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The Pde-neo is an imaging system used in capturing fluorescent images for the visual assessment of blood flow, as an adjunctive method for the evaluation of tissue perfusion, and related tissue-transfer circulation in tissue and free flaps used during plastic, micro-, reconstructive, and organ transplant surgeries.

The pde-neo is an imaging system used in capturing and viewing fluorescent images for the visual assessment of blood flow, as an adjunctive method for the evaluation of tissue perfusion, and related tissue-transfer circulation in tissue and free flaps used during plastic, micro-, reconstructive, and organ transplant surgeries. The pde-neo is intended for intraoperative visual assessment of blood vessels and related tissue perfusion by enabling surgeons to observe fluorescent images of blood vessels and related tissue perfusion. Indocyanine green (ICG) is injected intravenously into patients. Infrared light-emitting diodes (LEDs) are used to excite the fluorescence of ICG and illuminate the regions of a patient's anatomy to be observed. A charge coupled device (CCD) camera captures the fluorescent image that is used to assess the blood vessels and related tissue perfusion.

The pde-neo consists of the following components: Camera Unit, Controller, and Remote Controller. The Camera Unit contains a CCD camera and LED light sources and is used either by hand or attaching it to a mechanical arm. The Controller receives the video signal of the fluorescent image from the Camera Unit and outputs the processed fluorescent image to the external video monitor and recorder. Adjustments of the fluorescent image are possible either by the Camera Unit or the Remote Controller.

This document describes the 510(k) summary for the pde-neo, a fluorescent angiographic system.

1. Table of acceptance criteria and the reported device performance:

The document does not explicitly state quantitative acceptance criteria in terms of performance metrics (like sensitivity, specificity, accuracy). Instead, the performance evaluations focus on functional aspects, safety, and image quality comparison to a predicate device.

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

• Test set sample size: Not explicitly stated for image quality or functional testing. The "study of image quality" is mentioned but without the number of images or cases.
• Data provenance: Not specified. Given that Hamamatsu Photonics K.K. is the submitter in Japan, the data likely originates from Japan, but it's not confirmed whether it was retrospective or prospective.

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 image quality study is mentioned but lacks details on human expert involvement or ground truth establishment.

4. Adjudication method for the test set:

This information is not provided in the 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:

A multi-reader multi-case (MRMC) comparative effectiveness study comparing human readers with and without AI assistance was not conducted or reported. This device is not an AI-assisted diagnostic tool in the sense of providing automated interpretations or predictions that would require such a study. It's an imaging system providing visual information to the surgeon.

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

A standalone algorithm-only performance study was not explicitly reported or necessary for this device type. The device provides visual information for human interpretation, not automated diagnostic outputs. The "software verification" indicated that the software operates as intended but doesn't describe an algorithm's standalone performance in a medical context.

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

The document does not explicitly define the type of ground truth used. For the "image quality study," the implication is that images from the new device were compared against those from the predicate device to establish "substantial equivalence" in quality, likely based on visual assessment.

8. The sample size for the training set:

There is no mention of a "training set" as this device does not appear to employ machine learning or AI that would require one for its primary function.

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

Not applicable, as no training set for machine learning/AI is described.

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The PDE is an imaging system used in capturing and viewing fluorescent images for the visual assessment of blood flow as an adjunctive method for the evaluation of tissue perfusion, and related tissue-transfer circulation in tissue and free flaps used during plastic, micro-, reconstructive and organ transplant surgeries.

The PDE is an imaging system used in capturing and viewing fluorescent images for the visual assessment of blood flow as an adjunctive method for the evaluation of tissue perfusion, and related tissue-transfer circulation in tissue and free flaps used during plastic, micro-, reconstructive and organ transplant surgeries. The PDE is intended for intraoperative visual assessment of blood vessels and related tissue perfusion, by enabling surgeons to observe fluorescent images of blood vessels and related tissue perfusion. Indocyanine green (ICG) is injected intravenously into patients. Infrared light-emitting diodes (LEDs) are used to excite the fluorescence of ICG and illuminate the regions of a patient's body to be observed. A charge coupled device (CCD) camera captures the fluorescent image that is used to assess the blood vessels and related tissue perfusion. The PDE consists of the following components: Camera Unit, Controller, and Remote Controller. The Camera Unit contains a CCD camera and LED light sources and is used either by hand or attaching it to a mechanical arm. The Controller receives the video signal of the fluorescent image from the Camera Unit and outputs the processed fluorescent image to the external video monitor and recorder. Adjustments of the fluorescent image are possible either by the Camera Unit or the Remote Controller.

The provided document describes a 510(k) submission (K110480) for the Hamamatsu Photonics K.K. PDE device, a fluorescent angiographic system. This device is an imaging system used for the visual assessment of blood flow as an adjunctive method for evaluating tissue perfusion and related circulation in certain surgical contexts.

However, the document does not contain the detailed information typically found in a clinical study report that would allow for a comprehensive description of acceptance criteria and a study proving the device meets those criteria, especially in the context of an AI/human-in-the-loop system.

Instead, the submission primarily focuses on establishing substantial equivalence to predicate devices (Novadaq Technologies Inc.'s SPY Imaging System SP2000 and SPY Fluorescent Imaging System SP2001) based on:

• Identical intended use, indications for use, and principles of operation.
• Similar technological characteristics.
• Safety and efficacy confirmation through Hamamatsu's testing and validation activities.

The "Performance data" section lists the following as having been conducted:

1. Electrical per IEC 60601-1.
2. Electromagnetic Compatibility per IEC 60601-1-2.
3. Light Emitting LED Product per IEC 60825-1 (Class1 LED product).
4. Clinical use in Japan for 5 years without adverse events and a review of published literature.

This submission is for a medical imaging device, not an AI algorithm, and therefore the types of studies and acceptance criteria (e.g., MRMC, standalone algorithm performance, ground truth establishment by experts, adjudication methods) relevant to AI-based medical devices are not detailed.

Therefore, based only on the provided text, I cannot complete a table of acceptance criteria and reported device performance, nor can I describe study specifics like sample size for test sets, number of experts, adjudication methods, MRMC studies, or specific ground truth methodologies in the way you've outlined for an AI-centric study.

The document does mention "clinical tests" and states that "All tests demonstrate that the device functions as intended," and refers to "a review of the published literature" and 5 years of clinical use in Japan. However, these are high-level statements and do not provide the granular detail required for your request.

Summary of what can be extracted from the document regarding "proof" the device meets criteria (though not in the requested format):

• Acceptance Criteria (Implied):
  • Compliance with electrical safety (IEC 60601-1).
  • Compliance with electromagnetic compatibility (IEC 60601-1-2).
  • Compliance with LED product safety (IEC 60825-1, Class 1).
  • Functioning as intended for visual assessment of blood flow for tissue perfusion (based on substantial equivalence and lack of adverse events in 5 years of clinical use in Japan).
  • Being at least as safe and effective as predicate devices.
• Reported Device Performance (Implied from the text):
  • "All tests demonstrate that the device functions as intended."
  • "The PDE has been sold and used clinically for 5 years in Japan without any adverse events."
  • "A review of the published literature concludes that the device worked as intended by safely assessing the blood flow and related tissue perfusion during surgeries."
  • The FDA's determination of "substantial equivalence."

In conclusion, the provided 510(k) summary focuses on demonstrating substantial equivalence and compliance with general safety and performance standards for a traditional medical device, not on specific AI algorithm performance metrics or study designs typically employed for AI/ML-based medical devices.

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The Hamamatsu PET Scanner Model SHR-22000 is indicated for the imaging of the distribution in the body of physiological tracer molecules labeled with positron-emitting isotopes. Such images are particularly useful in the assessment of brain function.

The Hamamatsu PET Scanner Model SHR-22000 is similar to previous PET scanners marketed by other companies, but it represents an increase in performance over other PET Scanners through improved resolution. The system was developed in a joint venture with Hitachi Medical Corporation (HMC). HMC provided the imaging workstation, patient table, and user interface, adapted from its nuclear medicine imaging workstation.

The SHR-22000 PET Scanner system consists of five main subsystems: the main gantry with the detector arrays, the signal processing unit, the data acquisition unit, the patient table and positioning sub-system, and the imaging workstation and user interface.

Model SHR-22000 has almost 22000 scintillator segments in its detector arrays, providing very fine resolution. The gantry also has a 6Ge-6Ga source mounted in a stainless steel rod for calibration.

The provided document describes a 510(k) premarket notification for the Hamamatsu PET Scanner Model SHR-22000. This is a medical imaging device, and the submission focuses on demonstrating substantial equivalence to a predicate device, rather than proving performance against specific acceptance criteria for an AI/CADe device.

Therefore, many of the requested elements (acceptance criteria, specific performance metrics, sample sizes for test/training sets, expert ground truth, adjudication methods, MRMC studies, standalone performance, etc.) are not applicable or not explicitly detailed in this type of regulatory submission for a hardware device like a PET scanner.

Here's an breakdown based on the information available:

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

• Acceptance Criteria: Not explicitly stated as quantifiable metrics for a specific diagnostic task like an AI/CADe device. The primary "acceptance criteria" for a 510(k) submission for a traditional medical device are demonstrating substantial equivalence to a legally marketed predicate device. This typically involves demonstrating similar technological characteristics and performance (e.g., image quality, resolution, safety) to the predicate.
• Reported Device Performance: The document states:
  • "Model SHR-22000 has almost 22000 scintillator segments in its detector arrays, providing very fine resolution." (This is a design characteristic, implying improved resolution).
  • "The system was developed in a joint venture with Hitachi Medical Corporation (HMC). HMC provided the imaging workstation, patient table, and user interface, adapted from its nuclear medicine imaging workstation."
  • "Imaging performance tests were carried out to assure equivalence of image characteristics with the predicate device." However, specific numerical performance metrics (e.g., spatial resolution in mm, sensitivity in cps/kBq) are not provided in this summary.

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

• Not applicable / Not explicitly detailed. This document is for a hardware PET scanner. The "testing" mentioned refers to compliance with electrical safety and general imaging performance characteristic equivalence, not a clinical study on a patient test set for a diagnostic algorithm.

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):

• Not applicable. No such clinical test set or ground truth establishment is described in this 510(k) summary for a PET scanner.

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

• Not applicable. See point 3.

5. If a multi reader multi case (MRMC) comparative effectiveness study was done, If so, what was the effect size of how much human readers improve with AI vs without AI assistance:

• No. This is a hardware device (PET scanner), not an AI/CADe algorithm. MRMC studies are typically performed for AI or CADe devices to assess improved reader performance.

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

• Not applicable. This describes a hardware device, not an algorithm.

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

• Not applicable. No clinical ground truth is mentioned for performance evaluation in this 510(k) summary. The "testing" focused on device characteristics and safety.

8. The sample size for the training set:

• Not applicable. This is a hardware device; there isn't a "training set" in the context of an AI algorithm.

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

• Not applicable. See point 8.

Summary for the Hamamatsu PET Scanner Model SHR-22000:

This 510(k) summary demonstrates that the Hamamatsu PET Scanner Model SHR-22000 is substantially equivalent to the Siemens ECAT Exact HR PET scanner (K962797). The equivalence is based on similar indications for use, technological characteristics (coincidence detection, scintillator segments, image reconstruction), and the results of imaging performance tests to ensure similar image characteristics. The details provided are typical for a hardware device submission, focusing on safety and equivalence to established technology rather than rigorous clinical performance metrics for a diagnostic algorithm.

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