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SnapshotNIR (KD205) is intended for use by healthcare professionals as a non-invasive tissue oxygenation measurement system that reports an approximate value of:

• oxygen saturation (St02),
• relative oxyhemogoblin level (Hb02), and
• relative deoxyhemoglobin (Hb) level
  in superficial tissue. SnapshotNIR (KD205) displays two-dimensional color-coded images of tissue oxygenation of the scanned surface and reports multispectral tissue oxygenation measurements for selected tissue regions.

SnapshotNIR (KD205) is indicated for use to determine oxygenation levels in superficial tissues.

SnapshotNIR (KD205) is a medical device that operates like a camera. The SnapshotNIR device is completely non-contact, capturing images from 12 inches away from the patient or other imaging field-ofviews. The device uses six near-infrared wavelengths of light and white light emitting diodes (LEDs) to produce a resultant tissue oxygenation image and a colour-based "RGB" or clinical image, respectfully, that can be viewed on the touchscreen display.

SnapshotNIR is based on multispectral imaging technology and performs spectral analysis at each pixel to determine the approximate values of tissue oxygen saturation (StO2), oxyhemoglobin levels (HbO2), and deoxyhemoglobin levels (Hb) in superficial tissues and displays a two-dimensional, color-coded image of the tissue oxygen saturation (StO2).

SnapshotNIR consists of:

• · SnapshotNIR
• · Recharger
• · User Guide
• · Quick Start Guide
• · Sterile Drape (Optional)

The intended use and user interaction remained the same in the new model (KD205). The user will notice that the modified device no longer requires the user to take a calibration image prior to image capture. SnapshotNIR now uses an embedded computer module and a touchscreen LCD. Additional minor form factor changes were made on the new model (KD205) to accommodate the hardware change. With respect to the software used by SnapshotNIR, the predicate and new model (KD205) operate similarly.

This document, K240601, is a 510(k) Premarket Notification from the FDA regarding the Kent Imaging SnapshotNIR model KD205. It's a clearance letter, not a study report or a detailed technical submission. Therefore, it does not contain the information requested regarding acceptance criteria and performance studies in the level of detail typically found in a clinical study report or a more comprehensive technical submission to the FDA.

Specifically, the document states:

• "Nonclinical engineering-based tests (e.g., IEC 60601-1-2, etc) among other performance bench tests support conclusions that the KD205 device is safe for use with a low risk of adverse events occurring during the intended use scenarios."
• "These tests and findings are analogous to those conducted on the predicate KD204 SnapshotNIR device and demonstrate that the KD205 device is similarly safe, and effective compared to a current legally marketed device."
• "In a sub-analysis of the pre-clinical dataset, the KD205 device demonstrated no significant differences (derived via 2-way ANOVA models) in STO2 across the ischemia-reperfusion test when comparing cohorts of low versus high tissue scattering effects."

While it mentions "pre-clinical dataset" and "tests and findings," it does not provide any specific acceptance criteria, reported performance values, sample sizes, provenance of data, expert qualifications, or details about MRMC studies, standalone performance studies, or ground truth establishment. The purpose of this 510(k) summary is to establish substantial equivalence to a predicate device, not to detail the full performance study results.

Therefore, I cannot populate the requested table or answer most of the questions based solely on the provided text. The document focuses on the conclusion of substantial equivalence rather than the raw data and detailed methodology of the performance studies conducted.

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SnapshotNIR is intended for use by healthcare professionals as a non-invasive tissue oxygenation measurement system that reports an approximate value of:

• oxygen saturation (StO2),
• relative oxyhemogoblin level (HbO2), and
• relative deoxyhemoglobin (Hb) level
  in superficial tissue. SnapshotNIR displays two-dimensional color-coded images of tissue oxygenation of the scanned surface and reports multispectral tissue oxygenation measurements for selected tissue regions.

SnapshotNIR is indicated for use to determine oxygenation levels in superficial tissues.

SnapshotNIR, Model KD204 (K201976), is a modification to the Kent Camera, Model KD203 (K163070). The changes made to create the modified snapshot include modifications to the software. Both devices have similar hardware.

SnapshotNIR is based on multispectral imaging technology and performs spectral analysis at each point in a two-dimensional scanned area producing an image displaying information derived from the analysis. SnapshotNIR determines the approximate values of oxygen saturation (S-O2), oxyhemoglobin levels (HbO₂), and deoxyhemoglobin levels (Hb) in superficial tissues and displays a two-dimensional, color-coded image of the tissue oxygenation (S-O2).

The camera consists of:

• Camera: Contains light source, camera and touchscreen PC
• Recharger: Used to recharge the camera
• Reference Card: To calibrate the camera

The provided text is a 510(k) summary for the SnapshotNIR device, which is a modification of an existing predicate device. The primary focus of the document is to demonstrate "substantial equivalence" to the predicate device, rather than to establish new performance criteria for the device itself. Therefore, the "acceptance criteria" in the traditional sense of a new medical device approval (e.g., minimum sensitivity/specificity thresholds) and a separate "study that proves the device meets the acceptance criteria" are not explicitly defined in the provided document in the way one might expect for a novel device or AI/ML product.

Instead, the acceptance criteria are implicitly met through the demonstration of linear relationship and agreement between the modified device's algorithm and the predicate device's algorithm for StO2 measurements over a clinically meaningful range. The study is a pre-clinical agreement study conducted to support this substantial equivalence.

Here's the breakdown of the information based on your request, extracted from the provided text:

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

As discussed, specific numerical "acceptance criteria" and "reported device performance" in terms of clinical accuracy (e.g., sensitivity, specificity, or specific error bounds against a gold standard) are not explicitly stated in the provided 510(k) summary for the modified device. The document primarily focuses on demonstrating that the modified device's performance (specifically the new StO2 algorithm) is linearly related and in agreement with the predicate device's performance.

The implicit "acceptance criteria" for demonstrating substantial equivalence for the modified algorithm is that it should:
* Show a linear relationship with the predicate algorithm for relative oxyhemoglobin (HbO2) and deoxyhemoglobin (Hb) estimates (R^2 > 0.98).
* Show a linear relationship over a wide and clinically meaningful dynamic range of StO2.
* Allow for quantification of scale shift and bias using Bland-Altman plots, with an estimation of 95% levels of agreement (though the specific numerical agreement is not detailed in the summary).

Reported Device Performance (against the Predicate Device's Algorithm):

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

• Sample Size: 38 volunteer subjects.
• Data Provenance: Field data acquired (implies prospective data collection). No specific country of origin is mentioned, but the company address is Canada. The study involved a "forearm ischemia protocol," suggesting a controlled experimental setting rather than real-world patient data for diagnosis.

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)

This category is not applicable as the study did not establish a ground truth by human expert review in the traditional sense for an AI/ML diagnostic device. The study's purpose was to demonstrate agreement between the modified device's algorithm and the predicate device's algorithm. The ground truth, in this context, is the measurement provided by the predicate device (KD203).

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

This is not applicable. There was no human expert review or adjudication process described as the ground truth was derived from the predicate device's measurements.

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, a multi-reader multi-case (MRMC) comparative effectiveness study was not conducted or described. This device is an oximeter, not an AI-assisted diagnostic imaging tool that would typically involve human reader improvement. The study compared the device's algorithm performance to its predicate.

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

Yes, the performance reported is standalone (algorithm only). The study compared the StO2 measurements from the modified device (KD204) directly against the predicate device (KD203). The output of the device is a measurement (StO2, HbO2, Hb) and a color-coded image, not a diagnostic interpretation that typically involves human-in-the-loop assistance for clinical decision-making.

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

The "ground truth" for this agreement study was the measurements obtained from the predicate device (Kent Camera, Model KD203). The study's objective was to show that the modified device's StO2 algorithm produces results that are linearly related and agree with the predicate device over a clinically relevant range, essentially validating the new algorithm against the established (predicate) one.

8. The sample size for the training set

The document does not specify a sample size for a training set. The change is described as a "modified algorithm for calculating StO2" which "was implemented to increase the signal to noise ratio and provide better image quality." This implies an algorithmic refinement rather than a machine learning model that would typically require a separate, quantifiable training set. While algorithmic development often uses data, the document does not present it as a trained AI/ML model with a distinct training dataset.

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

Since a "training set" with established ground truth for an AI/ML model is not explicitly mentioned or the focus of the document, this question is not applicable in the context of the provided text. The modified algorithm presumably underwent internal development and validation, but the mechanism for establishing "ground truth" for its development is not detailed. The primary validation shown to the FDA is the agreement study against the predicate device.

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