(46 days)
"The Spectrum Insulation Tester checks for insulation integrity on laparoscopic instruments"
Insulation Tester
The information details the acceptance criteria and performance testing for a medical device called an "Insulation Tester".
1. Table of acceptance criteria and the reported device performance
| Acceptance Criteria / Spectrum Specifications (Measured Values) | Spectrum Insulation Tester Reported Performance | Predicate Device (Jac-Cell Medic K020334) Performance | Comments |
|---|---|---|---|
| Normal (open) Batt. @ 9V - 32 - 40 mA | 34.5mA | 38.2mA | Spectrum lower = longer Batt. life. |
| Normal (open) Batt. @ 9V - 2200 - 2400 Volts | 2.330 Volts | 2.374 Volts | Slight voltage diff. performance is same |
| Normal (open) Batt. @ 7V - 32 - 40 mA | 34.5mA | 30.9mA | Not at 7V for long period of time. Low Battery. |
| Normal (open) Batt. @ 7v - 2200 - 2400 Volts | 2.330 Volts | 2.015 Volts | Spectrum voltage stable through 7V |
| Fault (short) Batt. @ 9V - 115 - 145mA | 132mA | 158mA | Spectrum lower longer Batt. life. |
| Fault (short) Batt. @ 7V - 115 - 145 mA | 132mA | 108mA | Not at 7V for long period of time. Low Battery. |
| Fault Detection | Red LED, Sound | Red LED, Sound | Spectrum & Jac-Cell Equal |
| Low Battery Indicator | Red LED | Red LED | Spectrum and Jac-Cell Equal |
| Detection of insufficient insulation | 100% success rate | Not explicitly stated, but implied as predicate standard | Achieved for both pinhole and abraded surface conditions. Not rejected known good insulated shafts. |
2. Sample size used for the test set and the data provenance
- Sample Size: A minimum of 30 tests were performed for each condition (pin hole, abraded surface) over 2 different time periods. In total, 180 tests were completed.
- Data Provenance: The study simulated "real-world conditions" by modifying laparoscopic shafts. This suggests a laboratory-based, prospective testing scenario by the manufacturer, Spectrum Surgical Instruments. The country of origin of the data is not explicitly stated but can be inferred as the United States, given the company's address and submission to the FDA.
3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts
This type of information is not provided in the document. The device testing relies on the physical modification of laparoscopic shafts by introducing "a small pin hole" or "abrading the surface in an area" to simulate insufficient insulation. The success or failure of detection is based on the device's output (Red LED, Sound) rather than expert interpretation of the condition.
4. Adjudication method for the test set
Not applicable. The "ground truth" (presence or absence of insufficient insulation) was established by physically altering the laparoscopic shafts. The device's detection capabilities (activating LED and sound) were then directly observed.
5. If a multi reader multi case (MRMC) comparative effectiveness study was done, If so, what was the effect size of how much human readers improve with AI vs without AI assistance
Not done and not applicable. This document describes the performance of a standalone device (Insulation Tester), not an AI-assisted diagnostic tool or system involving human readers.
6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done
Yes, a standalone performance study was done. The entire performance testing section evaluates the "Insulation Tester's" ability to autonomously detect insufficient insulation without human interpretation or intervention beyond operating the device. The device had a "100% success rate in being able to detect insufficient insulation while not rejected known good insulated laparoscopic shafts."
7. The type of ground truth used (expert consensus, pathology, outcomes data, etc)
The ground truth was simulated physical defects / known conditions. Laparoscopic shafts were intentionally modified to create "a small pin hole" or "abraded the surface in an area" to represent insufficient insulation. This provides a definitive, objective ground truth for the presence of a fault.
8. The sample size for the training set
Not applicable. This device is a physical instrument for detecting electrical faults, not an AI/machine learning algorithm that requires a training set.
9. How the ground truth for the training set was established
Not applicable. As stated above, this device does not utilize a training set in the context of machine learning.
§ 878.4400 Electrosurgical cutting and coagulation device and accessories.
(a)
Identification. An electrosurgical cutting and coagulation device and accessories is a device intended to remove tissue and control bleeding by use of high-frequency electrical current.(b)
Classification. Class II.