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The POGO Automatic Blood Glucose Monitoring System is intended to be used for the quantitative measurement of glucose (sugar) in fresh capillary whole blood samples drawn from the fingertips. The POGO Automatic Blood Glucose Monitoring System is intended to be used by a single person and should not be shared.

The POGO Automatic Blood Glucose Monitoring System is intended for self testing outside the body (in vitro diagnostic use) by people with diabetes at home as an aid to monitor the effectiveness of diabetes control. The POGO Automatic Blood Glucose Monitoring System should not be used for the diagnosis of or screening of diabetes or for neonatal use. The POGO Automatic Blood Glucose Monitoring System is indicated for use in adults and adolescents (13 and up).

POGO Automatic Test Cartridges are for use with the POGO Automatic Meter to quantitatively measure glucose (sugar) in fresh capillary whole blood samples drawn from the fingertips.

Device Description

The POGO BGMS is a quantitative assay for the detection of glucose in capillary whole blood sampled from the fingertip. The system includes multiple glucose-oxidase-based dry-reagent test strips housed in a cartridge and a photometer to read the glucose-dependent color change from the in-use strip. The meter uses calibration information from a barcode on the disposable cartridge to convert the reflectance information into a plasma-equivalent glucose value. A Bluetooth Low Energy (BLE) module in the meter allows the user to authorize the POGO meter to send data wirelessly, securely, and automatically after each test to specified electronic computing devices.

The POGO BGMS automates finger lancing, blood sample collection and placement onto the test strip, and calculation of the blood glucose result, and so requires significantly fewer steps than existing BGMSs to obtain a glucose result. The POGO BGMS accomplishes this via a 10-test cartridge where each foil-sealed test includes a hollow lancet, spring, and test strip. The user does not need a separate lancing device since the lancing mechanism is built into individual test cells of the cartridge. The blood sample is drawn into the test through capillary action and the meter, by monitoring the time and degree of blood spreading on the reagent pad, can detect when the test is under-filled and allow the user a limited amount of time to add blood to the test trip to obtain a glucose result rather than an error. Additionally, used tests are retained within the cartridge for added user convenience.

AI/ML Overview

The provided document is a 510(k) summary for the POGO Automatic Blood Glucose Monitoring System (POGO BGMS). It describes the device, its intended use, and compares it to a predicate device (an earlier version of the same system).

However, this document does not contain the detailed information necessary to fully answer your request regarding acceptance criteria and the study that proves the device meets them. Specifically, it lacks:

A table of acceptance criteria and reported device performance for glucose measurement accuracy. While it lists general characteristics, it does not provide specific performance metrics like accuracy studies (e.g., ISO 15197 compliance data).
Sample sizes used for testing.
Ground truth establishment and expert qualifications.
Information about MRMC studies, standalone algorithm performance, or training set details.

The document primarily focuses on demonstrating substantial equivalence to a predicate device based on modifications that are largely non-functional (BLE capability, display changes, speaker, feet). It asserts that the fundamental technological characteristics, blood sampling, blood glucose measurement, and algorithm remain unchanged from the predicate. Therefore, it relies on the predicate's proven performance for establishing substantial equivalence in its core function (glucose measurement).

Based on the provided text, I can infer the following:

The acceptance criteria for the POGO Automatic Blood Glucose Monitoring System's glucose measurement performance is that it performs as safely and effectively as the predicate device (K162203). The document explicitly states: "The subject modifications for the POGO BGMS do not affect any of the above attributes that were included in the substantial equivalence section of the predicate device and relied upon for the substantial equivalence finding for the predicate device, K162203." It also mentions "Same fundamental technological characteristics. The following remain unchanged: blood ● sampling and blood glucose measurement, use of the same cartridge (which contains the reagent system used to measure blood glucose), technology and procedure to acquire a blood sample and monitor the color development on the reagent pad and the algorithm to calculate a glucose value from the reaction at the reagent pad."

Therefore, the "study that proves the device meets the acceptance criteria" for glucose measurement is implicitly the original study data for the predicate device (K162203), as the core measurement technology has not changed. The current submission focuses on demonstrating that the modifications (BLE, display, etc.) do not degrade this established performance.

Here's what can be extracted and what is missing based on your request:

Acceptance Criteria for the POGO Automatic Blood Glucose Monitoring System

1. Table of Acceptance Criteria and Reported Device Performance:

Acceptance Criteria Category	Specific Criterion (Inferred from non-change)	Reported Device Performance (Inferred/Stated)
Glucose Measurement Accuracy	Equivalent to predicate device (K162203) as core technology is unchanged.	The document states the blood glucose measurement technology and algorithm are "unchanged." Specific accuracy data (e.g., % within ISO 15197 zones) is not provided in this document for either the subject or predicate.
Functional Modifications	Performance specifications established for modified components.	"Pass" result for all performance tests on modified items.
Safety Testing	Compliance with Mechanical, Environmental, and Electrical safety standards for SMBG devices.	"Pass" result for all safety tests.
Cleaning & Disinfection	Efficacy and robustness challenges on raw materials and assembled device.	"Pass" result for efficacy and robustness challenges.
Software V&V	Software V&V requirements met.	"Pass" result for Software V&V.
Wireless Connectivity (BLE)	Bluetooth wireless proximity and coexistence testing requirements met.	"Pass" result for BLE testing.
Labeling	Readability.	"Pass" result for labeling readability.
Under-filled detection	Detects under-filled reagent pad.	"Yes," device detects under-filled reagent pad.
Blood reapplication	Allows blood reapplication for up to 15 seconds.	"Yes," provides glucose result or error code after blood addition; 15 seconds allowed for reapplication.
Blood application time	90 seconds allowed if blood does not reach reagent pad.	"Yes," 90 seconds allowed.
Strip exposure limits	Meter enforces limits on strip exposure (5 min open cell time window).	"Yes," device design prevents strip use after 5 minutes.
Lancet re-use	Prevents lancet re-use.	"No," device design prevents re-use.
Cal Coding	Automated.	"Automated."
Hematocrit Range	20% to 60%.	"20% to 60%."
Humidity Range	10% to 90% RH.	"10% to 90% RH."
Measurement Range	20 - 500 mg/dL.	"20 - 500 mg/dL."
Min. Sample Size	0.25 µL.	"0.25 µL."
Operating Temperature Range	50 to 104° F.	"50 to 104° F."

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

Test Set Sample Size: Not specified for any of the performance tests. The document only states that "Multiple nonclinical tests were identified via risk assessment activities as requirements to demonstrate comparable safety and effectiveness."
Data Provenance: Not specified (e.g., country of origin). The document implies the tests were conducted by the manufacturer, Intuitv Medical, Inc. The data is retrospective in the sense that it relies on the predicate's established performance and new testing for the modifications.

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

Not applicable / Not specified. This type of information is usually relevant for AI/ML diagnostic devices where human expert consensus establishes ground truth for image interpretation or diagnosis. For a blood glucose monitoring system, the ground truth for glucose measurements would typically be established by a laboratory reference method (e.g., YSI analyzer), rather than expert consensus.

4. Adjudication method for the test set:

Not applicable / Not specified. 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. This is not relevant for a blood glucose monitoring system, which provides a direct quantitative measurement and does not involve human interpretation of complex medical images or data requiring AI assistance.

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

Yes (in principle, for the core function). The device itself performs the glucose measurement and calculation ("algorithm to calculate a glucose value from the reaction at the reagent pad"). The performance refers to the device's accuracy against a known standard. The 510(k) submission states that this core algorithm and measurement technology are unchanged from the predicate device. Therefore, the standalone performance was established for the predicate device.

7. The type of ground truth used:

Laboratory Reference Method. For blood glucose monitoring systems, the ground truth for glucose concentration is typically established by a highly accurate laboratory analyzer (e.g., YSI glucose analyzer) that is well-calibrated and traceable to international standards. This document does not explicitly state the reference method, but it is standard practice for such devices.

8. The sample size for the training set:

Not applicable / Not specified. Blood glucose monitoring systems typically use a chemical reaction and photometric measurement with a pre-defined calibration curve. They are not generally "trained" in the typical machine learning sense with a "training set" of patient data that needs a ground truth established in the same way as an AI algorithm for image recognition. The "algorithm" here refers to the fixed mathematical conversion from optical signal to glucose concentration, derived from chemical principles and calibrated during manufacturing. While calibration data is used, it's not described as a "training set" in this context.

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

Not applicable. As above, the device uses a chemical-photometric principle. Calibration curves are established using samples with known glucose concentrations, typically measured by a laboratory reference instrument (the "ground truth").

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K Number

K162203

Device Name

POGO Automatic Blood Glucose Monitoring System

Manufacturer

Intuity Medical, Inc.

Date Cleared

2017-04-06

(244 days)

Product Code

Regulation Number

Type

Panel

Reference & Predicate Devices

k130265

Predicate For

K181316

Intended Use

POGO Automatic Test Cartridges are for use with the POGO Automatic Meter to quantitatively measure glucose (sugar) in fresh capillary whole blood samples drawn from the fingertips.

Device Description

The POGO Automatic Blood Glucose Monitoring System (POGO BGMS) is a quantitative assay for the detection of glucose in capillary whole blood sampled from the fingertip. The system includes multiple glucose-oxidase-based dry-reagent test strips housed in a cartridge and a photometer to read the glucose-dependent color change from the in-use strip. The meter uses calibration information from a barcode on the disposable cartridge to convert the reflectance information into a plasma-equivalent glucose value.

AI/ML Overview

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

Acceptance Criteria and Device Performance

Acceptance Criteria (from FDA Guidance for Self-Monitoring Blood Glucose Test Systems for Over-the-Counter Use, October 2016)	Reported Device Performance (POGO BGMS Self-Test Glucose Results)
Accuracy relative to YSI reference:
**For glucose concentrations 75mg/dL) or 10 mg/dL (for glucose

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K Number

K152493

Device Name

POGO Automatic Blood Glucose Monitoring System

Manufacturer

INTUITY MEDICAL, INC

Date Cleared

2016-04-25

(237 days)

Product Code

Regulation Number

Type

Panel

Reference & Predicate Devices

k081389,k123851

Predicate For

N/A

Intended Use

POGO Automatic Test Cartridges are for use with the POGO Automatic Meter to quantitatively measure glucose (sugar) in fresh capillary whole blood samples drawn from the fingertips.

POGO Control Solutions are used with the POGO Automatic Meter to indicate appropriate user technique and to indicate that the POGO Automatic Test Cartridge and POGO Automatic Meter are functioning properly.

Device Description

AI/ML Overview

Here's a breakdown of the acceptance criteria and study information for the Pogo Automatic Blood Glucose Monitoring System, based on the provided text:

Acceptance Criteria and Device Performance

Acceptance Criteria Category	Acceptance Criteria (from "The protocol acceptance criteria were met")	Reported Device Performance (as described in the text)
Repeatability	Met the protocol acceptance criteria.	%CVs ranged from 2.3% to 3.4% across five whole blood samples (41.8 mg/dL to 306.7 mg/dL) tested in replicates of 10 with 10 meters.
Intermediate Precision	Met the protocol acceptance criteria.	%CVs were 6.5% (2.1 SD), 3.1%, and 3.2% for three levels of commercial controls (approx. 30, 100, and 300 mg/dL) tested in duplicate with 10 meters over 10 days.
Linearity	Met the protocol acceptance criteria.	Linear throughout the claimed dynamic range, based on testing a whole blood sample spiked with nine glucose levels (17 mg/dL to 529 mg/dL) in replicates of 10.
Hematocrit Tolerance	Met the protocol acceptance criteria.	Accurate throughout the claimed 20-60% hematocrit range, based on studies with four glucose levels (60, 120, 250, and 400 mg/dL) and seven different hematocrits.
Altitude Tolerance	Met the protocol acceptance criteria.	Provides accurate results at altitudes up to the claimed 10,000 feet, based on studies at sea level, 5,000 feet, and 10,000 feet with blood at nominal glucose levels of 60, 120, and 300 mg/dL.
Temperature Tolerance	Met the protocol acceptance criteria.	Provides accurate results across the tested 4°C to 40°C range and the claimed temperature range of 10°C to 40°C, based on testing at five temperatures with nominal glucose levels of 60, 120, and 300 mg/dL.
Humidity Tolerance	Met the protocol acceptance criteria.	Provides accurate results when tested across the claimed humidity range of 10% to 90% RH, based on testing at 10%, 50%, and 90% relative humidity with nominal glucose levels of 60, 120, and 300 mg/dL.
Temperature and Humidity Combinations	Met the protocol acceptance criteria.	Provides accurate results at extreme combinations of temperature and humidity (25°C/45% RH, and 10℃/40℃ crossed with 10%/90% RH) with three nominal glucose levels of 60, 120, and 300 mg/dL.
Sample Volume Tolerance	Met the protocol acceptance criteria.	Accurate when tested with a sample volume between 250 and 1000 nL. At 80% felt it was more convenient. Majority (51%) agreed they'd better adhere to testing, and >81% would recommend it.

Study Details

Sample size used for the test set and the data provenance:
- Test Set Sample Size (Clinical): 287 subjects with diabetes.
- Data Provenance: Subjects were enrolled across US clinical sites. The study was prospective as subjects performed tests, were observed, and completed questionnaires.
Number of experts used to establish the ground truth for the test set and the qualifications of those experts:
- Number of Experts: Not explicitly stated as a specific number. The text mentions "health care professionals (HCPs)" observed subjects and "HCPs also performed assisted fingerstick tests." It doesn't specify how many or their exact qualifications beyond "health care professionals."
- Qualifications: "Health care professionals (HCPs)." No specific details like years of experience or specialization (e.g., endocrinologist, nurse) are provided.
Adjudication method (e.g., 2+1, 3+1, none) for the test set:
- The text does not describe an adjudication method for the test set results. The accuracy of the POGO BGMS results was compared directly against a reference instrument (YSI).
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, a multi-reader multi-case (MRMC) comparative effectiveness study was not done. This is a blood glucose monitoring system, not an imaging AI device. The study focused on the performance of the device itself and user experience.
If a standalone (i.e., algorithm only without human-in-the-loop performance) was done:
- Yes, a standalone performance was done for the analytical studies (Repeatability, Intermediate Precision, Linearity, Hematocrit Tolerance, etc.). These tests evaluate the device's inherent analytic capabilities under controlled conditions.
- The clinical accuracy study also includes a "user self-testing" arm, which represents the device's performance when used by the intended lay user (human-in-the-loop).
The type of ground truth used (expert consensus, pathology, outcomes data, etc.):
- Clinical Accuracy Ground Truth: YSI reference instrument. For blood glucose monitoring systems, a YSI (Yellow Springs Instrument) glucose analyzer is considered a highly accurate laboratory reference method.
The sample size for the training set:
- The provided document describes a 510(k) summary for a medical device (blood glucose monitor). For such devices, "training sets" in the context of machine learning (AI) are not typically applicable. The device relies on chemical reactions and optical measurement, calibrated during manufacturing and design verification. The "training" for the device's performance is embedded in its design, chemical formulation, and manufacturing process.
- Therefore, a specific "training set sample size" as one would discuss for an AI algorithm is not applicable here.
How the ground truth for the training set was established:
- As noted above, a "training set" in the AI sense is not applicable. The device's internal calibration and performance parameters would have been established during its development and manufacturing using high-precision reference methods (like YSI) and extensive analytical studies to ensure accuracy and precision across its operational range. These internal calibration processes are part of the device's engineering and quality control, not a "training set" for an algorithm.

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