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This instrument has been designed to be used with an Olympus endoscope to deliver injectable materials into the urinary bladder wall during the transurethral endoscopic procedures.

This instrument has been designed to be used with an Olympus endoscope to deliver injectable materials into the urinary bladder wall during the transurethral endoscopic procedures. The subject device is sterilized and packaged in a sterilization package.

The provided document is a 510(k) premarket notification for a medical device called the "Single Use Injector NM-221C-0427." This is a device used in conjunction with an endoscope to deliver injectable materials into the urinary bladder wall.

Based on the content, specifically Section 7 "PERFORMANCE DATA," it is clear that this submission focuses on bench testing, sterilization/shelf-life testing, and biocompatibility testing to demonstrate substantial equivalence to a predicate device. There is no mention of a study involving AI, human readers, or image analysis.

Therefore, many of the requested details about acceptance criteria and studies related to AI performance, such as MRMC studies, expert ground truth establishment, training sets, and data provenance, are not applicable to this specific device submission as described in the provided text.

However, I can extract the acceptance criteria and performance data that were provided for this traditional medical device.

1. Table of Acceptance Criteria and Reported Device Performance

For this device, the "acceptance criteria" are implied by the successful completion of the specified performance tests. The document states that the tests were conducted "to ensure that the subject device performs as intended and meet design specifications."

2. Sample size used for the test set and the data provenance

• Sample Size: Not explicitly stated for each test, but standard engineering practices for medical devices (e.g., ISO, ASTM standards) imply sufficient samples were tested to demonstrate performance and meet statistical confidence. This is not a data-driven AI model, so "test set" in the context of images or AI performance isn't applicable.
• Data Provenance: Not applicable in the context of image data. The testing was conducted by Olympus Medical Systems Corp. and Aomori Olympus Co., Ltd. (Japan) to support their 510(k) submission to the FDA (USA).

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts

• Not applicable. This device is a physical instrument, not an AI or image analysis system. Ground truth is established through adherence to engineering specifications and performance standards (e.g., "Needle slidability" is assessed against a defined mechanical threshold, not expert consensus on an image).

4. Adjudication method (e.g. 2+1, 3+1, none) for the test set

• Not applicable. See point 3.

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

• No. The document explicitly states: "No clinical study was performed to demonstrate substantial equivalence." and "No animal study was performed to demonstrate substantial equivalence." This is not an AI-assisted diagnostic device.

6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done

• Not applicable. This is a physical medical device, not an algorithm.

7. The type of ground truth used (expert consensus, pathology, outcomes data, etc.)

• For physical performance characteristics (e.g., flow rate, durability, puncture performance), engineering specifications and validated testing methods serve as the "ground truth." For biocompatibility and sterilization, the "ground truth" is adherence to established international standards (e.g., ISO, ASTM, USP) and FDA guidance.

8. The sample size for the training set

• Not applicable. No training set was used as this is not an AI/machine learning device.

9. How the ground truth for the training set was established

• Not applicable. See point 8.

In summary, the provided document describes the regulatory submission for a physical medical device (a single-use injector) that demonstrates substantial equivalence through bench testing, biocompatibility testing, and sterilization validation, rather than studies focused on AI performance or human reader interaction with an AI system.

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This instrument has been designed to be used with an Olympus endoscope to perform endoscopic vascular or submucosal injection in the GI tract.

This instrument has been designed to be used with an Olympus endoscope to perform endoscopic vascular or submucosal injection in the GI tract.

The subject device consists of a handle section, needle section, and sheath section. The subject device is used to puncture the target tissue where guided by the endoscope. The fluid is injected to the target through the subject device by an injector which is connected the subject device.

The provided text describes a medical device, the "Single Use Injector NM600/610," and its 510(k) summary for FDA submission. However, it does not include the detailed information required to fill out a table of acceptance criteria, reported device performance, or details about a study proving the device meets those criteria in the way typically associated with AI/ML-based medical devices.

The document focuses on the substantial equivalence of the new device to existing predicate devices based on technological characteristics and non-clinical performance testing for a physical medical instrument (an injector for endoscopes). It does not describe a study involving data provenance, ground truth establishment by experts, or AI/ML performance metrics.

Therefore, I cannot provide a response that directly answers your request in the format you provided because the document is not about an AI/ML device and does not contain the type of study data you are asking for.

The information primarily available is related to non-clinical performance testing to demonstrate the basic functionality of a physical injector.

Here's an analysis of what is recoverable from the text, highlighting why it doesn't fit the requested format:

1. A table of acceptance criteria and the reported device performance

• Acceptance Criteria Mentioned: The document vaguely states "Risk analysis was carried out in accordance with established in-house acceptance criteria based on ISO 14971:2007. The design verification tests and their acceptance criteria were identified and performed as a result of this risk analysis assessment." However, it does not list specific numerical or qualitative acceptance criteria for the performance tests.
• Reported Device Performance: Similarly, it states "The following performance testing was conducted to demonstrate the basic performance of the subject device and confirmed that the subject device performs as intended." It lists categories of tests but provides no quantitative or qualitative results for "performance as intended."

2. Sample sized used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective)

• This information is not present. The tests mentioned are for a physical device, likely involving engineering and mechanical tests, not data sets in the typical AI/ML sense.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts (e.g. radiologist with 10 years of experience)

• Not applicable/Not present. Ground truth is not established in the context of expert review of images or data for this physical medical device.

4. Adjudication method (e.g. 2+1, 3+1, none) for the test set

• Not applicable/Not present.

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 applicable/Not present. This is not an AI-assisted device.

6. If a standalone (i.e. algorithm only without human-in-the loop performance) was done

• Not applicable/Not present. This is a physical medical device.

7. The type of ground truth used (expert consensus, pathology, outcomes data, etc)

• Not applicable/Not present. The "ground truth" for a physical device's performance would be engineering specifications and functional benchmarks, not clinical outcomes data in the usual sense for AI/ML.

8. The sample size for the training set

• Not applicable/Not present. There's no "training set" for this type of device.

9. How the ground truth for the training set was established

• Not applicable/Not present.

Summary of available information relevant to "studies" for the Single Use Injector NM600/610:

• Type of Study: Non-clinical (bench/engineering) performance testing.
• Purpose: To demonstrate basic performance and confirm the device performs as intended.
• Tests Performed (Categories):
  1. Inserting into endoscope
  2. Withdrawing from endoscope
  3. Advance of tube
  4. Retraction of tube
  5. Needle extension length
  6. Smooth puncturing of the needle
  7. Normal reaction force to needle puncturing
  8. Amount of injected fluid
  9. Needle retraction propriety
  10. Performance after repeated device operation
  11. Visual inspection of the package
  12. Peel strength of the package
  13. Endurance to splitting of the package
  14. Integrity of the package
• Risk Analysis: Performed in accordance with ISO 14971:2007 using in-house acceptance criteria.
• Biocompatibility Testing: Performed in accordance with FDA Guidance (Blue Book Memo, G95-1) and ISO 10993 standards (10993-1, 10993-5, 10993-10, 10993-11).
• Other Standards Applied: ASTM F756-13, ISO 11135, ISO 14971, ASTM F1980-07.

The document is a 510(k) summary for a fairly standard, non-AI medical instrument. The questions you've posed are highly specific to AI/ML medical devices and predictive algorithms, which is not what this document describes.

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