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The Image 1 S is a camera control unit (CCU) for use with camera heads or video endoscopes for the visualization, image recording and documentation during general endoscopic and microscopic procedures.

The Image 1 S 4U camera head is intended to the Image1 S Camera Control Unit (CCU) and compatible endoscope for visualization, image recording and documentation during general endoscopic and microscopic procedure.

The Image1 S camera control unit is a medical device which consists of the Image1 S Connect (TC200US), Image1 S Connect II (TC201US) modules and the link modules. The link modules are Image1 S H3-Link (TC300US), Image1 S X-Link (TC301US), Image1 S D3-Link (TC302US) and Image1 S 4U-Link (TC304US).

The Image1 S Connect (TC200US) and the Image1 S Connect II (TC201US) modules can be connected to a minimum of one and a maximum of three links modules. The modularity enables customers to customize their Image1 S system to their specific current and future video needs.

The Image1 S includes, but not limited to, the following features:
• Brightness control
• Enhancement Control
• Automatic Light Source Control
• Shutter Control
• Image/Video Capture

When the Image1 S Connect II module is used with the 4U-Link and the Image1 S 4U camera head, it can output a 4K image to the monitor and also offers 7 increments of zoom ranging from 1x to 2.5x.

The software version of the Image1 S camera control is upgraded to version 4.0. Software version 4.0 introduces the KS HIVE, an Ethernet based interface that allows for communication between the Image 1 S camera control unit and certain KARL STORZ devices.

This is a premarket notification (510(k)) for an endoscopic video imaging system, the Image1 S CCU and Image1 S 4U Camera Head. The primary purpose of this 510(k) is to demonstrate that the new device is substantially equivalent to a previously cleared predicate device (Image1 S, K160044). Therefore, the "acceptance criteria" and "study that proves the device meets the acceptance criteria" in this context refer to the testing and analysis performed to demonstrate this substantial equivalence, rather than a clinical outcome-based performance study typically associated with AI/ML devices.

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

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

Since this is a substantial equivalence submission for an imaging system rather than an AI/ML diagnostic or prognostic device, the "acceptance criteria" are related to safety, effectiveness, and technological characteristics compared to the predicate device. The "reported device performance" are the results of the non-clinical tests demonstrating these aspects.

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

• Sample Size for Test Set: The document does not specify a "test set" in the context of clinical images or patient data that would typically be used for AI/ML performance evaluation. The testing primarily involved non-clinical bench testing, software verification/validation, and compliance with standards. Therefore, the "sample size" would refer to the various components and systems tested, but no numerical count is provided for this.
• Data Provenance: Not applicable as no clinical patient data was used for performance evaluation in this 510(k). The testing is primarily engineering and regulatory compliance based.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts (e.g. radiologist with 10 years of experience)

• Number of Experts & Qualifications: Not applicable. Ground truth, in the sense of clinical annotations by experts for AI/ML performance, was not established as this is not an AI/ML diagnostic device with a clinical performance study. The "ground truth" here is compliance with engineering standards, design specifications, and the functionality of the device as tested by engineers and technicians.

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

• Adjudication Method: Not applicable. There was no clinical ground truth established requiring expert adjudication.

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

• MRMC Study: No, an MRMC comparative effectiveness study was not done. This device is an endoscopic video imaging system, not an AI/ML diagnostic assistant, so such a study is not relevant to this submission. The document explicitly states: "Clinical testing was not required to demonstrate the substantial equivalence to the predicate devices. Non-clinical bench testing was sufficient to establish the substantial equivalence of the modifications."

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

• Standalone Performance: Not applicable. This is not an AI/ML algorithm. The "standalone performance" refers to the device's functional operation (imaging, brightness control, zoom, etc.) as detailed in the non-clinical performance data and bench testing, which was indeed performed without human-in-the-loop diagnostic interpretations.

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

• Type of Ground Truth: For this device, the "ground truth" used is primarily engineering specifications, compliance with recognized international standards (e.g., IEC 60601 series, ANSI/AAMI reprocessing standards), and the functional design requirements of the imaging system. It's not clinical ground truth like pathology or expert consensus on disease.

8. The sample size for the training set

• Training Set Sample Size: Not applicable. This device does not involve an AI/ML algorithm that would require a "training set" of clinical data.

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

• Training Set Ground Truth Establishment: Not applicable, as there is no AI/ML training set.

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The TELE PACK + is an all-in-one Imaging System, which comprises a light source for illumination, Camera Control Unit (CCU) for use with compatible camera heads or video endoscopes for image processing, as well as a monitor for image display, intended for the visualization of endoscopic and microscopic procedures.

The Telepack + is a portable and compact all-in-one imaging system that includes a 18.5 inch screen display, a camera control unit and internal LED light source, that is intended to be connected to a compatible device (camera head or videoendoscope) for the purpose of visualization and documentation of endoscopic and microscopic procedures as well as stroboscopy. The Telepack + includes a LED illumination light source to illuminate the intended area and a 18.5 inch monitor for display. It also allows the users to redefine the functions that take place when a button is pressed. The Telepack + is a non-patient contacting and require only wipe down as needed.

The provided text is a 510(k) Summary for the Telepack + imaging system. It describes the device, its intended use, and the non-clinical performance data used to demonstrate substantial equivalence to a predicate device. However, it does not contain information about:

• Acceptance criteria in the context of an AI/algorithm-driven device's performance metrics (e.g., sensitivity, specificity, AUC).
• A study proving the device meets these acceptance criteria through a comparison of algorithm performance against a ground truth dataset, or human reader performance.
• Sample sizes for test sets where ground truth is established by experts.
• The number or qualifications of experts used for establishing ground truth.
• Adjudication methods for ground truth.
• Multi-reader multi-case (MRMC) comparative effectiveness studies.
• Effect sizes of human reader improvement with AI assistance.
• Standalone algorithm performance studies.
• Specific types of ground truth (e.g., pathology, outcomes data for a disease).
• Sample size for training sets.
• How ground truth for training sets was established.

The "Performance Testing" section within the "Non-Clinical Performance Data" discusses basic image quality characteristics like "Minimum Illumination," "Spatial Resolution," "Color Performance," "Latency," "White Balance," and "AE Step Response," along with "Head Button Functionality." These are engineering performance metrics for an imaging system, not performance metrics for an AI/algorithm diagnosing or assisting in diagnosis.

The document explicitly states: "Clinical performance is not required to demonstrate substantial equivalence to the predicate devices. Non-clinical bench testing was sufficient to establish substantial equivalence." This indicates that the Telepack + is a hardware imaging system, and its approval was based on demonstrating that its image quality and functionality are comparable to its predicate, rather than on the diagnostic performance of an embedded AI algorithm.

Therefore, I cannot provide the requested information based on the given text.

In summary, the provided document does not contain the information required to answer your prompt because the device is an imaging system, not an AI/algorithm-driven diagnostic aid.

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This 3D Visualization System is intended to compose the imaging signals from video system center and convert them into 3D signals displayed on the monitor.

The 3D Visualization System can convert 2D endoscopic images synchronously. It is equipped HD-SDI and HDMI outputs ports which are compatible with 3D monitors of various interfaces. The 3DVS-S100 series 3D Visualization System includes 5 models, which are 3DVS-S100A, 3DVS-S100B, 3DVS-S100C, 3DVS-S100D and 3DVS-S100E. The differences between the models are in the number and type of imaging modes supported (single-lens endoscope with enhanced or standard 3D effects, and dual-lens endoscope with enhanced or standard 3D effects). The system should be used with endoscopic image processors which have HDMI or SDI output interface, and monitors which have SDI, HDMI or DVI interface. The device is provided non-sterile and for repeat use, does not have patient-contact, and is intended for use by a qualified healthcare professional and is not for home use.

The provided document describes the Scivita Medical Technology Co., Ltd. 3D Visualization System (K183675). The document clarifies that this device is intended to process imaging signals from a video system center and convert them into 3D signals for display on a monitor. The FDA's 510(k) clearance process focuses on substantial equivalence to a predicate device, rather than explicit acceptance criteria with numerical performance targets for the proposed device itself. However, the document does describe non-clinical testing conducted to demonstrate this equivalence and ensure the device meets design specifications.

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

1. Table of Acceptance Criteria and Reported Device Performance

• IEC 60601-1-2005+CORR.1:2006+CORR.2:2007+A1:2012 (General requirements for basic safety and essential performance)
• IEC 60601-1-2:2014 (Electromagnetic compatibility)
• IEC 60601-2-18:2009 (Particular requirements for endoscopic equipment) |
  | Software Validation | The software was validated in accordance with FDA guidance documents:
• "Guidance for the Content of Premarket Submissions for Software Contained in Medical Devices"
• "Off-The-Shelf Software Use in Medical Devices"
• "Cybersecurity for Networked Medical Devices Containing Off-the-Shelf (OTS) Software"
• "General Principles of Software Validation" |
  | Image Quality Equivalence (2D and 3D) | Image quality performance tests were conducted to quantitatively compare the proposed device and predicate devices for both 2D and 3D images.
  Parameters evaluated:
• Field of view
• Direction of view
• Depth of field
• Geometric distortion
• Noise and dynamic range
• Intensity uniformity
• Artifacts
• Image frame frequency and system delay

Result: The image quality of the proposed device was equivalent to that of the predicate device (OLYMPUS LTF-190-10-3D ENDOEYE FLEX 3D DEFLECTABLE VIDEOSCOPE, MAJ-YO154 3D PROCESSOR, OLYMPUS CV-190, EVIS EXERA III VIDEO SYSTEM CENTER - K123365). This equivalence was tested across all four modes of the proposed device in both described combinations. |
| Substantial Equivalence to Predicate Device (K123365) | The non-clinical performance testing summarized supported a substantial equivalence determination, demonstrating the subject device is as safe and as effective as the legally marketed predicate device. |

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

The document does not specify the sample size (e.g., number of images, cases, or videos) used for the image quality performance tests. It vaguely states "image quality performance tests were conducted to quantitatively compare the proposed device and predicate devices."

The data provenance (country of origin, retrospective/prospective) is also not mentioned. Given the manufacturer is based in China, it's plausible the testing was conducted there, but this is not explicitly stated.

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

The document does not mention the use of experts or any process for establishing ground truth as typically understood in a clinical study (e.g., for diagnostic accuracy). The testing described is purely technical and comparative against a predicate device's performance characteristics.

4. Adjudication Method for the Test Set

As no expert review or human assessment of diagnostic accuracy is mentioned, there is no adjudication method described.

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

No MRMC comparative effectiveness study is mentioned. The study described is a non-clinical, technical performance comparison between the proposed device and a predicate device, focusing on image quality characteristics, not on human reader performance with or without AI assistance. The device's function is to convert existing video signals into 3D signals, not to provide AI-assisted diagnostic capabilities.

6. Standalone (Algorithm Only Without Human-in-the-Loop Performance) Study

Yes, a form of standalone performance was assessed. The "image quality performance tests" were conducted on the device itself, comparing its output directly to the predicate device's output based on various technical image parameters. This is an evaluation of the algorithm's output (3D visualization) in isolation from human interpretation for diagnostic purposes.

7. Type of Ground Truth Used

For the non-clinical image quality tests, the "ground truth" was implicitly derived from technical performance metrics of the predicate device and established engineering standards for image quality. It was a comparative measurement against the performance characteristics of the legally marketed predicate device, not against clinical outcomes, pathology, or expert consensus on a diagnostic task.

8. Sample Size for the Training Set

The document does not mention a training set. This device is a "3D Visualization System" that converts video signals. It does not appear to be an AI/ML-driven diagnostic or image analysis tool that would typically involve a "training set" in the machine learning sense. Its function is signal processing and conversion.

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

As no training set is mentioned, this section is not applicable.

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The KARL STORZ Endoscopic ICG Imaging System is intended to provide real-time endoscopic visible and nearinfrared fluorescence imaging. The KARL STORZ ICG Imaging System enables surgeons to perform minimally invasive surgery using standard endoscopic visible light as well as visual assessment of vessels, blood flow and related tissue perfusion, and at least one of the major extra-hepatic bile duct, common bile duct and common hepatic duct), using near-infrared imaging.

Fluorescence imaging of biliary ducts with the KARL STORZ Endoscopic ICG Imaging System is intended for use with standard of care white light and, when indicated, intraoperative cholangiography. The device is not intended for standalone use for biliary duct visualization.

The KARL STORZ Endoscopic ICG Imaging System is used to provide real-time high definition (HD) endoscopic video images of visible (VIS) and near-infrared (NIR) indocyanine green (ICG) dye fluorescence during minimally invasive surgery. The system components are rigid ICG Endoscopes for VIS and NIR illumination and imaging, a light source with foot switch for emission of VIS and NIR illumination, a 3 CCD (charge coupled device) color video camera head capable of capturing both VIS and NIR imaging, and KARL STORZ Indocyanine Green (ICG Kit). Additional accessories used with the KARL STORZ Endoscopic ICG Imaging System include a standard fiber-optic light cable for transmission of VIS and NIR light and the Image 1 SPIES Camera Control Unit (CCU) cleared in K131953. The KARL STORZ Endoscopic ICG Imaging System can be used with any medical grade HD monitor with a DVI-D or 3G-SDI input. The KARL STORZ Endoscopic ICG Imaging System is a Class II device under 21 CFR 876.1500.

This document is a 510(k) summary for the KARL STORZ Endoscopic ICG Imaging System (K152583). It explicitly states that clinical performance data was not required to demonstrate substantial equivalence to the predicate device. Therefore, no studies were conducted to prove the device meets specific acceptance criteria based on human performance. The following information is based on the provided text, and many sections will indicate that the information is not available as no clinical study was performed.

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

Since no clinical study was performed to establish performance against acceptance criteria in a clinical setting, this table cannot be populated with device performance. The non-clinical performance data focused on functional and safety aspects.

The document mentions non-clinical performance data:

• "The KARL STORZ Endoscopic ICG Imaging System has been successfully tested for its functions and performance, including verification that the spectral characteristics of the ICG system illumination light source, light transmission system and endoscope enable a selective visualization of the ICG fluorescence signal as detected by the camera system."
• "Furthermore, a GLP animal study was successfully performed by the NAMSA testing facility to evaluate the performance of the KARL STORZ Endoscopic ICG Imaging System in the porcine minimally-invasive laparoscopy model."
• Safety testing: IEC 60601-1 and 60601-2-18 (electrical safety), IEC 60601-1-2 (electromagnetic compatibility), ISO 10993 (biocompatibility).
• Additional validations for system software, manual cleaning method, and sterilization process.

These are functional and safety-related performance measures, not clinical efficacy or diagnostic accuracy metrics typically found in acceptance criteria for AI/diagnostic devices.

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, as no clinical test set was used for human performance evaluation. A GLP animal study was performed in a porcine model. The specific sample size for the animal study is not provided, nor is the country of origin.

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, as no clinical test set requiring expert ground truth was used.

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

Not applicable, as no clinical test set was used.

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

Not applicable. No MRMC study was done, as clinical testing was not required and the device is an imaging system, not explicitly an AI/diagnostic algorithm that enhances human reader performance in a comparative study.

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

This device itself is an imaging system. It performs a standalone function of capturing and displaying images. However, the FDA states, "The device is not intended for standalone use for biliary duct visualization," meaning it requires a human surgeon's interpretation in conjunction with white light imaging and potentially intraoperative cholangiography. No 'algorithm-only' performance was assessed independently of the visual output for a human.

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

For the non-clinical functional tests, the ground truth would have been established by engineering specifications and physical measurements. For the GLP animal study, the "performance" evaluation would likely involve visual assessment by qualified personnel regarding the visualization of ICG fluorescence in the porcine model. There is no mention of pathology or outcomes data as ground truth.

8. The sample size for the training set

Not applicable. This device is an imaging system, not an AI/ML algorithm that requires a training set in the conventional sense.

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

Not applicable, as there was no training set for an AI/ML algorithm.

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The IMAGE1 SPIES is a camera control unit (CCU) for use with camera heads or video endoscopes for the visualization. image recording and documentation during general endoscopic and microscopic procedures.

The modified Image1 SPIES (Storz Professional Image Enhancement System) camera control unit includes an additional camera head that can be connected to the X-link module and software updates for improvement of existing features and bug fixes. The SPIES Camera System is intended and designed for use during endoscopic and microscopic procedures.

The document provided is a 510(k) summary for the Karl Storz Endoscopy America, Inc. Image1 SPIES System. This type of submission is for demonstrating substantial equivalence to a predicate device, not typically for proving a device meets
clinical acceptance criteria through a specific study in the way a novel AI algorithm might.

Based on the provided text, here's a breakdown of the acceptance criteria and the study that "proves" the device meets them, keeping in mind the context of a 510(k) for an endoscopic camera system:

1. Table of Acceptance Criteria and Reported Device Performance

The document does not provide specific numerical acceptance criteria (e.g., minimum resolution in lp/mm, acceptable latency in ms) in a table format. Instead, it states that the device "met all its specifications." The performance validation involved a series of bench tests.

The "bench test data for the Image1 SPIES System demonstrates that the design characteristics used as the basis for the comparison have been met. The results show that the subject device has met all its specifications. The performance validation test report can be provided upon request."

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

• Test Set Sample Size: The document does not specify a "sample size" in terms of cases or patients for the bench tests. The testing performed was on the device itself and its components.
• Data Provenance: Not applicable in the context of clinical data. The tests were bench tests conducted on the device.

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

• Number of Experts: Not applicable. The ground truth for bench tests is based on engineering specifications and direct physical measurements, not expert consensus on medical images.

4. Adjudication Method for the Test Set

• Adjudication Method: Not applicable. Bench testing relies on objective measurements against predefined technical specifications.

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

• MRMC Study: No, a Multi-Reader Multi-Case (MRMC) comparative effectiveness study was not performed. This type of study is typically done for diagnostic aids or AI algorithms that assist human readers in interpreting medical images, assessing the impact on diagnostic accuracy, sensitivity, or specificity. The Image1 SPIES System is a camera control unit for visualization, not a diagnostic AI tool.

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

• Standalone Study: Yes, in a sense, the bench testing performed is analogous to a standalone performance evaluation for the device's technical specifications. The performance of the camera control unit itself (resolution, brightness, etc.) was evaluated independently of human interpretation of clinical outcomes. However, this is not an "algorithm only" study as would be described for AI.

7. The Type of Ground Truth Used

• Type of Ground Truth: The ground truth for the bench tests was based on engineering specifications and objective physical measurements. For example, resolution would be measured against a standard test pattern, and latency would be measured with specialized equipment.

8. The Sample Size for the Training Set

• Training Set Sample Size: Not applicable. This device is not an AI/ML algorithm that requires a training set.

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

• Ground Truth for Training Set: Not applicable, as there is no training set for this device.

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3D TIPCAM®1: The Rigid Videoendoscope is intended to be used together with the camera control unit is for use during diagnostic and/or surgical procedures when endoscopic video assistance is required. For use in all endoscopy and endoscopic surgery within the peritoneal and thoracic cavity, including gynecological anatomy.

IMAGE1 SPIES is a camera control unit (CCU) for use with camera heads or videoendoscopes for the visualization and documentation of endoscopic and microscopic procedures.

The SPIES 3D System is intended for use during diagnostic and/or surgical procedures when endoscopic video assistance is required within the peritoneal and thoracic cavity, including gynecological and urological anatomy.

The SPIES 3D System is a medical device system which consists of the camera control unit (CCU) – a combination of the previously 510k cleared device (K131953) – Image1 Connect module (TC200) and the D3-Link module, and 3D Tipcam®1.

The provided text is a 510(k) summary for the Karl Storz Endoscopy-America, Inc. SPIES 3D System. Based on the information provided, here's a detailed breakdown of the acceptance criteria and study information:

1. Table of Acceptance Criteria and Reported Device Performance

The document does not explicitly present acceptance criteria in a quantitative table format with corresponding reported performance values for each criterion. Instead, it lists the types of performance characteristics that were tested and states that the device "met all its specifications" and "met all its design specification." Since specific numerical criteria and corresponding results are not given, the table below will summarize the areas of performance validated and the general conclusion.

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

The document explicitly states: "No clinical information is required for this submission." The performance data presented are from bench tests and non-clinical performance tests.

• Sample Size for Test Set: Not applicable for a clinical test set, as no clinical studies were performed. For bench tests, the sample size is not specified but would typically involve one or a small number of devices tested under various conditions.
• Data Provenance: The data provenance is from internal non-clinical performance and bench testing conducted by the manufacturer (Karl Storz Endoscopy-America, Inc.). It is retrospective in the sense that the tests were performed on finished devices, but the data itself is from laboratory settings, not patient data. Country of origin of data is not specified but implicitly North America (where the applicant is located) and/or Germany (where Karl Storz is headquartered).

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

Not applicable. No clinical test set was used, and thus no expert ground truth panels were involved. The "ground truth" for the non-clinical tests would be the established engineering and performance specifications, and the judgment was made by engineers and quality assurance personnel performing the tests.

4. Adjudication Method for the Test Set

Not applicable. Since no clinical test set requiring human interpretation or judgment was used, there was no adjudication method involving multiple experts.

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

No. The document explicitly states: "No clinical information is required for this submission." Therefore, no MRMC study was performed.

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

Yes, in essence. The "standalone" performance here refers to the device's technical specifications and functionality independent of human interpretation of its output in a clinical scenario. The document describes various "bench test data" and "non-clinical performance data" that demonstrate the device's capabilities. These include tests for resolution, brightness, white balance, color performance, latency, minimum illumination, auto-exposure response, and zero-degree parallax. These tests evaluate the inherent technical performance of the device itself.

7. The Type of Ground Truth Used

The ground truth for the non-clinical performance tests was the engineering design specifications and recognized international standards (e.g., IEC 60601 series for safety, ISO 10993 series for biocompatibility). The device was tested to verify that it met these predetermined functional and safety requirements.

8. The Sample Size for the Training Set

Not applicable. This device is an imaging system (hardware and associated software), not an AI/machine learning algorithm that requires a "training set" in the conventional sense. The "training" for such a device involves its design, manufacturing, and internal testing to ensure it meets its intended specifications.

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

Not applicable. As mentioned, this is not an AI/machine learning algorithm requiring a separate training set. The "ground truth" for the device's development (design and manufacturing of the hardware and embedded software) would effectively be established through engineering principles, established medical device development processes, and adherence to relevant industry standards and regulatory requirements.

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