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The Bioptics 10 X 10 VISION is a digital specimen radiography add-on upgrade to previously released cabinet x-ray systems. The 10 X 10 Vision is used for digital specimen radiography to provide rapid verification that the correct tissue has been excised during excisional or percutaneous biopsy. The 10 X 10 Vision is excluded from any use in in-vivo diagnostic or screening mammography applications.

The 10 X 10 VISION will be marketed as a digital specimen radiography add-on upgrade for standard film-based cabinet x-ray systems. using high-performance, flat panel CMOS detector, and high-resolution Gd202S:Tb (Gadolinium Oxysulfide) scintillator technologies. Bioptics digital x-ray imagers are packaged in a thin (<14 mm) form factor similar to standard film cassette, include power supply, cables and needed mechanical accessories for convenient and cost-efficient upgrade of existing film-based cabinet x-ray systems. The x-ray imager assembly is installed permanently in the detector/cassette housing compartment or at the base of the host cabinet x-ray unit. The passive x-ray detection technology does not emit radiation and its low power (<5W) consumption does not require additional cooling. The 10 X 10 VISION employs the use of Bioptics Vision image acquisition software. The Bioptics Vision software handles digital x-ray image acquisition, calibration, image display, image analysis and manipulation, patient database, image archiving, and transmittal. Bioptics Vision software is the central part of this system. Bioptics Vision software is compliant with Digital Imaging and Communications in Medicine (DICOM) 3.0, and comes with DICOM Print, Store and Modality Work List (MWL).

The submission describes a digital specimen radiography add-on upgrade called "10 X 10 VISION" intended for use with existing film-based cabinet x-ray systems. The device provides digital x-ray imaging for rapid verification of excised tissue during biopsy procedures.

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

1. Table of Acceptance Criteria and Reported Device Performance

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

The provided document does not specify a sample size for a test set in the context of clinical or image-based performance evaluation. The testing described primarily focuses on engineering verification and validation of the device's technical specifications and software functionality.

The data provenance is for engineering verification and validation data, not patient data. It is internal company data generated during device development ("Extremely controlled and detailed design inputs and outputs define Bioptics product development activities").

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

The document does not mention the use of experts or the establishment of ground truth by experts for a test set. This type of evaluation is typically associated with clinical performance studies, which were explicitly stated as not being conducted or required. The testing performed was technical (software validation, compliance with radiation standards).

4. Adjudication Method for the Test Set

As there is no mention of a test set involving human interpretation of images or clinical outcomes, there is no adjudication method described. The "review" of performance data was done by "appropriate management" within Bioptics, Inc.

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

No, a multi-reader multi-case (MRMC) comparative effectiveness study was not done. The submitter explicitly states: "To the submitters knowledge, the predicate device...did not provide or reference any clinical tests submitted in compliance with 807.92(b)(2), therefore the submitter believes such clinical testing is not appropriate or required by FDA and has not made or provided any summary of such testing."

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

The primary studies described are related to the standalone performance of the device's technical attributes and software functionality, but not in the context of an "algorithm only" with clinical outcomes without human interaction. This device is an imaging system, not an AI diagnostic algorithm. The "standalone" testing refers to the system meeting its design specifications and processing images as intended.

7. The Type of Ground Truth Used

For the technical and software validation, the "ground truth" was essentially the device's own design specifications and requirements, along with compliance standards (e.g., DICOM, 21 CFR 1020.30/1020.40). The testing confirmed that the device operated according to these pre-defined technical benchmarks. There was no "expert consensus," "pathology," or "outcomes data" ground truth established for the purpose of proving diagnostic accuracy.

8. The Sample Size for the Training Set

The document does not specify a sample size for a training set. This is expected as the device is a digital imaging system, not an AI model that typically requires a large training dataset for learning. The software "handles digital x-ray image acquisition, calibration, image display," etc., implying standard image processing and display functionalities, not machine learning model training based on a dataset.

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

Since there is no mention of a training set in the context of an AI/ML model, the concept of establishing ground truth for a training set is not applicable to this submission. The validation focused on the system's engineering and software adherence to specifications.

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The BioVision Digital Specimen Radiography (DSR) System is a cabinet digital X-ray imaging system intended to generate and control X-rays for examination of various anatomical regions, and to provide rapid verification that the correct tissue has been excised during excisional or percutaneous biopsy.

Performing the verification directly in the same biopsy procedure room enables cases to be completed faster, thus limiting the time the patient needs to be under examination. Specimen radiography can potentially limit the number of patient recalls. This device is intended to be operated wherever the medical professionals deem appropriate, including a surgical suite or a room adjacent to a surgical suite.

The BioVision Digital Specimen Radiography (DSR) System is a stand-alone cabinet digital X-ray imaging system to provide rapid verification that the correct tissue has been excised during percutaneous biopsy.

Performing the verification directly in the same biopsy procedure room enables cases to be completed faster, thus limiting the time the patient needs to be under examination. Specimen radiography can potentially limit the number of patient recalls.

The BioVision Digital Specimen Radiography (DSR) System employs the use of Bioptics Vision image acquisition software. The Bioptics Vision software handles the digital X-ray image acquisition, calibration, image display, image analysis and manipulation, patient database, image archiving, and transmittal. Bioptics Vision software is the central part of this system. Bioptics Vision software is Digital Imaging and Communications in Medicine (DICOM) 3.0 compliant and comes with DICOM Print, Store and Modality Work List (MWL).

Here's a breakdown of the acceptance criteria and the study information for the BioVision Digital Specimen Radiography (DSR) System, based on the provided 510(k) summary:

1. Table of Acceptance Criteria and Reported Device Performance

The provided document details the design specifications and testing performed to demonstrate substantial equivalence to a predicate device, rather than explicit acceptance criteria and performance against those criteria in a tabular format typical for AI/CADe devices. However, we can infer the acceptance criteria and reported performance from the provided specifications and claims. The primary acceptance criterion is substantial equivalence to the predicate device, the piXarray 100 Digital Specimen Radiography (DSR) System, focusing on technological characteristics and safety.

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

The document does not specify a test set in the context of clinical data, as the device is not an AI/CADe device that performs automated analysis on patient images. The testing conducted was primarily related to hardware and software validation, and safety compliance.

• Sample Size for Test Set: Not applicable in the context of clinical image analysis. The "test set" for hardware and software validation is not quantified in terms of number of cases or images.
• Data Provenance: Not applicable for clinical data. The validation activities mentioned are internal and related to device design and function. The document mentions "a few devices, labeled 'Research Use Only,' are being placed to further document the submitter's performance claims and attempt to identify any unknown hazards," which suggests some limited, prospective, real-world deployment for further internal evaluation, but not as a formal clinical test set for regulatory submission.

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

This information is not provided as the submission does not detail a study involving expert-established ground truth on a clinical test set. The device is a digital X-ray imaging system, not an AI/CADe algorithm making diagnostic assessments.

4. Adjudication Method for the Test Set

This information is not provided as there is no mention of a formal clinical test set requiring adjudication in the context of image interpretation or diagnostic accuracy.

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

There was no MRMC comparative effectiveness study performed. Since this device is a digital specimen radiography system and not an AI/CADe or diagnostic aid intended to assist human readers, such a study is not relevant to its regulatory submission.

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

A "standalone" performance study in the context of an algorithm's diagnostic accuracy was not performed. This device is a digital X-ray imaging system that produces images for human interpretation, not an algorithm that outputs a diagnostic result independently. The software in question (Bioptics Vision) handles image acquisition, display, analysis, and manipulation, not independent diagnostic assessment.

7. The Type of Ground Truth Used

The concept of "ground truth" as it applies to clinical diagnostic accuracy (e.g., pathology, outcomes data) is not relevant for this submission. The ground truth for the device's performance relates to its ability to accurately acquire and display X-ray images, adhere to safety standards (e.g., radiation leakage), and perform its stated functions in line with design specifications. The acceptance of the device's functionality is based on its ability to produce images that allow medical professionals to "rapidly verify that the correct tissue has been excised."

8. The Sample Size for the Training Set

This information is not applicable. The BioVision Digital Specimen Radiography (DSR) System is a hardware device with controlling software, not an AI/ML algorithm that is "trained" on a dataset. The software performs image acquisition and processing based on deterministic algorithms, not learned patterns from a training set.

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

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

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The Core Vision Digital Specimen Radiography (DSR) System is a cabinet digital X-ray imaging system intended to generate and control X-rays for examination of various anatomical regions, and to provide rapid verification that the correct tissue has been excised during percutaneous biopsy.

Performing the verification directly in the same biopsy procedure room enables cases to be completed faster, thus limiting the time the patient needs to be under examination. Specimen radiography can potentially limit the number of patient recalls. This device is intended to be operated wherever the medical professionals deem appropriate, including a surgical suite or a room adjacent to a surgical suite.

The Core Vision Digital Specimen Radiography (DSR) System is a stand-alone cabinet digital X-ray imaging system to provide rapid verification that the correct tissue has been excised during percutaneous biopsy.

Performing the verification directly in the same biopsy procedure room enables cases to be completed faster, thus limiting the time the patient needs to be under examination. Specimen radiography can potentially limit the number of patient recalls.

The Core Vision Digital Specimen Radiography (DSR) System employs the use of Bioptics Vision image acquisition software. The Bioptics Vision software handles the digital X-ray image acquisition, calibration, image display, image analysis and manipulation, patient database, image archiving, and transmittal. Bioptics Vision software is the central part of this system. Bioptics Vision software is Digital Imaging and Communications in Medicine (DICOM) 3.0 compliant and comes with DICOM Print, Store and Modality Work List (MWL).

Here's a breakdown of the acceptance criteria and the study information for the Core Vision Digital Specimen Radiography (DSR) System, based on the provided text:

1. Table of Acceptance Criteria and Reported Device Performance

The provided document does not explicitly present a "table of acceptance criteria" in the format typically seen with quantitative metrics for accuracy, sensitivity, or specificity. Instead, the submission focuses on demonstrating substantial equivalence to a predicate device (piXarray 100 Digital Specimen Radiography (DSR) System, K052433) by showing that it has the same indications for use and similar technological characteristics, with improvements or non-significant differences.

The primary "acceptance criteria" here appear to be:

• Compliance with regulatory standards: Adherence to 21 CFR 1020.40 for Cabinet X-ray systems, and meeting radiation emission limits.
• Performance as intended: The device performs its intended functions (rapid verification of excised tissue, high-resolution digital imaging, DICOM compliance, etc.).
• Equivalence to predicate device: The new device functions at least as well as the predicate device.

Given this, a table summarizing the "acceptance criteria" based on the provided text would look like this:

Study Details:

It's important to note that this 510(k) submission primarily relies on non-clinical performance data testing and review to demonstrate substantial equivalence, rather than a traditional clinical study with defined acceptance criteria for diagnostic performance metrics.

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

   • Test Set Sample Size: Not specified in terms of number of images or patients. The testing appears to be focused on engineering verification and validation of system functionality and compliance, rather than a clinical dataset for diagnostic performance.
   • Data Provenance: Not applicable in the context of a clinical test set. The data would be derived from internal engineering testing, hardware performance checks, and software validation.

2. 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 as no clinical ground truth establishment is described for a diagnostic performance test set. The validation was against engineering specifications and regulatory compliance.

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

   • Not applicable. There was no clinical ground truth established or adjudicated in this type of submission.

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 comparative effectiveness study was done. The device is a digital X-ray system, not an AI-assisted diagnostic tool for human readers.

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

   • The term "standalone" in this context refers to the device functioning independently (algorithm only without human-in-the-loop performance). The device itself (the Core Vision DSR System) is a standalone imaging system. The performance testing was of this system's ability to acquire, process, and display images according to its specifications and regulatory requirements. It's not an algorithm that outputs a diagnostic decision, but an imaging system for practitioners to use.

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

   • The "ground truth" for this submission was primarily engineering specifications, regulatory standards (like 21 CFR 1020.40), and the performance characteristics of the predicate device. For example, the ground truth for radiation emission was the specified limit of 0.5 milliroentgen.

7. The sample size for the training set:

   • Not applicable. This device is an imaging system, not an AI model that requires a training set of data for learning. Its software handles image acquisition, processing, and display, which are functional tasks, not machine learning tasks.

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

   • Not applicable, as there was no training set for an AI model.

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The piXarray 100 Digital Specimen Radiography (DSR) System is a cabinet digital Xray imaging system intended to generate and control X-rays for examination of various anatomical regions, and to provide rapid verification that the correct tissue has been excised during excisional or percutaneous biopsy.

Performing the verification directly in the same biopsy procedure room enables cases to be completed faster, thus limiting the time the patient needs to be under examination. Specimen radiography can potentially limit the number of patient recalls. This device is intended to be operated wherever the medical professionals deem appropriate, including a surgical suite or a room adjacent to a surgical suite.

The piXarray 100 Digital Specimen Radiography (DSR) System is a standalone cabinet digital X-rav imaging system to provide rapid verification that the correct tissue has been excised during excisional or percutaneous biopsy.

Performing the verification directly in the same biopsy procedure room enables cases to be completed faster, thus limiting the time the patient needs to be under examination. Specimen radiography can potentially limit the number of patient recalls.

The piXarray 100 Digital Specimen Radiography (DSR) System employs the use of Biopix image acquisition software. The Biopix software handles the digital X-ray image acquisition, calibration, image display, image analysis and manipulation, patient database, image archiving, and transmittal. Biopix software is the central part of this system. Biopix software is Digital Imaging and Communications in Medicine (DICOM) 3.0 compliant and comes with DICOM Print, Store and Modality Work List (MWL).

Here's an analysis of the provided text regarding the piXarray 100 Digital Specimen Radiography (DSR) System, focusing on acceptance criteria and study details.

Based on the provided document, the submission for the piXarray 100 Digital Specimen Radiography (DSR) System (K052433) is a 510(k) premarket notification. This type of submission primarily relies on demonstrating substantial equivalence to a legally marketed predicate device, rather than extensive clinical studies proving new acceptance criteria.

The acceptance criteria are therefore focused on meeting regulatory standards and demonstrating equivalence to the predicate, the Mammopath™ cabinet X-ray system (K021113).

Here's the breakdown of the information requested, based on the provided text:

1. Table of Acceptance Criteria and Reported Device Performance

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

The document does not specify a sample size for a test set in the context of clinical or performance data for image interpretation. The testing described is primarily for engineering, software, and regulatory compliance.

• Sample Size (Test Set): Not specified for image-based performance evaluation. The "Alpha validation testing" implies internal software and system functionality testing, but not a dataset of medical images for diagnostic performance.
• Data Provenance: Not applicable in the context of image data. The testing mentioned is internal to the manufacturer (Bioptics, Inc.) for system and software validation.

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

Not applicable. The submission does not describe a study involving expert readers establishing ground truth for a test set of medical images for diagnostic purposes. The "ground truth" for the engineering and software validation would be the design specifications and expected system behavior.

4. Adjudication Method for the Test Set

Not applicable, as no external test set requiring expert adjudication is described 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

No. The document explicitly states: "To the submitter's knowledge, the predicate device, Mammopath cabinet X-ray system, did not provide or reference any clinical tests submitted in compliance with 807.92(b)(2), therefore the submitter believes such clinical testing is not appropriate or required by FDA and has not made or provided any summary of such testing."

This indicates that no clinical comparative effectiveness study, including an MRMC study with or without AI assistance, was performed or presented as part of this 510(k) submission. The device is for "Digital Specimen Radiography," which typically involves immediate visual verification by a clinician, not necessarily a complex AI-assisted diagnostic workflow with multiple readers.

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

No specific standalone algorithm performance study is described as part of this submission. The device is a "system" (piXarray 100 DSR System) that includes the Biopix software, which "handles the digital X-ray image acquisition, calibration, image display, image analysis and manipulation, patient database, image archiving, and transmittal." This is an integrated system for image generation and display for human interpretation, not an AI algorithm intended for standalone diagnostic output.

7. The Type of Ground Truth Used

The primary "ground truth" for this submission revolves around:

• Regulatory Standards: Compliance with 21 CFR 1020.40 and general performance standards for X-ray emitting products.
• Predicate Device Characteristics: The functional and technological characteristics of the Mammopath cabinet X-ray system.
• Design Specifications: For the internal verification and validation of the piXarray system's own design, software, and hardware.

There is no mention of ground truth established from pathology, expert consensus on image findings, or patient outcomes data for clinical diagnostic performance.

8. The Sample Size for the Training Set

No training set is mentioned or applicable, as this device primarily relies on traditional X-ray imaging principles and software for image acquisition, processing, and display for human interpretation, rather than a machine learning or AI model that requires a training set of images.

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

Not applicable, as there is no mention of a training set.

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