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The LymphaTech Mobile 3D Measuring Tool is a software application that uses input from the Structure Sensor, an off-the-shelf long-wave infrared camera, to measure the diameter, surface area, volume, and perimeter/circumference of a part of the body. The 3D Measuring Tool is non-contact with respect to the patient and provides an adjunctive tool to help a qualified health care professional measure and record body part data. The device uses input from the Structure Sensor camera to accurately capture and construct a 3D model of a patient's anatomy. It is intended for trained and qualified healthcare professionals, who are trained in its use. The 3D Measuring Tool is to be used on a patient population that includes non-pregnant female or male adults. The 3D Measuring Tool is intended to be used in any environment where health care is provided by a qualified health care professional. The 3D Measuring Tool does not provide a diagnosis or therapy.

The LymphaTech Mobile 3D Measuring Tool is a standalone software mobile application that uses a high-accuracy off-the-shelf long-wave infrared camera for measuring the diameter, surface area, volume, and circumference of a part of the body. The software allows clinicians to measure body region volume, circumference, surface area, and length with high precision. Specifically, this device uses an off-the-shelf depth sensing scanner, which is a type of long-wave infrared camera, together with an off-the-shelf iPad to acquire complete 3D renderings of the body regions.

The provided document is an FDA 510(k) clearance letter and its associated 510(k) summary for the LymphaTech Mobile 3D Measuring Tool. While it outlines the device's intended use, comparison to a predicate, and types of testing performed, it does not include the specific acceptance criteria or detailed results of the study that proves the device meets those criteria.

Specifically, the document states: "Non-Clinical Bench Performance Testing including linear diameter and length accuracy testing, circumference and surface area testing, volume accuracy testing as compared to water displacement and perometry, and inter-operator variability assessment." However, it does not provide:

• The quantitative acceptance criteria (e.g., "accuracy within X%").
• The reported performance statistics (e.g., "average accuracy of Y% for diameter").
• Details about the sample sizes for the test set, data provenance, ground truth establishment, or multi-reader studies.

Therefore, I cannot fulfill all parts of your request based solely on the provided text. I will answer what can be inferred and explicitly state what information is missing.

Acceptance Criteria and Device Performance

Information Missing: The document states that "Non-Clinical Bench Performance Testing" was conducted, including "linear diameter and length accuracy testing, circumference and surface area testing, volume accuracy testing as compared to water displacement and perometry, and inter-operator variability assessment." However, it does not specify the quantitative acceptance criteria (e.g., a target accuracy percentage or deviation limit) for any of these measurements, nor does it present the reported device performance statistics (e.g., the actual accuracy achieved).

Therefore, a table of acceptance criteria and reported device performance cannot be generated from the given text.

Study Details

Here's what can be extracted or inferred about the study performed, with clear notes on missing information:

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

• Sample Size (Test Set): Not specified. The document mentions "Performance testing of the subject device was conducted on humans," but it does not provide the number of subjects or scans in the test set.
• Data Provenance: The document states "Performance testing of the subject device was conducted on humans." The country of origin is not specified, and it is implied to be a prospective collection of data for the purpose of the study, but this is not explicitly stated.

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

• Number of Experts: Not specified.
• Qualifications of Experts: Not specified.
• Note: The document states that "volume accuracy testing as compared to water displacement and perometry" was performed. Water displacement and perometry are objective physical measurement methods often considered 'gold standards' for volume and circumference, rather than requiring expert consensus in the same way, for example, classifying a medical image would.

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

• Adjudication Method: Not specified. Given the nature of the measurements (physical dimensions),, it's more likely that direct comparison to ground truth (water displacement/perometry) would be the primary method for accuracy rather than expert adjudication, but this is not explicitly stated.

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:

• MRMC Study: No, this type of study was not performed or reported. The device is described as an "adjunctive tool to help a qualified health care professional measure and record body part data." The testing description ("inter-operator variability assessment") suggests evaluating consistency among users of the device, rather than comparing human performance with and without device assistance.
• Effect Size: N/A, as an MRMC study comparing human readers with/without AI assistance was not reported.

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

• Standalone Performance: Yes, implicitly. The "Non-Clinical Bench Performance Testing" described for linear diameter, length, circumference, surface area, and volume accuracy against objective ground truths (water displacement, perometry) represents standalone algorithm performance concerning its measurement capabilities. While a human operates the device, the accuracy of the measurements generated by the software is what's being assessed against physical standards, which is a form of standalone performance.

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

• Type of Ground Truth: The ground truth for measurement accuracy included water displacement and perometry. These are objective, gold-standard physical measurement techniques.

7. The sample size for the training set:

• Sample Size (Training Set): Not specified. The document describes "Software Verification Testing" but does not detail any machine learning model training or associated training data sets. The device is referred to as "software only."

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

• Ground Truth Establishment (Training Set): Not specified. As the document doesn't explicitly mention a machine learning component requiring a distinct training set, this information is not provided. The software's core function is to construct a 3D model and perform geometric calculations; the accuracy of these calculations is what the bench testing verifies against physical ground truths.

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The SnapshotGLO is a handheld imaging tool that allows clinicians diagnosing and treating skin wounds, at the point of care, to (i) View and digitally record images of a wound, (ii) Measure and digitally record the size of a wound, and (iii) View and digitally record images of fluorescence emitted from a wound when exposed to an excitation light. The fluorescence image, when used in combination with clinical signs and symptoms, has been shown to increase the likelihood that clinicians can identify wounds containing bacterial loads >104 CFU per gram as compared to examination of clinical signs and symptoms alone. The SnapshotGLO device should not be used to rule-out the presence of bacteria in a wound. The SnapshotGLO does not diagnose or treat skin wounds.

SnapshotGLO is a medical device that operates like a camera. It is a point-of-care, wound imaging device. This device is a non-contact imaging device wherein the wound images are captured from a height of ~12 cm using 395 nm LEDs and a white LED to produce a resultant fluorescence image that aids in visualising the bacteria on the wound and a colour-based "RGB" image or clinical image. Resultant images are viewed on the 7-inch touchscreen display. SnapshotGLO is based on autofluorescence imaging technology and uses native fluorescence of bacteria to determine the presence of bacterial bioburden and displays a two-dimensional, colour-coded highlight of bioburden presence on the wounds. SnapshotGLO is a handheld imaging tool that allows clinicians diagnosing, monitoring and treating skin wounds at the point of care with the help of the following features: . View and digitally record images of a wound, . Measure and digitally record the size of a wound, and View and digitally record images of fluorescence emitted from a wound when exposed to an ● excitation light SnapshotGLO consists of: - SnapshotGLO device - Medical grade adapter - User Manual - Quick Start Guide ● SnapshotGLO is intended for wound care applications as an adjunct tool that uses autofluorescence to detect tissues or structures. The fluorescence image, when used in combination with clinical signs and symptoms, has been shown to increase the likelihood that clinicians can identify wounds containing high bacteria bioburden compared to clinical symptoms alone. SnapshotGLO should not be used to rule-out the presence of bacteria in a wound. This device is not intended to provide a diagnosis.

The provided text is a 510(k) summary for the SnapshotGLO (KB100) device, aiming to demonstrate its substantial equivalence to the MolecuLightDX as a predicate device. While it details the device's function and provides some study information, it does not explicitly state "acceptance criteria" in a tabulated format alongside "reported device performance." Instead, it discusses the outcomes of non-clinical and clinical studies that support the device's equivalence and performance.

Based on the information provided, here's an attempt to structure the response according to your request, extracting the closest equivalents to "acceptance criteria" and "reported performance" from the study descriptions.

1. Table of Acceptance Criteria and Reported Device Performance

The document does not explicitly define quantitative acceptance criteria for the clinical study. However, the non-clinical tests imply an acceptance criterion of "comparable performance" or "substantially equivalent performance" to the predicate device. For the clinical study, the acceptance was based on showing "improved accuracy" compared to the predicate device when used with clinical signs and symptoms.

2. Sample Size and Data Provenance

• Test Set Sample Size:
  • Clinical Study: 40 patients.
  • Non-clinical Studies: Not explicitly stated, but conducted on "culture plates" and "wound dimensions."
• Data Provenance: The document does not specify the country of origin for the clinical study. The clinical study was described as retrospective.

3. Number of Experts and Qualifications for Ground Truth

• Number of Experts: Two clinical evaluators were involved in the retrospective clinical study.
• Qualifications: Not explicitly stated in the document.

4. Adjudication Method for the Test Set

The document states "This blinded assessment" for the clinical study, indicating that the evaluators were blinded to some information, but it does not describe a specific adjudication method (e.g., 2+1, 3+1 consensus).

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

• Was an MRMC study done? A clinical study comparing the SnapshotGLO and the predicate device was done, involving two clinical evaluators. While it's a comparative study with multiple readers, the format described does not fully align with a typical MRMC study designed to assess reader improvement with AI assistance. It rather compares the device's performance (with CSS) against the predicate device's performance (with CSS) validated against culture.
• Effect Size of Human Readers Improvement with AI vs. Without AI Assistance: The study compared SnapshotGLO + CSS versus MolecuLightDX + CSS versus CSS alone (implicit, as the basis for comparison), all validated against culture results. It demonstrated:
  • SnapshotGLO + CSS accuracy: 75-82.5%
  • MolecuLightDX + CSS accuracy: 52.5-65%
  • The phrase "increase the likelihood that clinicians can identify wounds containing bacterial loads >10^4 CFU per gram as compared to examination of clinical signs and symptoms alone" (from the Indications for Use) suggests that the device, when combined with CSS, improves performance over CSS alone. The specific "effect size" of improvement of human readers with AI vs. without AI assistance (meaning AI as an added tool for human readers) is not directly quantified as a comparative value in terms of reader gain. The comparison shown is between two different devices (both of which are imaging tools that provide additional information to clinicians) when used with CSS, against culture results.

6. Standalone (Algorithm Only) Performance

The document does not report on standalone (algorithm only without human-in-the-loop performance). The indications for use consistently state that the fluorescence image is to be used "in combination with clinical signs and symptoms."

7. Type of Ground Truth Used

• Clinical Study: The ground truth for identifying wounds with bacterial loads >10^4 CFU per gram was established using culture results.
• Non-clinical Studies: The ground truth for bacterial fluorescence detection was based on bacterial presence in in vitro culture plates. For wound dimensions, it was likely based on known or carefully measured dimensions.

8. Sample Size for the Training Set

The document is a 510(k) summary for a medical device and does not provide information regarding the training set sample size as it primarily focuses on the device's performance for regulatory submission. This device description points to an "autofluorescence imaging technology" for directly visualizing bacterial compounds, rather than a machine learning algorithm that requires a training set. If there is an AI component for image processing or interpretation not explicitly detailed, the training set information is not included in this document.

9. How Ground Truth for Training Set Was Established

As no training set is discussed concerning an AI/ML algorithm, no information is provided on how its ground truth was established. The device utilizes physical principles of autofluorescence.

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The MSI is a handheld imaging tool that allows clinicians diagnosing and treating skin wounds to:
· View and digitally record images of a wound.
· Measure and digitally record the size of a wound.

• · View and digitally record images of fluorescence emitted from a wound when exposed to an excitation light.
  · View and digitally record thermal images of a wound.
  The MSI does not diagnose or treat skin wounds.

The MSI Kit includes the following: the MSI, a Universal Serial Bus (USB-C) cable, a wall adapter, and a carrying case.
The MSI captures and processes optical data of an imaged wound. The MSI consists of three imaging modalities: white light, autofluorescence and thermal. The MSI has 395nm excitation LEDs for autofluorescence imaging, a sensor for measuring distance to the wound, and a thermal sensor for capturing temperature gradients.
The MSI is powered by an onboard rechargeable battery and has a USB-C connection for uploading images to a computer. The manual provides users of the MSI with detailed instructions for proper use, maintenance, and storage.

The provided text outlines the general safety and performance testing conducted for the Multispectral Imager Kit (MSI Kit) to establish its substantial equivalence to predicate devices, but it does not contain the specific acceptance criteria or detailed results of a study demonstrating the device meets those criteria.

However, based on the information provided, here's a breakdown of what can be inferred and what is missing:

Acceptance Criteria and Reported Device Performance:

The document lists various performance tests, implying certain acceptance criteria for each, but the specific numerical targets or thresholds are not provided. The conclusion statement indicates that the clinical and non-clinical data "indicate that the MSI Kit is as safe and effective as the predicate devices," which serves as a general statement of meeting underlying acceptance criteria for equivalence.

Missing information: The actual acceptance criteria (e.g., minimum accuracy for wound measurement, specific signal-to-noise ratio requirements for fluorescence imaging) and the quantitative results from the study demonstrating the device's performance against these criteria are not detailed in the provided text.

Detailed Information based on the provided text:

1. Table of Acceptance Criteria and Reported Device Performance

As noted above, specific acceptance criteria and detailed quantitative results are not provided in the document. The document lists the types of tests performed and generally states that the device's performance was evaluated, leading to a conclusion of safety and effectiveness comparable to predicate devices.

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

• Test Set Sample Size: The document does not specify the sample size for any of the performance tests, including the clinical study.
• Data Provenance:
  • Clinical Study: "A clinical study was conducted under anticipated conditions with anticipated users." The location (country of origin) is not specified, nor is whether it was retrospective or prospective. Given the phrasing "conducted," it implies a prospective study.
  • Bench Studies: Performed to verify various performance aspects. No information on data provenance other than being "bench studies."

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

The document states that the MSI is "a handheld imaging tool that allows clinicians diagnosing and treating skin wounds to: ... View and digitally record images of fluorescence emitted from a wound when exposed to an excitation light," and that it "does not diagnose or treat skin wounds." The clinical study evaluated the "ability of the MSI to detect autofluorescence signals from tissues or structures consistent with the indications for use."

This implies that the assessment of autofluorescence signals in wounds would typically require clinical experts. However, the document does not specify the number of experts or their qualifications used to establish ground truth for any test set.

4. Adjudication method for the test set

The document does not specify any adjudication method for establishing ground truth in the clinical study or any other performance testing.

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

The document does not mention a multi-reader, multi-case (MRMC) comparative effectiveness study, nor does it refer to AI assistance or human reader improvement with AI. The device is described as an "imaging tool" for clinicians, not as an AI-powered diagnostic aid that assists in interpretation.

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

The device description focuses on its imaging capabilities and the output of images (white light, fluorescence, thermal) for clinician evaluation. None of the performance data sections suggest a standalone algorithm-only performance assessment where the device makes interpretations without human involvement. The indications for use specifically state it "does not diagnose or treat skin wounds" and provides images for clinicians.

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

The document does not explicitly state the type of ground truth used in the clinical study. For "autofluorescence signals from tissues or structures consistent with the indications for use," the ground truth would likely involve a clinical assessment by wound care specialists or potentially correlated with wound characteristics that are known to exhibit certain autofluorescence, but this is not specified.

8. The sample size for the training set

The document does not mention a training set or its sample size. This suggests that the device's functionality does not rely on a machine learning model that requires a labeled training dataset in the way a diagnostic AI would. The device's operation, as described, appears to be based on capturing and processing optical and thermal data rather than learning from data.

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

Since no training set is mentioned, there is no information on how ground truth for a training set was established.

Ask a specific question about this device

The MolecuLight i:X is a handheld imaging tool that allows clinicians diagnosing and treating skin wounds, at the point of care, to

(i) View and digitally record images of a wound,

(ii) Measure and digitally record the size of a wound, and

(iii) View and digitally record images of fluorescence emitted from a wound when exposed to an excitation light.

The fluorescence image, when used in combination with clinical signs and symptoms, has been shown to increase the likelihood that clinicians can identify wounds containing bacterial loads >10^4 CFU per gram) as compared to examination of clinical signs and symptoms alone. The MolecuLight i:X device should not be used to rule-out the presence of bacteria in a wound.

The MolecuLight i:X does not diagnose or treat skin wounds.

The MolecuLight i:X Imaging Device is a handheld medical imaging device comprised of a high-resolution color LCD display and touch-sensitive screen with integrated optical and microelectronic components. MolecuLight i:X uses its patented technology to enable real-time standard digital imaging and fluorescence (FL) imaging in wounds and surrounding healthy skin of patients as well as wound area measurements.

The MolecuLight i:X is a handheld imaging tool that helps clinicians identify wounds containing elevated bacterial loads. The provided text outlines the acceptance criteria and the study that supports the device's claims.

1. Table of Acceptance Criteria and Reported Device Performance

The provided documentation does not explicitly state "acceptance criteria" as a set of predefined thresholds that the device had to meet for clearance. Instead, it presents performance metrics from a clinical study, which implicitly serve as evidence for addressing the added labeling claims. The key performance metrics are related to the device's ability to guide wound sampling for detecting bacterial burden.

Derived Acceptance Criteria (based on the device's claims and study outcomes) and Reported Device Performance:

• SoC-guided sample: 87.2% (95% CI: 77.7%, 93.7%)
• FL-guided sample: 98.7% (95% CI: 93.06%, 99.97%)
  P-value: P = 0.012 (FL-guided sampling was significantly more sensitive)

Ability to detect a higher number of bacterial species:

• Mean number of species by FL-guided Biopsy: 3.026 (SD 1.667)
• Mean number of species by SoC-guided Biopsy: 2.231 (SD 1.528)
• Difference: 0.795 (SD 1.804)
  P-value: P 10^4 CFU per gram" were the key threshold. Pathogen identification was also a part of the ground truth.

• Non-Clinical Testing: The ground truth for demonstrating red fluorescence production for specific bacterial species was based on sub-culturing on Porphyrin Test Agar (PTA) and direct observation of fluorescence using the MolecuLight i:X.

8. The Sample Size for the Training Set

The document primarily discusses a retrospective analysis for clinical validation and in-vitro testing. It does not mention a separate training set size for any specific AI/algorithm development, as the device's core functionality appears to be based on known autofluorescence properties of bacteria rather than a deep learning model trained on large datasets for complex pattern recognition. The study mentioned ([K191371](https://510k.innolitics.com/search/K191371)) was used to support subsequent claims, implying this may have provided data for initial claims.

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

As no explicit training set for a machine learning algorithm is detailed, the method for establishing ground truth for such a set is not provided. The non-clinical testing described involves culturing specific bacterial species to verify their autofluorescent properties, which would serve as a 'ground truth' for the physical principle the device leverages.

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The MolecuLightDX is a handheld imaging tool that allows clinicians diagnosing and treating skin wounds, at the point of care, to

• (i) View and digitally record images of a wound,
• (ii) Measure and digitally record the size of a wound, and
• (iii) View and digitally record images of fluorescence emitted from a wound when exposed to an excitation light.

The fluorescence image, when used in combination with clinical signs and symptoms, has been shown to increase the likelihood that clinicians can identify wounds containing bacterial loads >104 CFU per gram as compared to examination of clinical signs and symptoms alone. The MolecuLightDX device should not be used to rule-out the presence of bacteria in a wound.

The MolecuLightDX does not diagnose or treat skin wounds.

The MolecuLightDX Imaging Device is a handheld medical imaging device comprised of a high-resolution color AMOLED display and touch-sensitive screen with integrated optical and microelectronic components. MolecuLightDX uses its patented technology to enable real-time standard digital imaging and fluorescence imaging in wounds and surrounding healthy skin of patients as well as wound area measurements.

This document is a 510(k) summary for the MolecuLightDX device. It describes non-clinical testing performed on the device but does not contain information about clinical studies or acceptance criteria for identifying bacterial loads >10^4 CFU per gram. The summary focuses on established equivalence to a predicate device (MolecuLight i:X K191371) based on similar technological characteristics and compliance with electrical safety and software validation standards, rather than direct performance metrics against clinical acceptance criteria for bacterial load detection.

Therefore, the requested information regarding acceptance criteria and a study proving the device meets those criteria, specifically concerning the identification of bacterial loads, cannot be fully provided from the given text.

However, based on the information provided, here's what can be extracted:

1. Table of Acceptance Criteria and Reported Device Performance:

The document primarily discusses compliance with safety and technical standards rather than specific clinical performance acceptance criteria for bacterial load detection. The key performance claim related to its indication for use is: "The fluorescence image, when used in combination with clinical signs and symptoms, has been shown to increase the likelihood that clinicians can identify wounds containing bacterial loads >10^4 CFU per gram as compared to examination of clinical signs and symptoms alone."

While this is an indication of efficacy, the document does not specify quantitative acceptance criteria (e.g., sensitivity, specificity, or AUC thresholds) for this "increased likelihood" or present a study showing the MolecuLightDX device itself met such criteria. Instead, it refers to its predecessor's demonstrated capability.

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

Not provided in this document for the MolecuLightDX's bacterial load identification capability. The non-clinical testing mentioned includes:

• Standards Compliance Testing
• Software Verification and Validation
• System Verification and Validation
• Accuracy and Inter/Intra Reader Variability Testing of Wound Measurement Function
• Packaging and Transport validation

These tests do not involve clinical test sets for bacterial load identification. The effectiveness claim for bacterial load identification references earlier work/evidence from the predicate device, implicitly assuming the newer device maintains this capability due to similar technology.

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

Not provided in this document.

4. Adjudication method for the test set:

Not provided in this document.

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 provided in this document. The device is a "handheld imaging tool" and not an AI-driven diagnostic algorithm in the typical sense that would necessitate an MRMC comparative effectiveness study for "AI vs without AI assistance". Its primary function is to enable visualization of fluorescence which clinicians then interpret.

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

Not applicable. The device is explicitly described as a "handheld imaging tool that allows clinicians... to view and digitally record images... The fluorescence image, when used in combination with clinical signs and symptoms, has been shown to increase the likelihood that clinicians can identify wounds..." This indicates a human-in-the-loop system.

7. The type of ground truth used:

Not explicitly stated for the MolecuLightDX in this document regarding bacterial load. However, the indication for use refers to "bacterial loads >10^4 CFU per gram," which implies quantitative microbiology (CFU per gram) as the ground truth method in the referenced studies from which the claim of "increased likelihood" originated (likely from the predicate device's clearance).

8. The sample size for the training set:

Not applicable, as this document does not describe a machine learning algorithm requiring a training set for the bacterial load identification claim. The device is an imaging tool.

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

Not applicable.

Summary based on available information:

The provided document (a 510(k) summary) focuses on demonstrating substantial equivalence to a predicate device (MolecuLight i:X K191371) through non-clinical testing, regulatory compliance, and comparison of technological characteristics. It does not detail a specific clinical study for MolecuLightDX to establish performance against acceptance criteria for identifying bacterial loads. Instead, it leverages the known efficacy claim from its predicate device for its indication of use, implying that the MolecuLightDX, being technologically similar, shares this established capability. The "increased likelihood" claim for identifying bacterial loads >10^4 CFU per gram is presented as a previously established benefit from the predicate device's performance.

Ask a specific question about this device

The MolecuLight i:X is a handheld imaging tool that allows clinicians diagnosing and treating skin wounds, at the point of care, to
(i) View and digitally record images of a wound.
(ii) Measure and digitally record the size of a wound, and
(iii) View and digitally record images of fluorescence emitted from a wound when exposed to an excitation light.

The fluorescence image, when used in combination with clinical signs and symptoms, has been shown to increase the likelihood that clinicians can identify wounds containing bacterial loads >104 CFU per grams as compared to examination of clinical signs and symptoms alone. The MolecuLight i:X device should not be used to rule-out the presence of bacteria in a wound.

The MolecuLight i:X does not diagnose or treat skin wounds.

The MolecuLight i:X Imaging Device is a handheld medical imaging device comprised of a high-resolution color LCD display and touch-sensitive screen with integrated optical and microelectronic components. MolecuLight i:X uses its patented technology to enable real-time standard digital imaging and fluorescence imaging in wounds and surrounding healthy skin of patients as well as wound area measurements.

Here's a breakdown of the acceptance criteria and the study proving the device meets them, based on the provided FDA 510(k) summary for the MolecuLight i:X:

The MolecuLight i:X device is an autofluorescence detection device for general surgery and dermatological use. The 510(k) summary details a modification to the device's labeling to clarify the relationship between cyan fluorescence and the increased likelihood of Pseudomonas aeruginosa bacterial loads. The study's purpose is to demonstrate that this additional labeling statement does not raise new questions of safety or efficacy and is supported by existing clinical data.

1. Table of Acceptance Criteria and Reported Device Performance

The acceptance criteria are implicitly tied to the performance metrics of the device in identifying bacterial loads, specifically Pseudomonas aeruginosa and Total Bacterial Load (TBL), as established in the original K191371 clearance. The current submission's goal is to demonstrate that the expanded labeling around cyan fluorescence's association with P. aeruginosa is supported by the data and does not alter the previous safety and effectiveness profile.

The performance is reported in terms of sensitivity, specificity, Positive Predictive Value (PPV), Negative Predictive Value (NPV), and Likelihood Ratio for different fluorescence signatures (Cyan, Red, Red or Cyan) in detecting bacterial loads.

Note: The document states "The additional labeling statement does not raise different questions of safety or efficacy. Retrospective analysis has demonstrated the safety and effectiveness of MolecuLight i:X with regards to the additional labeling statement." This implies that the acceptance criteria are met if the performance metrics continue to support the device's utility in identifying bacterial loads and the new labeling is consistent with the observed data.

2. Sample Size and Data Provenance

• Sample Size for Test Set: Data from 350 patients were retrospectively analyzed.
• Data Provenance: The document does not explicitly state the country of origin but refers to "retrospective analysis" of existing clinical study data. Given MolecuLight Inc. is based in Toronto, Canada, and the clinical study was "reported in support of K191371", it is likely the data was collected in either Canada or the US, or potentially a multi-site international study. The study was retrospective.

3. Number of Experts and their Qualifications

The document does not specify the number of experts used to establish the ground truth for the test set, nor does it explicitly state their qualifications. The interpretation of "fluorescence image, when used in combination with clinical signs and symptoms" suggests that the ground truth was established by clinicians based on the convergence of factors, potentially including microbiological culture results (as indicated by CFU/g measurements).

4. Adjudication Method for the Test Set

The document does not describe any adjudication method for the test set. Given that the analysis is "retrospective analysis" of existing clinical study data, the ground truth was likely established as part of the original study design.

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

A Multi-Reader Multi-Case (MRMC) comparative effectiveness study was not performed for this 510(k) submission. This submission is a "Real-World Data" (retrospective analysis) supporting a labeling change for a previously cleared device, not a new device clearance or a comparative effectiveness study with human readers. The document states: "The fluorescence image, when used in combination with clinical signs and symptoms, has been shown to increase the likelihood that clinicians can identify wounds containing bacterial loads >10^4 CFU per grams as compared to examination of clinical signs and symptoms alone." This sentence refers to a finding from the original K191371 clearance, not a new MRMC study in this submission.

6. Standalone (Algorithm Only) Performance

This device, the MolecuLight i:X, is an imaging tool used by clinicians to view and record images of fluorescence. It is not an AI algorithm that generates a diagnosis or interpretation autonomously. Therefore, a standalone (algorithm only) performance study was not applicable or performed in the context of this 510(k). The device provides visual information (fluorescence images) that clinicians interpret in conjunction with clinical signs and symptoms.

7. Type of Ground Truth Used

The ground truth for the test set appears to be microbiological culture results (bacterial loads measured in CFU/g) combined with clinical assessment. The specific phrases "identify wounds containing bacterial loads >10^4 CFU per grams" and "Pseudomonas aeruginosa at Species Specific Levels ≥ 10^4 CFU/q" clearly indicate that quantitative bacterial culture was the definitive ground truth for bacterial presence and load.

8. Sample Size for the Training Set

The document does not describe a training set in the context of an AI/algorithm. The "study" described is a retrospective analysis of previously collected clinical data to support a labeling claim for a medical device. If there was any machine learning involved (which is not directly implied for this device's function as an imaging tool), that would have been part of the original K191371 submission and is not detailed here.

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

As no training set (in the context of an AI/ML algorithm) is described, this question is not applicable based on the provided document. The device's function described (capturing and displaying fluorescence) implies it is a viewing tool, not an AI-powered diagnostic algorithm requiring a training phase for its output beyond the initial development of its optical and imaging capabilities.

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The SpyGlass Discover Digital System is indicated for use in diagnostic applications during endoscopic procedures in the pancreaticobiliary system including the hepatic ducts. The SpyGlass Discover Digital System comprises two components: the SpyGlass Discover Digital Catheter and the SpyGlass Discover Digital Controller.

The SpyGlass Discover Digital Catheter is intended to provide direct visualization and to guide both optical and accessory devices for diagnostic and therapeutic applications during endoscopic procedures in the pancreatico-biliary system including the hepatic ducts. The SpyGlass Discover Digital Controller is intended to provide illumination and receive, process, and output images from the SpyGlass Discover Digital Catheter for diagnostic and therapeutic applications during endoscopic procedures in the pancreaticobiliary system including the hepatic

Controller:

The SpyGlass Discover Digital Controller is intended to provide illumination and receive, process, and output images from the SpyGlass Discover Digital Catheter for diagnostic applications during endoscopic procedures in the pancreaticobiliary system including the hepatic ducts.

The SpyGlass Discover Digital System comprises two components: (1) a sterile, single-use endoscope, the SpyGlass Discover Digital Catheter (the "Catheter"); and (2) a non-sterile endoscopic video imaging system, the SpyGlass Discover Digital Controller (the "Controller").

The SpyGlass Discover Digital Catheter comprises a handle, an insertion tube, and a connection cable. The handle includes two articulation control knobs, a lever to lock the control knobs in place, connectors for irrigation and aspiration, a working channel port. The insertion tube contains one working channel for accessory devices and aspiration, two channels for irrigation, two optical fibers to transmit illumination from the Controller, and wiring to transmit video signals to the Controller. The bending section at the distal portion of the insertion tube is controlled by the user via the articulation control knobs on the handle. The distal end of the insertion tube contains a camera for capturing video and transmitting it to the Controller, elements for transmitting illumination from the Controller, and the distal openings of the irrigation and working channels. The catheter cable connects the catheter handle to the Controller for transmitting illumination and video signals.

The Controller is an endoscopic video imaging component that combines the functionality of a camera and an LED light source. The Controller receives video signals from the catheter, processes the video signals, and outputs video images to an attached monitor. The Controller also generates and controls the illumination transmitted to the distal end of the catheter. The user interface of the Controller comprises a power button, a receptacle to connect the catheter connection cable, buttons to turn illumination on or off and to control the illumination intensity, and an illumination intensity indicator. The Controller outputs video images to an attached monitor via DVI, VGA, or S-Video ports, and the user may select NTSC or PAL video formats according to the geographic region of use.

It appears you've provided documentation for a 510(k) submission to the FDA for the SpyGlass Discover Digital System. This document is a "510(k) Summary" and a "Clearance Letter." These documents describe the device and its indications for use, but they do not contain any information about acceptance criteria or actual device performance study results.

The purpose of a 510(k) submission is to demonstrate that a new device is "substantially equivalent" to a legally marketed predicate device, not necessarily to prove its clinical efficacy or safety through new clinical studies. While substantial equivalence often relies on performance testing, the provided text does not describe the specific acceptance criteria for a study or the results of such a study in the format you requested.

Therefore, I cannot extract the information to fill out your table and answer your questions regarding acceptance criteria and performance study details from the provided text. The document states:

• "The proposed SpyGlass Discover Digital System shares the same intended use and fundamental scientific technology as the predicate SpyGlass DS Direct Visualization System (K183636)." This suggests that Boston Scientific is primarily relying on the established performance of the predicate device and engineering design changes, rather than a new de novo clinical performance study with specific acceptance criteria and results for this submission.
• "Most of the SpyGlass Discover Digital Catheter components are identical in dimensions and in mechanical performance to its predicate." This further supports the reliance on the predicate's performance.

To answer your questions, you would typically need a separate clinical study report or a more detailed technical file that outlines the validation testing, acceptance criteria, and specific performance outcomes of the device.

If you have a document that describes a specific performance study (e.g., a "clinical report," "verification and validation report," or "performance data" section), please provide that, and I would be happy to help.

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The THD Procto Software is a software that can be used:

· In endoanal ultrasound (EAUS), in order to help evaluate pelvic floor disorders by processing and recording images of tissue structures in the pelvic region with the aid of a dedicated ultrasound probe. This is done by inserting the probe into the anal canal, acquiring the ultrasound signal and letting the software process the image.

· In Anoscopy exams in order to record images and videos of the anorectal channel, which are acquired through a dedicated video camera that provides images with a resolution greater than 1.1 MPx through USB protocol

The THD Procto Software System, is a diagnostic system intended to be used to investigate pelvic floor disorders, and specifically the THD Procto Software together with its accessories (endoanal probe and video camera), is able to be applied for:

• Endoanal ultrasound (→ trans-rectal ultrasound / echography) -
• -Anoscopy exams,

During Endoanal Ultrasound the THD Procto Software System processes and records images of tissue structures in the pelvic region with the aid of a dedicated ultrasound probe; Durinq Anoscopy exams the THD Procto Software System records and displays images of the anorectal channel with the aid of a dedicated video camera;

The THD Procto Software consists of three macro modules or sub-parts, each one with its own function, as described below:

• Medical Report (Launcher) module, which contains the functions for the management of . the patient database and of the Exams database. Patients and Exams databases support the operation of the remaining macro modules (Endoanal Ultrasound Module and Anoscopy Exams Module) that are listed below
• . Endoanal Ultrasound Module, which manages:
  • The acquisition of the ultrasound signal from the probe and its processing to o transform it into a two-dimensional echographic image / video
  • Any image / video processing (application of notes, zoom, measurements, etc.) in o real-time (during the exam) or post-processing,
  • The examination report (medical history, comments, conclusions) and the printing O
• Anoscopy Exams module, which manages:
  • Capturing images and video from the video camera via standard USB protocol. o Images are then recorded and displayed on the computer screen
  • Any image / video processing (application of notes, zoom, measurements, etc.) in O real-time (during the exam) or post-processing
  • The examination report (medical history, comments, conclusions) and the printing o

Here's a breakdown of the acceptance criteria and study details for the THD Procto Software System, based on the provided document. Please note that the document is a 510(k) summary, which focuses on substantial equivalence to predicate devices rather than detailed performance studies typical for novel AI/ML devices. Therefore, some information, particularly regarding specific performance metrics and AI/ML evaluation methodologies, is not present. The device appears to be primarily an imaging and diagnostic system with software for processing and recording, not an AI/ML diagnostic algorithm that would have specific performance metrics like sensitivity, specificity, or AUC.

Acceptance Criteria and Device Performance

The provided document (a 510(k) summary) doesn't explicitly state quantitative acceptance criteria in the typical sense of a pre-defined performance threshold for an AI/ML algorithm (e.g., "sensitivity must be >X%"). Instead, the "acceptance" in this context is based on demonstrating substantial equivalence to predicate devices for its intended use as an ultrasonic pulsed echo imaging system and for anoscopy exams.

Therefore, the "reported device performance" is largely demonstrated through a comparison of technological characteristics and intended uses with legally marketed predicate devices, rather than through specific performance metrics like accuracy, sensitivity, or specificity.

Table of Acceptance Criteria and Reported Device Performance (based on Substantial Equivalence)

Since the device is cleared via 510(k) substantial equivalence, the "acceptance criteria" revolve around demonstrating that the device is as safe and effective as the predicate. The "reported device performance" is the assertion of meeting these equivalence points.

Study Details (Based on the 510(k) Summary)

It's crucial to understand that a 510(k) summary is not a detailed scientific study report. It summarizes the basis for substantial equivalence to a predicate device. Therefore, explicit information about a "study" in the typical clinical trial sense, especially for AI/ML performance, is not present for this device. The information provided below is a reconstruction based on typical 510(k) submission practices rather than an explicit description of a performance study within the document.

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

• Not explicitly stated. For a 510(k) submission based on substantial equivalence of an imaging system rather than a diagnostic AI algorithm, there isn't typically a "test set" in the sense of a dataset used for performance evaluation against ground truth. Equivalence is primarily based on technical characteristics, rather than performance on a clinical dataset.
• The document implies that the device works by acquiring and processing real-time ultrasound signals and camera inputs, suggesting its function is akin to a medical device instrument rather than a standalone AI diagnostic tool evaluated on a pre-defined test set.

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

• Not applicable/Not stated. Given the nature of a 510(k) for an imaging system, ground truth establishment by experts for a "test set" is not detailed in this summary. The device's function is to provide images and measurements, not to interpret them autonomously.

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

• Not applicable/Not stated. No test set or associated adjudication method is mentioned in this 510(k) summary.

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 evidence. This document does not describe an MRMC study. The THD Procto Software System appears to be an imaging and measurement software rather than an AI-assisted diagnostic tool that would typically undergo such a study.

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

• No evidence. The document describes a system involving a human operator (inserting probes, acquiring signals, using the software for processing and recording). There is no mention of a standalone algorithm or its performance.

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

• Not applicable/Not stated. The document does not describe performance evaluation against a specific type of ground truth in the context of an AI/ML diagnostic claim. The device aims to visually present anatomical structures and allow for measurements, for which the "ground truth" is typically the real-time anatomical structures themselves as visualized by the medical professional.

7. The sample size for the training set:

• Not applicable/Not stated. The document does not describe a training set, suggesting this device is not based on a machine learning model that requires a training set. It appears to be a functional image acquisition, processing, and recording software.

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

• Not applicable/Not stated. As no training set is mentioned, ground truth establishment for it is not discussed.

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The MolecuLight i:X is a handheld imaging tool that allows clinicians diagnosing and treating skin wounds, at the point of care, to

• View and digitally record images of a wound, (i)
• Measure and digitally record the size of a wound, and (ii)
• View and digitally record images of fluorescence emitted from a wound when exposed to an excitation light. (iii)

The fluorescence image, when used in combination with clinical signs and symptoms, has been shown to increase the likelihood that clinicians can identify wounds containing bacterial loads >104 CFU per gram as compared to examination of clinical signs and symptoms alone. The MolecuLight i:X device should not be used to rule-out the presence of bacteria in a wound.

The MolecuLight i:X does not diagnose or treat skin wounds.

The MolecuLight i:X Imaging Device is a handheld medical imaging device comprised of a high-resolution color LCD display and touch-sensitive screen with integrated optical and microelectronic components. Moleculight i:X uses its patented technology to enable real-time standard digital imaging and fluorescence imaging in wounds and surrounding healthy skin of patients as well as wound area measurements.

Here's a breakdown of the acceptance criteria and the study that proves the MolecuLight i:X device meets them, based on the provided FDA 510(k) summary:

1. Table of Acceptance Criteria and Reported Device Performance

The core clinical claim for the MolecuLight i:X related to bacterial identification is: "The fluorescence image, when used in combination with clinical signs and symptoms, has been shown to increase the likelihood that clinicians can identify wounds containing bacterial loads >10^4 CFU per gram as compared to examination of clinical signs and symptoms alone."

While the document doesn't explicitly state quantitative acceptance criteria in the "we will achieve X performance" format, the demonstrated performance serves as the evidence for meeting their stated claim. The crucial part of the performance is the improvement in identifying wounds with relevant bacterial loads.

Acceptance Criteria (Implied by Clinical Claim and Study Results)

*   Microbiology Negative (10^4 CFU/g) was "quantitative microbiological analysis," which is a lab-based, objective method, not dependent on expert consensus.

4. Adjudication Method for the Test Set

• The document does not describe an adjudication method for the clinical evaluation of CSS or the interpretation of MolecuLight i:X images. It compares these clinical assessments directly against the quantitative microbiological analysis. This suggests that individual clinicians' interpretations were the data points, rather than a consensus interpretation.

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

• A formal MRMC study is not explicitly mentioned as having been performed. The study evaluates the likelihood that clinicians can identify wounds, comparing performance with and without the device. While it involves multiple clinicians, it's presented as a direct comparison of the combined CSS+iX approach versus CSS alone, rather than a statistical comparison of reader performance improvement.
• Effect Size:
  • The sensitivity for identifying high bacterial loads increased from 15.33% (CSS alone) to 60.98% (CSS + iX). This is a substantial increase, making the device significantly more likely to flag relevant wounds.
  • The specificity decreased from 93.65% (CSS alone) to 84.13% (CSS + iX). This indicates a trade-off where more wounds were incorrectly identified as having high bacterial loads with the device, but the report explicitly states this increase in false positives was "10^4 CFU per gram). This is an objective and laboratory-confirmed ground truth, considered a strong reference standard for bacterial burden.

8. Sample Size for the Training Set

• The document does not specify a separate training set or its size. This is typical for a medical device that provides direct imaging for human interpretation, rather than a machine learning algorithm that is "trained" on data. The clinical study described here functions as the pivotal performance validation.

9. How Ground Truth for the Training Set was Established

• As no separate training set or AI/ML training is indicated, this point is not applicable. The ground truth for the clinical study was established by quantitative microbiological analysis of wound samples.

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The Presero 3D Scanning System is an imaging tool that allows the processing, review, analysis, communication and media interchange of multi-dimensional digital images acquired from a 3D imaging device. It is indicated for the use of capturing visual images to measure the diameter, surface area, perimeter and volume of wounds. The Presero 3D Scanning System is designed for use by health care professionals and is intended to assist the healthcare professional who is responsible for making all final patient management decisions.

The Presero 3D Scanning System is a tablet-based system that allows the processing, review, analysis, communication and media interchange of multi-dimensional digital images acquired from a 3D imaging device. The Presero 3D Scanning System, does not contact the patient, nor does it control any life sustaining devices. A physician, providing ample opportunity for competent human intervention interprets images and information being displayed and printed. The Presero 3D Scanning System is comprised of a commercial off the shelf 3D camera fitted on a commercial off the shelf tablet, equipped with a proprietary software application that enables a health care professional to visualize and interact with 3D wound images, via a pen-like stylus, to assist in clinical decision making. The complete system (tablet, camera and software) integrates with a cloud back-end system. The cloud back-end system stores all patient data, operator details and other information allowing 2-way synchronization between the Presero 3D Scanning System and the cloud with the ability to fully support multiple systems within a single clinical facility.

Here's a breakdown of the acceptance criteria and study information for the Presero 3D Scanning System, based on the provided FDA 510(k) summary:

1. Table of Acceptance Criteria and Reported Device Performance

The provided document does not explicitly state numerical acceptance criteria or quantifiable device performance results for wound measurement accuracy. Instead, it makes a general statement about meeting design requirements and user needs.

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

The document does not specify a distinct "test set" and its sample size for evaluating the device's performance in measuring wounds. It mentions "verification and validation testing... in simulated use conditions," but provides no details on the number of cases or the nature of the data. The data provenance (country of origin, retrospective/prospective) is also not stated.

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

This information is not provided in the document. The method for establishing ground truth for any performance evaluation is not detailed.

4. Adjudication Method for the Test Set

No information is provided regarding an adjudication method for a test set.

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

No MRMC study comparing human readers with and without AI assistance is mentioned. The device is described as an "imaging tool" to "assist the healthcare professional," implying it provides measurements rather than assisting in diagnosis like some AI tools.

6. Standalone (Algorithm Only) Performance Study

While the document indicates that the device has undergone "verification and validation testing," it does not explicitly detail a standalone performance study in terms of specific metrics like sensitivity, specificity, or accuracy compared to a ground truth for wound measurements. The focus seems to be on the system's ability to perform its stated functions and comply with regulatory standards.

7. Type of Ground Truth Used

The document does not explicitly state the type of ground truth used for any performance evaluation. Given the device's function to measure wound characteristics, it is likely that a physical measurement (e.g., manual measurement with rulers, digital planimetry from other validated systems, or 3D models) would be used as a ground truth, but this is not confirmed in the provided text.

8. Sample Size for the Training Set

The document does not provide information on the sample size of a training set. This is often the case for 510(k) submissions of devices that are not primarily AI/machine learning diagnosis or detection tools where extensive training data might be discussed. The Presero 3D Scanning System is described as having "proprietary software application" and uses a "commercial off the shelf 3D camera," suggesting its primary function is measurement based on 3D scanning, which may rely more on geometric algorithms than machine learning requiring a large training set for image interpretation.

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

Since no training set is mentioned or detailed, there is no information on how its ground truth might have been established.

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