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The MIMOSA Imager is intended to non-invasively estimate the spatial distribution of percent oxygen saturation (StO2) in a volume of tissue. This is performed in medical environments including physician offices, hospitals, ambulatory care and Emergency Medical Services. The MIMOSA Imager is indicated for use in monitoring patients during circulatory or perfusion examinations of skeletal muscle or when there is a suspicion of compromised circulation.

The MIMOSA Imager is a non-contacting, cordless, battery powered device that non-invasively estimates the percent oxygen saturation (StO2) in a volume of tissue. The device captures spatially-resolved images that is triggered by the end user via a smartphone-app interface. By tracking the spectral signatures of dominant chromophores in the patient's superficial tissue, the device calculates and displays the StO2 estimate on the connected android device screen.

The MIMOSA Imager is intended for use by healthcare professionals as a non-invasive tissue oxygenation measurement system that maps the tissue oxygen saturation (StO2) values to a spatially registered heatmap. The MIMOSA Imager shares fundamental principles with other oximeters and tissue oxygenation measurement systems. Tissue oximetry exposes tissue to optical radiation of known wavelengths and captures the remitted or reflected light. The remitted back scattered light is then used to calculate StO2 based on principles of multispectral imaging. Spectral analysis is used to measure StO2 using specific both visible (VIS) and near-infrared (NIR) LED-illuminated wavelengths. Other systems that also measure oxygenation levels in superficial tissue may use only VIS or NIR wavelengths.

The provided document is a 510(k) Premarket Notification summary for the MIMOSA Imager, which is a medical device intended to non-invasively estimate the spatial distribution of percent oxygen saturation (StO2) in tissue. The document details the device's description, indications for use, comparison to a predicate device, and performance testing.

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

Acceptance Criteria and Reported Device Performance

The acceptance criteria are not explicitly stated in a table format with specific numerical targets. Instead, the document focuses on demonstrating substantial equivalence to a predicate device (ViOptix ODISsey Tissue Oximeter) through a comparative clinical study. The primary metric for this equivalence appears to be the statistical agreement of StO2 measurements between the two devices.

Study Details

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

   • Sample Size: 39 individuals.
   • Data Provenance: The document does not explicitly state the country of origin. It describes the study as "clinical testing," which implies it was a prospective study where data was collected specifically for this comparison.

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

   • The concept of "ground truth" in this context is established by the measurements of the predicate device (ViOptix ODISsey Tissue Oximeter), which is legally marketed and considered a benchmark for StO2 estimation.
   • No independent experts were used to establish a separate "ground truth" for the StO2 values themselves, as the study was comparative against an established device. The clinical study seems to have involved medical professionals ("healthcare professionals" as stated for device usage) who conducted the vascular occlusion test and measurements, but their number and specific qualifications for establishing ground truth are not detailed.

3. Adjudication method for the test set:

   • No adjudication method (like 2+1 or 3+1) is mentioned, as the study's goal was to compare the MIMOSA Imager's measurements directly against those of the predicate device, not to adjudicate discrepancies between human readers or between a human and an AI.

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:

   • No MRMC study was done. This device is a measurement tool (oximeter), not an AI-assisted diagnostic imaging device for human interpretation. The study focused on the device's direct measurement performance compared to a predicate device.

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

   • The clinical study was a standalone device performance study in the sense that the MIMOSA Imager's measurements were directly compared against the predicate device's measurements. The device generates the StO2 map, and its performance (accuracy/agreement) is assessed as an output of the device itself. Human interaction is for operating the device and interpreting the display, but the core measurement is automated by the device's algorithms.

6. The type of ground truth used:

   • The "ground truth" for the comparison was the measurements provided by the legally marketed predicate device (ViOptix ODISsey Tissue Oximeter) under controlled conditions (vascular occlusion test). This serves as a "clinical reference standard" rather than an independent expert consensus, pathology, or outcomes data.

7. The sample size for the training set:

   • The document does not provide details on a separate "training set" or its size. As this is an oximeter, its underlying principles are based on spectrophotometry and known biological chromophores, rather than a machine learning model that requires a dedicated training set in the typical AI/ML sense. The device calculates StO2 based on spectral analysis. Any internal calibration or algorithm development (if using something akin to statistical models) would presumably rely on fundamental physics and potentially pre-clinical or simulated data, not explicitly described as a "training set" in the document.

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

   • Given that no specific "training set" in the context of machine learning is identified, the method for establishing its ground truth is not applicable from the provided text. The device's StO2 estimation relies on principles of multispectral imaging and spectral analysis, with "bench data" demonstrating acceptable accuracy against "clinical/industrial gold standard" which likely refers to established physical or chemical standards for light absorption/reflection by oxygenated/deoxygenated blood.

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The Ox-Imager CS™ is indicated for use to determine oxygenation levels in superficial tissues for patients with potential circulatory compromise.

The Ox-Imager CSTM is intended for use by healthcare professionals as a noninvasive tissue oxygenation measurement system that maps value of: oxygen saturation, oxy-hemoglobin, and deoxy-hemoglobin into 2D/3D visual presentations. The Ox-Imager™ CS is a non-contact imaging device to visualize spatially-resolved optical and functional parameters of biological tissue. The Ox-Imager CS shares fundamental principles with other oximeters and tissue oxygenation measurement systems. Spectral analysis is used to measure oxygen saturation (StO2), oxyhemoglobin (HbO2), deoxyhemoglobin (HbR) and determine tissue optical properties (absorption and scattering) using specific LED wavelengths and patterns. The Ox-Imager CS uses both visible (VIS) and near infrared (NIR) wavelengths; other systems that also measure oxygenation levels in superficial tissue may use only VIS or NIR wavelengths. Tissue oximetry exposes tissue to optical radiation of known wavelengths and captures the remitted light or reflectance. The remitted back scattered light is then used to calculate the tissue constituents mentioned above based on principles of multispectral imaging and Spatial Frequency Domain Imaging (SFDI).

The provided document is a 510(k) summary for the Ox-Imager CS device, seeking clearance based on substantial equivalence to a predicate device, the Hypermed, Inc. OxyVu-1 System. This type of document typically focuses on demonstrating equivalence rather than establishing new acceptance criteria or providing in-depth performance studies with detailed statistical analysis against pre-defined thresholds.

Therefore, the information available is primarily focused on demonstrating equivalence and device functionality rather than specific acceptance criteria thresholds for a standalone performance study.

Here's an analysis based on the provided text:

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

The document does not explicitly state quantitative acceptance criteria (e.g., specific accuracy thresholds, sensitivity, specificity) for the Ox-Imager CS device's performance. Instead, the performance studies aim to demonstrate substantial equivalence to the predicate device and show that the device measures physiological changes as expected.

The key performance finding is:
"It was demonstrated that the predicate OxyVu-1 and the Ox-Imager CS recover highly correlated values of StO2 during the time course of a vascular occlusion test and that both devices measured a statistically significant decrease in tissue oxygen saturation (StO2) after circulatory compromise."

This implies a qualitative "acceptance criterion" of:

• Highly correlated values of StO2 between the Ox-Imager CS and the predicate OxyVu-1 during a vascular occlusion test.
• Statistically significant decrease in tissue oxygen saturation (StO2) measured by both devices after circulatory compromise.

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: The document does not specify the exact sample size (number of subjects/cases) for any of the clinical, pre-clinical, or benchtop studies. It mentions "a clinical study," "a pre-clinical study in rabbits," and "a blood phantom desaturation study."
• Data Provenance: The document does not specify the country of origin for the data or whether the studies were retrospective or prospective, although clinical and pre-clinical studies are typically prospective.

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

This section is not applicable in the traditional sense for this device. The "ground truth" for an oximeter is primarily physiological measurements. The studies do not describe a process of expert review or consensus for establishing ground truth, as would be common in image-based diagnostic AI. Instead, the ground truth is established through:

• Physiological changes: Vascular occlusion (inducing circulatory compromise) acts as a physiological "ground truth" stimulus.
• Reference measurements: Co-oximeter values (SaO2/SvO2 from blood draws) and transcutaneous oxygen measurements (tcpO2) serve as reference "ground truth" data points against which the Ox-Imager CS measurements are correlated.
• Expected values: For the blood phantom study, the pO2/StO2 curves were compared to "expected StO2 values."

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

Not applicable. As noted above, the "ground truth" is based on physiological changes and objective reference measurements, not expert review requiring adjudication.

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, an MRMC comparative effectiveness study was not done. The Ox-Imager CS is described as a measurement device that provides objective values and images of oxygenation levels, not an AI-assisted diagnostic tool that aids human readers in interpreting images. Therefore, the concept of "human readers improve with AI vs without AI assistance" does not apply to this device's reported evaluation.

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

Yes, the studies evaluate the standalone performance of the Ox-Imager CS device as a measurement tool. For example:

• The comparison between Ox-Imager CS and OxyVu-1 during vascular occlusion.
• The correlation of Ox-Imager CS StO2 with pO2 in blood phantoms.
• The correlation of Ox-Imager CS StO2 with co-oximeter values (SaO2/SvO2) in rabbits.
• The comparison of Ox-Imager CS StO2 with tcpO2 in a clinical study.

These are all assessments of the device's inherent measurement capabilities.

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

The ground truth for evaluating the Ox-Imager CS was based on:

• Physiological manipulation: Inducing a state of circulatory compromise (vascular occlusion) to predictably change tissue oxygenation.
• Reference standards/measurements:
  • The predicate device (Hypermed OxyVu-1) was used for direct comparison of StO2 values.
  • Co-oximeter values (SaO2/SvO2 from blood draws in the rabbit study).
  • Transcutaneous oxygen measurements (tcpO2 in one clinical study).
  • Expected StO2 values derived from pO2 curves for the blood phantom study.

8. The sample size for the training set

The document does not describe a "training set" as would be relevant for a machine learning or AI model. The Ox-Imager CS uses "spectral analysis" and "model-based analysis of light" based on principles of multispectral imaging and Spatial Frequency Domain Imaging (SFDI). These are physics-based models rather than data-driven machine learning models that require a distinct training set. The device's "training" would be more akin to software calibration or model development based on established optical properties and physiological principles, rather than a dataset of labeled clinical images.

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

Not applicable, as there is no mention of a traditional "training set" with ground truth labels in the context of machine learning. The device's underlying models are based on physical and biological principles.

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