The Laser Speckle Imaging System (RFLSI CZW) is intended for blood flow measurements in the micro-circulation. This device is intended for clinical research use.

The Laser Speckle Imaging System (RFLSI CZW) is intended for blood flow measurements in the micro-circulation. This device is intended for clinical research use. It is a measurement tool based on the laser speckle contrast analysis technology and provides real-time blood perfusion information of tissue and organs in a visual and quantitative way. The device is non-patient contacting and does not require the use of contrast agents.

The provided text describes the 510(k) premarket notification for the Laser Speckle Imaging System (RFLSI CZW) and its comparison to a predicate device. However, it does not contain detailed information about specific acceptance criteria and a study proving the device meets those criteria, particularly in the context of clinical performance or diagnostic accuracy. The document focuses on technological comparison, electrical safety, EMC, and laser safety testing.

Here's a breakdown of the information that is available and what is missing based on your request:

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

The document provides the following performance parameters for the subject device:

Note: The document states that "Differences in device parameters do not raise new concerns regarding safety and effectiveness" and that "Verification and validation testing for the subject device demonstrate safety and effectiveness." It also mentions "Comparison tests to verify the substantial performance of the device and the predicate device were conducted... and the results conclude that the device shows comparable performance, safety, and effectiveness to the predicate device." However, specific numerical acceptance criteria for comparability in these comparison tests are not explicitly stated.

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:
  • For the "laboratory testing model 'Flow Model' using a fluid simulator," the sample size is not specified.
  • For the "volunteer test on the human body using post-occlusive reactive hyperemia method," the sample size is not specified.
• Data Provenance: Not specified. The document indicates the applicant and correspondent are in China, but it doesn't state where the testing data originated.
• Retrospective or Prospective: Not specified.

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 information is not provided in the document. The performance tests described (flow model and volunteer test) likely use objective physical measurements rather than expert assessment for ground truth, but the details are missing.

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

This information is not provided.

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

This type of study is not mentioned in the document. The device is a Laser Speckle Imaging System for blood flow measurement, which typically outputs quantitative data directly, rather than images requiring human interpretation that AI might assist.

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

The performance testing described ("test with laboratory testing model 'Flow Model' using a fluid simulator" and "volunteer test on the human body") appears to evaluate the device's standalone performance in measuring blood flow. The document states that the testing demonstrated that "the device performs its intended purpose, and it meets the specified requirements and standards" and showed "comparable performance, safety, and effectiveness to the predicate device."

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

• For the "Flow Model" testing, the ground truth would likely be the known flow rates or parameters of the fluid simulator.
• For the "volunteer test on the human body using post-occlusive reactive hyperemia method," the ground truth would be established by the physiological changes induced by the post-occlusive reactive hyperemia method, which creates a temporary blood flow cessation followed by a reactive increase. The device's measurements would be compared against the expected physiological response. The precise method of establishing 'ground truth' for comparison during these physiological tests is not detailed, but it's not expert consensus, pathology, or outcomes data in the traditional sense for diagnostic accuracy.

8. The sample size for the training set

This information is not applicable/not provided. The device described is a measurement system and not an AI/ML device that typically requires a large training set in the way a diagnostic imaging algorithm might. The document does not mention any machine learning components that would necessitate training data.

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

This information is not applicable/not provided for the reasons stated in point 8.

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The SpectralMD™ DeepView™ Wound Imaging System 2.0 is an optical imaging device intended for studies of blood flow in the microcirculation. The DeepView system is suitable for a wide variety of clinical applications including plastic surgery, diabetes, dermatology, vascular surgery, wound healing, neurology, neurosurgery and anesthetics. In particular, it can be used for measuring perfusion of healthy and injured skin including burn wounds, skin flaps (plastic and reconstructive surgery), chronic wounds, decubitus ulcers and diabetic ulcers.

The DeepView System 2.0 is a prescription device that utilizes the principles of non-contact photoplethysmography (PPG) to capture images of tissue blood perfusion. This is accomplished by measuring the optical properties of tissues and blood as they vary in response to changing hemodynamic conditions. The device's software combines real-time digital analysis based on the interaction of light with vascular tissues below the skin's surface to produce 2-D color images on a touch-screen display depicting relative blood perfusion. The DeepView System consists of a Camera Head with LED optics, an Articulating Arm for Camera Head positioning, a Touch-Screen Display for image viewing, and for accessing and interacting with the Graphical User Interface (GUI). All components are integrated on a Mobile Cart that houses the hardware/software, uninterruptable power supply (UPS), and allows for transport between use environments. The DeepView System 2.0 is AC powered with a backup UPS, and is for use in healthcare/hospital facilities.

The provided text describes the SpectralMD DeepView Wound Imaging System 2.0 and its 510(k) submission for substantial equivalence to predicates.

Here's an analysis of the acceptance criteria and the study proving the device meets them, based on the provided document:

Crucially, the document states: "No clinical performance data were needed to support substantial equivalence." This means that the device's acceptance was not based on human reader studies, AI assistance, or the analysis of detailed clinical performance metrics like sensitivity, specificity, or AUC for diagnostic purposes. Instead, the focus was on demonstrating technological equivalence and safety.

Therefore, many of the typical criteria for evaluating AI/ML-based medical devices (especially those involving diagnostic performance improvement or standalone AI performance) are not applicable in this context, as the device is an imaging system measuring blood flow, not a diagnostic AI.

However, I can extract information related to the device's functional performance acceptance criteria and the engineering studies performed.

Acceptance Criteria and Reported Device Performance

The "acceptance criteria" here are framed around demonstrating equivalence to the predicate device in terms of fundamental functional performance related to blood flow detection and safety.

• Ability to identify the 2% alternating change (AC) modulation consistent with tissue-volume change from blood flow.
• Ability to identify AC modulations at various frequencies within human heart rate frequencies.
• Capability to detect fluid flow beneath the surface of an optically dense medium. These results "demonstrate that the DeepView System 2.0 performs in an equivalent manner to the original DeepView System (primary predicate)." |
  | Equivalent Frequency Detection of Pulsatile Flow | (See above) Demonstrated ability to identify AC modulations at various frequencies within human heart rate range. |
  | Equivalent Capability under Simulated Physiological Conditions | (See above) Demonstrated ability to detect fluid flow beneath the surface of an optically dense medium. |
  | Electrical Safety | Met AAMI/ANSI ES60601-1:2005/(R) 2012 & A1:2012; IEC 60601-1:2005 (Third Edition) + CORR. 1:2006 + CORR. 2:2007. |
  | Electromagnetic Compatibility (EMC) | Met IEC 60601-1-2 (2007)/(R) 2012. |
  | Human Factors and Usability Engineering Compliance & Validation | Designed "in accordance with FDA guidance on human factors and usability engineering" and "subjected to usability testing validation." |

Study Details (Based on the provided text)

Given the stated "No clinical performance data were needed," the "study" is primarily a series of engineering and bench testing studies to demonstrate functional and safety equivalence, not a traditional clinical trial.

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

   • Test Set Sample Size: Not specified in terms of number of "cases" or "patients" as this was not a clinical performance study. The tests likely involved physical phantoms or controlled bench setups to simulate blood flow conditions.
   • Data Provenance: Not applicable in the sense of patient data from specific countries. This was likely laboratory/bench testing.
   • Retrospective/Prospective: Not applicable in a clinical sense. These were controlled engineering experiments.

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

   • Not applicable. The ground truth for these engineering tests would be derived from the controlled experimental setup (e.g., known fluid flow rates, known material properties of optically dense media, precise electrical and EMC standards). No human "experts" establishing "ground truth" in terms of clinical interpretation were needed for these specific tests.

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

   • Not applicable. This type of adjudication is for clinical ground truth establishment, which was not performed here. The "adjudication" would be based on instrument readings and engineering standards.

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. The document explicitly states: "No clinical performance data were needed to support substantial equivalence." The device is an imaging system, and its software provides "specific wound modules for facilitating patient/wound documentation," but it's not described as having an AI component that assists human interpretation for a diagnostic outcome that would be subject to an MRMC study.

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

   • No standalone AI performance study was done in the diagnostic sense. The "algorithm" here processes light interaction with tissue to produce 2D perfusion images. The performance assessed was its ability to detect pulsatile flow equivalently to the predicate device, not its ability to make a diagnostic determination on its own.

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

   • The "ground truth" for the functional tests was engineered and controlled experimental conditions (e.g., known AC modulation percentages, known frequency inputs, known optical properties of test media). For safety tests, it was adherence to recognized international electrical safety and EMC standards.

7. The sample size for the training set:

   • Not applicable. The document describes a traditional medical device submission based on predicate equivalence, not an AI/ML device that requires a separate training set for algorithm development. While there's a "Proprietary Software Algorithm" for data analysis, there's no mention of a machine learning training phase or associated dataset.

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

   • Not applicable. As no training set was described for an AI/ML model, no ground truth establishment for such a set is relevant here.

In summary, the DeepView System 2.0's acceptance was based on demonstrating technical and functional equivalence to its predicate device through bench and engineering testing, alongside adherence to safety and usability standards. It was not cleared as an AI-enabled diagnostic device requiring clinical performance studies with human readers or standalone AI performance metrics.

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The moorLDI2 Laser Doppler Imager is intended for blood flow measurements in the microcirculation. It is intended to be used for clinical research applications and pre-clinical research applications.

The moorLDI2 is a device to perform non-invasive blood flow measurements in the microcirculation, for example skin, using the established laser Doppler technique to quantify movement of blood cells beneath the skin surface. The system scans a very low power laser beam across the tissue. Moving blood in the microvasculature causes a Doppler frequency shift of the laser light, which is photo detected and processed to generate a colour coded blood flow map, line by line. An in built CCD camera records a colour photograph to aid visualisation of the scan site.

The Moor Instruments Ltd moorLDI2 Laser Doppler Imager is a device intended for blood flow measurements in the microcirculation for clinical and pre-clinical research applications. The 510(k) summary provides information regarding its performance and comparison to a predicate device (moorLDI2-IR laser Doppler imager, K032841).

1. Table of Acceptance Criteria and Reported Device Performance:

The document doesn't explicitly state "acceptance criteria" in a pass/fail sense for a clinical study comparing the new device against specific benchmarks. Instead, it focuses on demonstrating substantial equivalence to the predicate device. The performance characteristics of the device itself are given.

2. Sample Size Used for the Test Set and Data Provenance:

The document states: "The moorLDI2 laser Doppler imager was tested in direct comparison to the predicate device using laboratory models and skin blood flow measurements on volunteers."

• Sample Size: Not explicitly provided. The number of laboratory models or volunteers used is not specified.
• Data Provenance: The study involved "laboratory models" and "skin blood flow measurements on volunteers." The country of origin for the data is implicitly the United Kingdom, where Moor Instruments Ltd is based. The data would be considered prospective as it involves new testing for the purpose of this submission.

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

This information is not provided in the document. The type of testing performed (laboratory models, skin blood flow measurements) suggests objective measurements rather than subjective expert consensus for ground truth establishment.

4. Adjudication Method for the Test Set:

This information is not applicable as the document describes direct comparison testing using objective measurements of performance characteristics rather than an expert-adjudicated test set in the traditional sense.

5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study:

An MRMC study was not conducted or described. The device is a measurement instrument, and the study focused on technical performance comparison rather than human reader interpretation with and without AI assistance.

6. Standalone Performance:

Yes, a standalone performance evaluation was done. The document provides detailed specifications and performance parameters like "Accuracy" for Flux, Concentration, and DC (Intensity), stating these accuracies relative to a standard. The study also concludes that the device performs "as well as or better than the predicate device," which implies standalone performance evaluation of the new device and its comparison to the predicate.

7. Type of Ground Truth Used:

The ground truth appears to be based on objective physical measurements from "laboratory models" and "skin blood flow measurements on volunteers." For parameters like Flux, Conc, and DC, accuracy is given relative to "Moor Instruments 'standard' moorLDI" and a "temperature controlled (22 ±1°C) motility standard." This suggests a reliance on established measurement standards and controlled experimental conditions to define the true values.

8. Sample Size for the Training Set:

This information is not provided and is likely not applicable. The device is not described as an AI/ML algorithm that requires a training set in the conventional sense. It's a measurement instrument, and its development would involve engineering design and calibration rather than AI model training.

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

This information is not provided and is not applicable, as there is no mention of an AI/ML component requiring a training set with established ground truth.

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The probe is for use with the Deltex Medical CardioQ-EDM and CardioQ-EDM+ for Monitoring of cardiac output and fluid status. The probe is only approved for oral placement into the esophagus of a single anesthetized patient 15 years of age or younger, 50cm (20") to 170cm (67") in height.

The Deltex Medical Ltd KDP72 Pediatric Doppler Probe is an oral extravascular blood flow probe designed to work with the CardioQ-EDM and CardioQ-EDM+ Systems (K111542 and K132139 respectively). It consists of a shaft, which is a spring reinforced silicone tube, with an electrical connector on the machine end and an ultrasonic transmitting and receiving tip on the patient end. The tip is fully covered and sealed to the shaft with a silicone rubber boot and by wires running through the shaft to the connector. Visual product identification is provided at the machine end and the device is provided single packed sterile for single patient use.

The provided document is a 510(k) summary for the Deltex Medical KDP72 Doppler Probe, asserting its substantial equivalence to previously cleared devices. It describes the device, its intended use, and performance data related to its design and safety standards, but does not contain information about a study proving the device meets specific acceptance criteria related to its clinical performance as a diagnostic tool for cardiac output and fluid status.

The "Performance Data" section briefly mentions:

1. "The performance data recommended in 'Information for Manufacturers Seeking Marketing Clearance of Diagnostic Ultrasound Systems and Transducers,' issued on September 9, 2008, has been included." This suggests that general requirements for diagnostic ultrasound devices were addressed, likely related to acoustic output and image quality, not the accuracy of cardiac output measurements.
2. "Additionally a flexibility test has been conducted on the subject and predicate devices which demonstrates the comparative flexibility." This is a mechanical performance test, not a clinical diagnostic performance study.

Therefore, many of the requested categories for acceptance criteria and a study to prove they are met cannot be extracted from this document, as the document focuses on demonstrating substantial equivalence based on design, materials, sterilization, biocompatibility, and electrical safety standards rather than clinical diagnostic accuracy.

However, based on the provided text, here’s what can be extracted:

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

2. Sample size used for the test set and the data provenance:
Not specified in the document for any clinical performance or diagnostic accuracy study. The document mentions a flexibility test, but details on sample size or provenance are not provided.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts:
Not applicable/not specified. The document does not describe a study involving expert-established ground truth for clinical diagnostic performance.

4. Adjudication method for the test set:
Not applicable/not specified.

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. This device is a probe for a cardiac output monitor, not an AI-assisted diagnostic imaging system that involves human readers.

6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done:
Not specified. The document refers to "performance data recommended in 'Information for Manufacturers Seeking Marketing Clearance of Diagnostic Ultrasound Systems and Transducers'" but does not detail what kind of study was performed or if it was standalone. The device is a probe to be used with a monitoring system, implying a system performance, not a standalone algorithm.

7. The type of ground truth used:
Not specified for diagnostic accuracy. For the engineering criteria (sterilization, biocompatibility, etc.), the ground truth is established by adherence to the respective international standards (e.g., ISO, IEC).

8. The sample size for the training set:
Not applicable/not specified. No information on an algorithm training set is provided; this is a medical device (probe) for measurement, not an AI/ML diagnostic algorithm.

9. How the ground truth for the training set was established:
Not applicable/not specified.

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The Bio-Probe blood flow monitoring system is to be used with an appropriate model Bio-Console extracorporeal blood pumping console to measure directly the blood flow in the extracorporeal perfusion circuit.

The Bio-Probe 10 Blood Flow Monitoring System consists of a flow transducer and a sterile, single-use insert. The flow transducer consists of a flow-meter, cable and connector. The TX50 (adult) and TX50P (pediatric) transducer models are reusable. The Bio-Probe blood flow monitoring system can be used to measure the patient blood flow during the extracorporeal procedure.

The provided document is a 510(k) summary for the Bio-Probe Blood Flow Transducer. This particular submission concerns the addition of a contraindication statement to the device.

Therefore, the document explicitly states that "Testing was not required for addition of a contraindication statement. Addition of the contraindication statement does not change the indications for use, technology and performance specifications of this device." This means there is no study described in this document proving the device meets acceptance criteria, as the submission is not about performance.

Here's the breakdown based on your request, highlighting the lack of relevant information for a performance study in this specific submission:

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

• Acceptance Criteria: Not applicable/Provided.
• Reported Device Performance: Not applicable/Provided.

Explanation: This 510(k) is solely for adding a contraindication statement, not for demonstrating new performance or design changes.

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

• Sample Size (Test Set): Not applicable.
• Data Provenance: Not applicable.

Explanation: No testing was performed for this submission.

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)

• Number of Experts: Not applicable.
• Qualifications of Experts: Not applicable.

Explanation: No ground truth establishment was needed as no performance testing was conducted.

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

• Adjudication Method: Not applicable.

Explanation: No adjudication was needed as no performance testing was conducted.

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

• MRMC Study: No.
• Effect Size: Not applicable.

Explanation: This device is a blood flow transducer, not an AI-assisted diagnostic or interpretation device that would involve human readers.

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

• Standalone Performance: Not applicable.

Explanation: This device is a physical transducer for measuring blood flow, not an algorithm.

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

• Type of Ground Truth: Not applicable.

Explanation: No ground truth was established as no performance testing was conducted.

8. The sample size for the training set

• Training Set Sample Size: Not applicable.

Explanation: This submission does not involve an algorithm or machine learning that would require a training set.

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

• Ground Truth Establishment (Training Set): Not applicable.

Explanation: No training set or ground truth for it was established.

In summary, this 510(k) submission is a regulatory update for an existing device (Bio-Probe Blood Flow Transducer, predicate device K070286) to add a contraindication statement. It explicitly states that no testing was required or performed because the change does not impact the device's indications for use, technology, or performance specifications. Therefore, information regarding acceptance criteria and performance study details is not present in this document.

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The moorLDLS-BI laser Doppler burns imager assesses the blood flow in burn wounds of the skin, when cleaned of surface debris, to aid in the clinician's assessment of burn wound healing potential. It is intended to be used as an aid to burn wound management for patients with Total Body Surface Area burn of up to 30%.

The device is intended to be used as an aid to burn wound assessment, and not as a stand-alone prediction device.

The moor DLS-BI laser Doppler burns imager is an imaging device to aid the clinician to judge the healing potential of bums and the need for surgery.

It uses the laser Doppler imaging technique to quantify the blood flow in an area of skin damaged by a burn. The device uses a line of laser light projected onto the tissue and a linear detector arrav that sample from the line as it is swept across the tissue to rapidly build up a colour coded image of blood flow in the burn area and the surrounding normal skin for healing potential prediction. In addition, a CCD camera is integrated into the scanner unit for recording a colour photograph at the time of scanning, corresponding closely with the blood flow image in size and aspect.

The moorLDLS-BI laser Doppler burns imager is a substantial equivalent to the predicate device moorLDI2-B1 and assesses burn wound healing potential.

Here's an analysis of the acceptance criteria and the study that proves the device meets them:

1. Table of Acceptance Criteria and Reported Device Performance

The acceptance criteria are implicitly defined by the clinical investigation's objectives and the comparative analysis against the predicate device. The primary performance metrics are accuracy in predicting healing potential and agreement with the predicate device.

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

• Sample Size for Test Set: 596 burn cases for 204 burn patients.
• Data Provenance: Multi-center clinical investigation conducted in five burns centers across various countries: 2 in the UK, 1 in the USA, 1 in Belgium, and 1 in Australia. The study was prospective in nature, as it was a "Clinical Investigation" designed to assess the performance of a new device.

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

The document does not explicitly state the number of experts used to establish the ground truth or their specific qualifications (e.g., radiologist with X years of experience).

However, given the nature of "healing records" being the ground truth and the device's intended use as an aid to "clinician's assessment of burn wound healing potential" by "burn surgeons," it is highly probable that the ground truth was established by burn care clinicians/surgeons responsible for patient management and outcome tracking.

4. Adjudication Method for the Test Set

The document does not explicitly state the adjudication method used for establishing the ground truth from "healing records."

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study was Done

• No, a MRMC comparative effectiveness study was not explicitly done for human readers. The study focused on comparing the performance of the device (moorLDLS-BI) against the predicate device (moorLDI2-BI) and against healing records, not on comparing human reader performance with and without AI assistance.
• Effect size of human reader improvement: Not applicable, as this type of study was not conducted as described. The device is intended as an "aid to burn wound management," implying human oversight, but the study directly compares device performance, not human-AI synergy.

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

• Yes, a standalone performance assessment was done. The "overall accuracy of 94.2% was found for moorLDLS-Bl when compared with healing records" represents the device's standalone performance in predicting healing potential.
• It's important to note that while the device has standalone accuracy, its intended use statement explicitly says, "It is intended to be used as an aid to burn wound assessment, and not as a stand-alone prediction device," indicating that clinical interpretation by a human is still required.

7. The Type of Ground Truth Used

• Outcomes Data (Healing Records): The primary ground truth for the overall accuracy of the moorLDLS-BI was based on "healing records." This refers to the actual observed healing time of the burn wounds, which is a direct patient outcome.

8. The Sample Size for the Training Set

The document does not explicitly state the sample size for a training set. The description mentions a "clinical investigation" and performance data, but does not distinguish between a training set and a testing set in the context of machine learning model development. This suggests the moorLDLS-BI, while a new device, might not be heavily reliant on a trainable "algorithm" in the modern AI sense, but rather on a refined physical measurement principle and associated processing algorithms for which the mentioned clinical investigation serves as a validation/test set.

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

Since a distinct "training set" is not mentioned or detailed, the method for establishing its ground truth is also not described. If any internal development process involved a training phase, that information is not part of this 510(k) summary.

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The Spectral MD DeepView system is intended for studies of blood flow in the microcirculation. The DeepView system is suitable for a wide variety of clinical applications including plastic surgery, diabetes, dermatology, vascular surgery, wound healing, neurology, physiology, neurosurgery and anesthetics.

Deep View system-based technology combines real-time digital analysis of optical signatures, thereby sensitizing an imager to photon-tissue interactions deep below the skin's surface. These image signatures are unique to the body and relate directly to a person's dynamic nature - both in terms of the quantity and quality of important physiological properties. This technology is non-invasive and uses no harmful radiation such as X-rays and allows clinical investigators to look deeper into the body, delivering images of blood flow under the skin's surface without ever touching the patient.

The DeepView system is composed of a mobile cart with uninterruptible power supply, a laptop computer with remote multimedia keyboard, an LCD screen mounted on a bracket that allows for side-to-side panning, a mechanical arm, a CMOS camera with DSP electronics, and disposable LED cartridges with an associated LED driver control board.

The provided document describes the DeepView Digital Video Physiological Portable Imaging System, a device intended for studies of blood flow in the microcirculation. It focuses on demonstrating substantial equivalence to predicate devices rather than establishing novel acceptance criteria and proving performance against them in a de novo study.

Here's an analysis based on the available information:

1. Table of Acceptance Criteria and Reported Device Performance

The document does not explicitly define acceptance criteria in terms of specific performance metrics (e.g., sensitivity, specificity, accuracy, or quantitative blood flow measurements) that the DeepView device needed to meet. Instead, the study's goal was to demonstrate substantial equivalence to existing predicate devices.

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

• Sample Size: Not specified. The document mentions "comparison testing" which included "camera distance testing and tissue phantom testing." This implies the use of a controlled test environment (tissue phantoms) rather than human subjects. No specific number of phantoms or test cases is provided.
• Data Provenance: The testing appears to be conducted in a laboratory setting using "tissue phantoms." There is no mention of human data, country of origin, or whether it was retrospective or prospective.

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

Not applicable in the context of this submission. The ground truth for the comparison testing seems to be the performance of the predicate devices themselves, as the DeepView's output was compared to theirs. There's no indication of independent expert review to establish a separate "ground truth" for the test set.

4. Adjudication Method for the Test Set

Not applicable. There is no mention of human-in-the-loop assessment or expert adjudication for the "comparison testing." The comparison was against the output of the predicate devices.

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 involving human readers and AI assistance was not conducted or reported. This submission focuses on the standalone device's equivalence to existing technology, not on improving human reader performance.

6. If a Standalone (i.e., algorithm only without human-in-the-loop performance) was Done

Yes, the testing described appears to be a standalone performance evaluation of the DeepView device against the predicate devices. The "comparison testing" suggests the DeepView's output was directly compared to the outputs of the moorLDI, moorLDI2-IR, and Avant 9600. The device's ability to "detect blood flow and pulse pressure" and produce "2D color images of relative perfusion distribution" constitutes its standalone performance.

7. The Type of Ground Truth Used (expert consensus, pathology, outcomes data, etc.)

The "ground truth" for the comparison testing was effectively the performance of the predicate devices. The DeepView was evaluated for its ability to produce similar results (detect blood flow, pulse frequency, and produce flow images) to the already legally marketed and accepted predicate devices (moorLDI, moorLDI2-IR, and Avant 9600). For the tissue phantom testing, the "ground truth" would implicitly be the known properties of the phantoms and the expected measurements, as validated by the predicate devices.

8. The Sample Size for the Training Set

Not applicable. As this is a 510(k) premarket notification for a device using established optical principles, there is no mention of an "AI algorithm" requiring a training set in the contemporary sense. The device's operation is based on "real-time digital analysis of optical signatures" and "non contact Photoplethysmography (PPG)," which points to signal processing rather than machine learning models that require training data.

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

Not applicable, as there is no mention of a training set or an AI algorithm that would require one.

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The Aïmago EasyLDI Microcirculation Camera is intended for blood flow measurements in the microcirculation. In particular, it can be used for measuring perfusion of healthy and injured skin including burn wounds, skin flaps (plastic and reconstructive surgery) and hand surgery.

EasyLDI Studio is intended to be used as offline viewer application for snapshots, videos and references recorded with the Aïmago EasyLDI Microcirculation Camera.

The Aïmago EasyLDI microcirculation camera is a device for imaging blood flow in the microcirculation. It is a medical diagnostic imaging device which serves to visualize the perfusion of cutaneous microcirculation in the form of arbitrary units in real-time. The EasyLDI uses the established laser Doppler technique performing a 2-dimensional area scan to build up a color coded image of the blood flow in the tissue. In the form of arbitrary units, this image allows the surgeon to quantify movement of blood cells beneath the skin surface.

The software changes implemented in the Aïmago EasyLDI microcirculation camera V2.X allow the user different modes of displaying the information on the built-in screen, thus facilitating the assessment of the microcirculation patterns for the specified applications.

EasyLD! Studio is a standalone software which runs on Windows systems. It is an optional accessory to the Aïmago EasyLDI microcirculation camera. It can be used to view LDI items (i.e. LDI snapshots, videos or references previously recorded with the Aïmago EasyLDI) on a commercially available desktop computer.

The provided text describes a 510(k) summary for the Aïmago EasyLDI Microcirculation Camera and its accessory, EasyLDI Studio. It focuses on demonstrating substantial equivalence to a predicate device rather than presenting a study with specific acceptance criteria and detailed device performance metrics in the way a clinical trial would.

Therefore, many of the requested details about acceptance criteria, sample sizes, expert involvement, and ground truth establishment are not present in this type of regulatory submission. This submission primarily relies on comparing technological characteristics and demonstrating safety and effectiveness based on in-house and contract laboratory testing for regulatory compliance (e.g., FCC, IEC standards).

Here's a breakdown of what can be extracted and what is not available based on the provided text:

Acceptance Criteria and Device Performance

The document does not specify performance-based acceptance criteria in the typical sense of a clinical or functional study (e.g., sensitivity, specificity, accuracy for a particular clinical outcome). Instead, "acceptance criteria" here refer to regulatory compliance and equivalence:

Study Details

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

• Sample Size: Not specified for any performance testing related to clinical application. The testing mentioned refers to regulatory compliance tests (e.g., FCC, IEC), for which sample sizes for device hardware/software are typically small and not relevant to clinical data.
• Data Provenance: Not applicable in the context of clinical data. The testing mentioned ("performed in-house as well as at contract laboratories") is for regulatory compliance, not clinical data.

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

• Not applicable. No ground truth for a test set in a clinical context is mentioned. Clinical assessments are explicitly stated to be "aid to healthcare professionals," and the device "do not provide specific clinical assessments such as burn depth assessments or potential healing times."

4. Adjudication method for the test set

• Not applicable. No clinical test set or adjudication process is described.

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 or reported. This device precedes the widespread use of clinical AI assistance in this context. The software changes are described as facilitating assessment, but no studies on reader improvement are mentioned.

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

• The device's core function is to visualize and measure blood flow, which is then interpreted by a human professional. The "new software applications are intended only as an aid to healthcare professionals in their clinical assessments." This implies a human-in-the-loop scenario. No standalone algorithm performance without human interpretation is described for clinical outcomes. The device itself is a standalone imaging device, but its utility for "assessment" relies on the human user.

7. The type of ground truth used

• Not applicable for clinical efficacy. The "ground truth" for the regulatory compliance testing would be established by the standards themselves (e.g., an ESD test either passes or fails according to IEC 60601-1-2 criteria). The device's output is "arbitrary units" of blood flow, which doesn't directly map to a "ground truth" in the sense of a definitive diagnosis or pathology.

8. The sample size for the training set

• Not applicable. This device is cleared based on predicate equivalence and compliance with engineering standards, not through machine learning or AI model development that would require a training set.

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

• Not applicable, as no training set for a machine learning model is mentioned.

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The moorFLPI-2 Full-Field Laser Perfusion Imager is intended for blood flow measurements in the microcirculation. This device is intended for clinical research use.

The moorFLPI-2 is a device to perform non-contact imaging of tissue blood perfusion in the microcirculation, for example skin, using speckle contrast analysis. The tissue surface is illuminated with a diverging infra-red laser beam resulting in a laser speckle pattern. The pattern is imaged by a CCD camera and image processing of the speckle contrast is used to generate colour coded images of the tissue blood perfusion in the microcirculation.

The provided text describes the moorFLPI-2 Full-Field Laser Perfusion Imager, a device intended for non-contact imaging of tissue blood perfusion in the microcirculation for clinical research use. It focuses on demonstrating substantial equivalence to a predicate device (moorFLPI Full-Field Laser Perfusion Imager, K063586).

Here's an analysis of the provided information regarding acceptance criteria and the study:

1. Table of Acceptance Criteria and Reported Device Performance

The document does not explicitly state quantitative acceptance criteria or detailed performance metrics. Instead, it relies on a qualitative comparison to the predicate device.

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

• Test Set Sample Size: The document does not specify a numerical sample size for the "test set." It mentions "laboratory models and skin blood flow measurements on volunteers." The number of volunteers or specific lab models used is not provided.
• Data Provenance: The study was conducted by Moor Instruments Ltd, located in the United Kingdom. It appears to be a prospective comparison study as it describes testing the moorFLPI-2 directly against the predicate device.

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

This information is not provided in the document. The study focuses on direct device comparison rather than relying on expert-established ground truth for a diagnostic outcome.

4. Adjudication Method for the Test Set

This information is not provided. Given the nature of a direct device comparison (measuring blood flow), a traditional expert adjudication method for a test set of cases is unlikely to be applicable in the same way as for diagnostic imaging.

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. This device is a measurement instrument for blood flow, not an AI-powered diagnostic tool that assists human readers.

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

The moorFLPI-2 is a standalone device ("Full-Field Laser Perfusion Imager") that performs measurements. The study described compares its performance to another standalone device (the predicate). Therefore, the study, by its nature of comparing two instruments, can be considered evaluating its standalone performance in relation to the predicate. However, it's not an "algorithm only" in the sense of a software-based AI system; it's a hardware device with integrated signal processing.

7. The Type of Ground Truth Used (expert consensus, pathology, outcomes data, etc.)

The concept of "ground truth" in the diagnostic sense (e.g., pathology for a tumor) is not directly applicable here. The study established "ground truth" through direct comparison to the predicate device's measurements on the same laboratory models and volunteers. The predicate device (moorFLPI) itself implicitly serves as the "reference standard" or "ground truth" for the new device's performance, as the goal was to demonstrate equivalence.

8. The Sample Size for the Training Set

This information is not applicable or not provided. The moorFLPI-2 is a measurement device that uses speckle contrast analysis, not a machine learning or AI algorithm that requires a "training set" in the conventional sense. Its underlying physics and signal processing are based on established principles, rather than learning from a large dataset.

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

This information is not applicable as there is no "training set" for an AI algorithm.

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