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The TD-5010 Lancing Device and TD-5084 Sterile Lancets are intended to obtain capillary blood for testing purposes from the side of a fingertip and from alternative sites, such as the palm, and the forearm. The single-use TD-5084 Sterile Lancets are to be used with the reusable TD-5010 Lancing Device that is to be cleaned and disinfected between each use, and then the TD-5084 Sterile Lancets are to be disposed of.

For use only on a single patient in a home setting.

Not suitable for use by healthcare professionals with multiple patients in a healthcare setting.

The TD-5010 Lancing Device uses compatible TD-5084 Sterile Lancets to obtain a drop of blood from a fingertip or alternative sites. The TD-5010 Lancing Device consists of two components:

(1)TD-5010 Lancing Device

(2) Alternative Site Testing (AST) Cap

The provided text describes the submission for a medical device (TD-5010 Lancing Device and TD-5084 Sterile Lancets) for clearance by the FDA, based on substantial equivalence to a predicate device (Accu-Chek Softclix Blood Lancing System). The document focuses on non-clinical performance testing rather than a clinical study involving human patients or an AI algorithm.

Therefore, many of the requested fields regarding acceptance criteria, sample sizes for test/training sets, expert involvement, and statistical measures for an AI/ML-based medical device are not applicable to this submission as it pertains to a mechanical lancing device and sterile lancets. There is no AI or software component described in this submission.

However, I can extract the acceptance criteria and performance data for the mechanical and material properties of the lancing device and lancets as described in the non-clinical testing summary.

Here's the breakdown based on the provided document:

Device: TD-5010 Lancing Device and TD-5084 Sterile Lancets

Study Type: Non-clinical (bench) performance testing for substantial equivalence. This is not a study proving the efficacy of an AI model.

Acceptance Criteria and Reported Device Performance (Non-Clinical Bench Testing):

Study Details:

1. Sample Size used for the test set and the data provenance:

   • The document describes bench testing of physical devices and components, not a "test set" in the context of an AI/ML algorithm.
   • Specific numbers of devices or lancets tested for each non-clinical test (e.g., how many lancet caps twisted, how many devices dropped) are not explicitly stated. The provenance is implied to be laboratory testing conducted by the manufacturer. It is non-clinical/bench data.

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

   • Not applicable. This is a mechanical device, not an AI model requiring expert ground truth for image interpretation or diagnosis. Ground truth is based on engineering specifications and physical measurements.

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

   • Not applicable. There's no human interpretation or adjudication process described for the device's mechanical performance.

4. 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. No AI component. No human reader study described.

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

   • Not applicable. No AI algorithm.

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

   • For the non-clinical tests, the "ground truth" or reference standards are the engineering specifications, physical measurements, and established testing protocols (e.g., for sterilization, biocompatibility, mechanical forces, and material properties) for lancing devices, often guided by relevant ISO standards (though not explicitly cited for each test, 21 CFR 878.4850 is mentioned).

7. The sample size for the training set:

   • Not applicable. There is no AI component and thus no "training set" in the context of machine learning.

8. How the ground truth for the training set was established:

   • Not applicable.

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MedtFine Blood Lancet is intended for use together with a lancing device of skin to obtain a drop of capillary blood from fingertip or from alternative sites.

MedtFine Blood Lancet is a small medical device used for capillary blood sampling. It is similar to a small scalpel with a double-edged needle used to make punctures, such as a fingerstick, to obtain small blood specimens. The device consists of a needle sheath, a hub, and a needle and provided sterility that is sterilized by gamma radiation. The device has three types listed in table 1 with recognized color. The device is an over-the-counter instrument equipped with a lancing device that patients mostly use during blood monitoring. However, in this case, it doesn't contain a lancing device, only the lancet. The proposed device is a universal device that can be fitted with most common marked lancing devices.

This document is a 510(k) Pre-market Notification for a medical device called "MedtFine Blood Lancet." It aims to demonstrate that the device is substantially equivalent to legally marketed predicate devices.

Here's the breakdown of the acceptance criteria and the study that proves the device meets them, based on the provided text:

1. Table of Acceptance Criteria and Reported Device Performance

The acceptance criteria are primarily derived from various ISO standards. The reported device performance is presented in comparison to these standards and the predicate devices.

2. Sample Sizes Used for the Test Set and Data Provenance

The document does not explicitly state the specific sample sizes used for each individual non-clinical test conducted for the MedtFine Blood Lancet. It generally states that "all proposed device types' performance testing is run" for the different gauge sizes.

The data provenance is from non-clinical testing conducted by the manufacturer, Shanghai Carelife International Trading Co. Ltd. (China). The data is prospective in the sense that these tests were performed specifically for this 510(k) submission to demonstrate compliance.

3. Number of Experts Used to Establish the Ground Truth for the Test Set and Qualifications of Those Experts

This detailed information is not provided in the document. The non-clinical testing appears to be primarily laboratory-based measurements and evaluations against established ISO standards and internal requirements, rather than relying on expert consensus for "ground truth" in the way a clinical study might for image interpretation.

4. Adjudication Method for the Test Set

This information is not applicable in the context of this 510(k) submission. The non-clinical tests described are objective measurements against predefined standards, which do not typically involve human adjudication or consensus methods like those used in clinical studies for diagnostic accuracy.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study Was Done, and Effect Size

No, a Multi-Reader Multi-Case (MRMC) comparative effectiveness study was not done. The submission explicitly states: "No clinical testing is available for this device."

6. If a Standalone (Algorithm Only Without Human-in-the-Loop Performance) Was Done

This question is not applicable as this describes a physical medical device (blood lancet), not a software algorithm or AI-driven system.

7. The Type of Ground Truth Used

The "ground truth" for the non-clinical tests is established by:

• International Standards: Primarily ISO standards (e.g., ISO 9626, ISO 7864, ISO 10993, ISO 11137, ISO 11607, ISO 11737).
• National Standards/Pharmacopoeias: ASTM standards (e.g., ASTM F1980, ASTM F1929, ASTM D4169) and USP (United States Pharmacopeia) tests (e.g., USP , USP , USP , USP ).
• Internal Requirements: For visual checking, additional internal requirements "derived from the visual checking sections of ISO 9626 and ISO 78664" were used.

These standards define the acceptable range or qualitative outcome for each test (e.g., "no corrosion," "meets requirements," specific numerical limits).

8. The Sample Size for the Training Set

This question is not applicable. This is a submission for a physical medical device. The concepts of "training set" and "validation set" are relevant to machine learning/AI models, which are not involved here. The manufacturer performed a series of tests on the device itself to demonstrate its inherent properties and safety.

9. How the Ground Truth for the Training Set Was Established

This question is not applicable for the same reasons as #8.

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