Surgical Staple Image Inspection for Defect Mapping and Measurement
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Solution Overview
Problem
Conventional surgical stapling device inspection processes are slow, reliant on human interpretation, and lack fine-grained granularity for identifying specific failures such as misfired, malformed, or improperly formed staples.
Innovation Solution
An automated surgical staple inspection system using machine learning models to analyze images of staples fired into a test skin, detecting staple locations, classes, and keypoint features, generating digital reports, and simulating tests based on design characteristics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If manual inspection of staples is performed, then human interpretation and judgment are applied, but the inspection process is slow and lacks fine-grained granularity
Solution Approach 1:
The patent replaces manual mechanical inspection with an automated image processing system that captures images of test staples and uses computer vision algorithms to automatically detect and classify staple defects. This substitution eliminates human interpretation variability and significantly reduces inspection time while providing detailed granular data about each staple's condition.
Solution Approach 2:
The system creates digital copies (images) of the physical test staples and performs inspection on these copies through automated image analysis. This allows multiple analyses of the same staple without physical manipulation, enabling rapid repeated inspections and detailed examination of specific defect areas without affecting the original test sample.
2Measurement precision
If manual inspection is used, then general pass/fail determination is provided, but fine-grained granularity relating to specific failures is not achieved
Solution Approach 1:
The inspection system segments the inspection process into distinct analytical components: detecting staple presence, identifying staple locations, classifying staple formation quality, and categorizing specific defect types. This segmentation enables the system to provide detailed information about each aspect of staple performance rather than a single aggregate pass/fail result, preserving comprehensive failure information.
Solution Approach 2:
The patent introduces image processing algorithms and machine learning models as intermediaries between the physical staple and the inspection result. These intermediaries extract detailed features from staple images, including subtle formation defects, positioning errors, and dimensional variations, converting them into structured data that preserves fine-grained information about specific failures.
3Productivity
If conventional testing methods are used, then simple pass/fail results are obtained, but detailed inspection data for optimization is not generated
Solution Approach 1:
The automated inspection system enables continuous operation where images are captured and processed without interruption. Multiple staples can be inspected in sequence or parallel, maintaining continuous workflow while generating comprehensive data for each staple. This continuous operation increases throughput while the detailed image analysis ensures no information is lost, unlike discrete manual inspection intervals.
Solution Approach 2:
The system transforms the inspection output from a single parameter (pass/fail) to multiple parameters including staple position coordinates, formation quality metrics, defect type classifications, and dimensional measurements. This parameter expansion allows the system to maintain high productivity through automated processing while preserving detailed information useful for manufacturing optimization and root cause analysis.
Data Source
AI summary
An automated surgical staple inspection system automatically generates surgical staple inspection data characterizing a set of staples fired into a test skin by stapling device in a staple test. An inspection processing device obtains one or more images of the set of staples and applies an object detection model to infer locations of the staples and respective classes of the staples indicating whether the staples are properly formed or have one or more identified defects. Based on the inferred locations, staples may be mapped to respective positions in an expected staple pattern to generate a staple map. A keypoint detection model furthermore operates to automatically identify a set of keypoints in each staple from which staple height may be determined. The inspection processing device may generate one or more digital reports based on the inspection data, which may be outputted to a user interface of a user device.