inventive Principle:

Principle #15Dynamics

Solution Approach 2:

The system changes identification parameters dynamically by using multiple acoustic features (frequency, amplitude, temporal patterns) rather than fixed visual parameters. This allows the system to automatically identify devices while adapting to different surgical scenarios and device types without requiring predefined databases

Inventive Principle:

Principle #35Parameter changes

3Ease of operation

If voice control is used for device identification, then hands-free operation is possible, but the system is not generic enough for every situation

Engineering Contradiction:

Improvehands-free operationVSAvoidsituational coverage

Core Design Contradiction:

Ease of operationVSAdaptability or versatility

Solution Approach 1:

The patent creates a universal acoustic identification system that works across all surgical situations without requiring specific voice commands. The system uses audio sensors to capture a wide range of device sounds and operational contexts, providing hands-free identification that is as versatile as voice control but applicable to all devices and scenarios

Inventive Principle:

Principle #6Universality (Multi-functionality)

Data Source

PatentUS12450881B2Training and using a learning algorithm using a transcript of audio data for identification of a medical device visible in image data

Publication Date: 2025.10.21 BRAINLAB AG

AI summary

Disclosed are computer-implemented methods of training a learning algorithm on the basis of audio and image data which has been pre-processed to generate a time-synchronized transcript of the audio information and the image information to allow the learning algorithm to identify a medical device such as a medical instrument or a medical instrument on an instrument table which is visible in the image data and output corresponding information to a user. Embodiments include additional prediction of a next instrument to be used and a counting of instruments which have been used.