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The KARL STORZ OR1™ SCB CONTROL provides central control of compatible medical equipment in the operating room during diagnostic and therapeutic procedures.

The KARL STORZ OR1™ SCB CONTROL is a central control unit which provides a single point of contact in the operating room for centralized control of various medical devices in sterile and non-sterile areas during diagnostic and therapeutic procedures. The system consists of KARL STORZ OR1™ SCB CONTROL software run on a dedicated KARL STORZ OR1™ SCB CONTROL hardware platform (Model Number: WU300) which connects to a touch-sensitive LCD monitor (Model Number: WM100 or WM101).

The user interface enables central display and control of multiple parameters for KARL STORZ and third-party devices connected via SCB serial cable or ethernet cable to the KARL STORZ OR1™ SCB CONTROL hardware CPU. The equipment controls can be operated via sterile input on the touchscreen monitor with the use of a sterile disposable cover. Outside the sterile area a keyboard or mouse can optionally be used.

The provided document is a 510(k) Summary for the KARL STORZ OR1™ SCB CONTROL. It is a control unit for medical equipment in an operating room and does not involve AI or diagnostic capability. Therefore, it does not contain the information required to answer the questions about acceptance criteria, study details, ground truth, or MRMC studies for an AI-powered device.

The document focuses on demonstrating substantial equivalence to a predicate device through non-clinical performance data (electrical safety, EMC, software lifecycle, V&V testing) and does not present acceptance criteria or detailed study results in the context of diagnostic accuracy or AI performance.

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

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The Smith+Nephew INTELLIO Tablet is indicated for use to provide wired or wireless remote control of Smith+Nephew compatible surgical and endoscopic devices within the operating room including camera control unit, patient information system, mechanical resection system, fluid management system and RF coblation system. These controls consist of adjusting parameter settings only.

The INTELLIO Tablet application software is pre-installed upon a medical grade tablet that is used with compatible Smith + Nephew surgical and endoscopic device allows for control of parameters within the operating room, including camera control system, patient information system, mechanical resection system, Fluid Management system and RF Coblation system. The device also connects to the MY.INTELLIO™ cloud and provides access to medical device status, settings, images/videos, and reports before, during and after surgical procedures. Additionally, software updates are manually downloaded from the MY.INTELLIO cloud account via secured link and can be installed via the administrator account. The device is able to set parameters remotely for the Fluid management pumps, Mechanical Resection controller and RF Coblation controller by connecting to the Wifi connect device either separate (LINK box) or integrated with the individual pump or controller. It also provides a secondary control mechanism for all of these devices forming a connected system. The primary control for setting parameters are on the individual devices.

The provided text does not contain any information about acceptance criteria or a study that proves the device meets specific performance criteria.

Instead, this document is a 510(k) premarket notification letter from the FDA for a device called the "Smith+Nephew INTELLIO Tablet." It focuses on demonstrating "substantial equivalence" to a predicate device (Smith+Nephew Tablet Application cleared in K203566), rather than presenting a performance study with detailed acceptance criteria.

The "Performance Data" section (H) merely lists the types of testing performed:

• Physical Verification
• Software Verification and Validation
• Emissions and Immunity Testing
• Electrical Safety Testing

It states that "Testing demonstrated that the Smith+Nephew INTELLIO Tablet has met the performance specifications," but it does not specify what those performance specifications (acceptance criteria) were or what the reported device performance was. It also doesn't describe any clinical study or any study involving human readers or ground truth.

Therefore, I cannot fulfill your request for the specific information regarding acceptance criteria, reported performance, sample sizes, data provenance, expert ground truth, adjudication methods, MRMC studies, standalone performance, or training set details, as this information is not present in the provided document.

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The Smith & Nephew Tablet Application is indicated for use to provide wireless control of Smith & Nephew compatible surgical and endoscopic devices within the operating room including camera/camera control unit, patient information system, mechanical resection system, fluid management system and RF coblation system. These controls consist of adjusting parameter settings only.

The Smith & Nephew Tablet Application is a software application that provides a Wi-Fi connection between compatible medical devices. Once connected the Tablet Application has the ability to provide limited remote control to the connected devices.

The provided document is a 510(k) premarket notification for the Smith & Nephew Tablet Application, which is a software application for wireless control of surgical and endoscopic devices. However, it does not contain specific acceptance criteria, performance data, or details about a study proving the device meets those criteria. The document focuses on demonstrating substantial equivalence to a predicate device (K192876) based on technological characteristics and intended use.

Therefore, I cannot provide the requested information from the given text.

Here's why and what information is missing:

1. Table of acceptance criteria and reported device performance: The document states that "Testing demonstrated that the Smith & Nephew Tablet Application has met the performance specifications" (Page 5, Section H). However, it does not list what those specific "performance specifications" (acceptance criteria) are, nor does it provide the reported device performance against those criteria (e.g., specific accuracy, latency, reliability metrics).
2. Sample size and data provenance: There is no mention of a test set, its sample size, or its provenance (e.g., country of origin, retrospective/prospective).
3. Number/qualifications of experts for ground truth: Since there's no mention of a clinical study or performance evaluation involving human interpretation of data where ground truth would be established by experts, this information is absent.
4. Adjudication method: Similarly, no adjudication method is described because no expert-based ground truth establishment is detailed.
5. Multi-reader multi-case (MRMC) comparative effectiveness study: The document does not describe any study involving human readers or AI assistance for human readers. The device's function is to control surgical parameters, not to assist in image interpretation or diagnosis.
6. Standalone performance: While the software itself performs wirelessly, the concept of "standalone performance" in the context of an AI device (i.e., algorithm only without human-in-the-loop performance) is not applicable here, as its function is control, not interpretation or diagnosis. No specific performance metrics like sensitivity, specificity, or AUC for a diagnostic task are provided.
7. Type of ground truth: No ground truth is described because the device's function is control, not diagnosis or interpretation that would require a ground truth for evaluation. The "performance specifications" it met would likely relate to functional requirements, responsiveness, and connectivity, not diagnostic accuracy.
8. Sample size for the training set: There is no mention of a training set as this is not an AI/ML diagnostic or predictive algorithm.
9. How ground truth for the training set was established: Not applicable, as there's no training set mentioned.

In summary, the provided document is a 510(k) summary for a device that controls surgical equipment wirelessly, not an AI/ML diagnostic or interpretative device. Therefore, the types of studies and performance metrics typically associated with AI/ML devices (like those requiring ground truth, expert readers, MRMC studies, etc.) are not present in this submission. The "performance data" mentioned (Page 5, Section H) refers to "Software validations" and "Cybersecurity testing," which are distinct from the type of performance data requested in the prompt for AI/ML devices.

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The Smith & Nephew Tablet Application is indicated for use to provide wireless control of parameters settings for compatible Smith & Nephew surgical and endoscopic devices within the operating room including cameralcamera control unit, patient information system, DYONICS POWER II Control Unit and the Werewolf Controller.

The Smith & Nephew Tablet Application is a software application that provides a Wi-Fi connection between compatible medical devices. Once connected the Tablet Application has the ability to provide limited remote control to the connected devices.

This document is a 510(k) summary for the INTELLIO Tablet Application, which primarily discusses substantial equivalence to a predicate device (K190367) based on its intended use and technological characteristics, particularly the addition of two new compatible medical devices. The document explicitly states that "Testing demonstrated that the Smith & Nephew Tablet Application has met the performance specifications and required Cybersecurity testing therefore, is substantially equivalent to the predicate device cleared in K190367." and lists "Software verification" and "Software validation" as conducted tests.

However, the provided text does not contain the specific details regarding acceptance criteria for performance, device performance metrics, sample sizes, data provenance, ground truth establishment, or multi-reader studies that you requested. It acts as a high-level summary confirming that testing was done and met specifications for substantial equivalence, but it does not elaborate on the specifics of those tests or their results.

Therefore, I cannot fulfill your request for the detailed table and study information based solely on this document. The document suggests that such performance specifications and verification/validation results exist but does not provide them within this 510(k) summary.

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The IDSS-ForceTriad Control Module is designed for integration in the IDSS(Integrated Digital Surgical Suite) and enables the Covidien ForceTriad Energy Platform to be controlled remotely.

The IDSS ForceTriad Control Module is an additional function to the IDSS, which is an integrated operating room system controlling video displays, observation cameras, audio video equipment, teleconferencing and the routing of video and images from multiple sources to multiple destinations via a touch screen interface. With the IDSS ForceTriad Control Module, operation room staff is able to control the ForceTriad setup from the touch panel location rather than using the ForceTriad unit itself.

The provided FDA 510(k) summary (K153205) describes the IDSS-ForceTriad Control Module. This device is an accessory that enables remote control of the Covidien ForceTriad Energy Platform within an Integrated Digital Surgical Suite (IDSS). The primary purpose of this submission is to demonstrate substantial equivalence to a predicate device, not to showcase the performance of an AI/ML algorithm that interprets medical images or data.

Therefore, many of the requested criteria regarding AI/ML performance studies, such as effect size of human readers with AI assistance, standalone algorithm performance, number of experts for ground truth, and training set information, are not applicable to this document.

Here's an analysis of the provided information, focusing on the context of a medical device accessory and its validation:

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

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. This device is a control module, not a diagnostic or AI image analysis system that would typically use a "test set" of medical images or patient data. The testing is focused on software functionality and risk management.

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 noted above, this is a control module, not an AI/ML system requiring expert-adjudicated ground truth for medical data.

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

Not applicable. This is not an AI/ML system requiring adjudication of medical cases.

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

Not applicable. This filing concerns a control module for an energy platform, not an AI-assisted diagnostic or imaging device. There is no mention of human readers or AI assistance in the context of clinical interpretation.

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

Not applicable. This is not an AI algorithm. Its function is to remotely control an existing medical device.

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

For the software validation and risk analysis, the "ground truth" would be related to the correct and expected functionality of the software according to its design specifications, user requirements, and safety standards (e.g., ISO 14971:2007). This is established through internal design documents, functional specifications, and compliance with regulatory standards, rather than clinical outcomes or expert consensus on medical findings.

8. The sample size for the training set

Not applicable. This is not an AI device that involves a "training set" in the machine learning sense.

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

Not applicable. This is not an AI device.

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Core.nova (core.browser, core.connect with driver, IR Transceiver, core.media, and core.portal) are components used for the central control of integrated medical devices and accessories in one central location within the operating room. In particular core.nova is used for the control of connected compatible OR equipment, like OR-lamp, OR-camera, ORtable, insufflator, or video- and documentation system intended for diagnostic and therapeutic medical conditions. The connected devices (Richard Wolf or third party) can be control terminals (with touch screen), or the control mechanism on actual device.

The core nova (complete operating room endoscopy nova) by Richard Wolf is a networkbased integrated operating room accessory that provides central control to various Richard Wolf devices and third party devices. This control allows the user to control device settings from the sterile area. Richard Wolf's core nova is comprised of core.browser, core.connect with driver, IR Transceiver, an optional core.media, and an optional server core.portal.

The provided document is a 510(k) premarket notification for the "core nova Complete Operating Room Endoscopy" system by Richard Wolf. This document focuses on demonstrating substantial equivalence to a predicate device rather than presenting a study with specific acceptance criteria and detailed device performance metrics in the way one might expect for a diagnostic or therapeutic algorithm.

Therefore, the requested information, particularly regarding acceptance criteria, sample sizes, expert ground truth, adjudication methods, MRMC studies, and standalone performance related to diagnostic or therapeutic effectiveness, is not present in this document. The device in question, an endoscopic central control unit, is a system for managing and controlling other medical devices in an operating room, rather than a device that directly yields a diagnostic output or performs a direct therapeutic action that would typically require such performance studies.

However, I can extract information related to the device's validation based on the document's content:

1. Table of Acceptance Criteria and Reported Device Performance

As noted, the document doesn't provide acceptance criteria and performance in the typical sense for a diagnostic device (e.g., sensitivity, specificity). The "performance" described here relates to compliance with regulatory standards for medical electrical equipment and software.

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

• Sample size: Not applicable/Not provided. The validation described is compliance with electrical and software standards, not performance on a dataset of patient data.
• Data provenance: Not applicable.

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

• Not applicable. The "ground truth" here is compliance with established engineering and software safety standards, assessed through testing, not expert interpretation of medical data.

4. Adjudication method for the test set:

• Not applicable.

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 MRMC study was done. This device is not an AI-assisted diagnostic tool.

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

• Not applicable. This device is an operating room control system, not a standalone diagnostic or therapeutic algorithm.

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

• The "ground truth" in this context is the adherence of the device to established international and national electrical safety and electromagnetic compatibility (EMC) standards and compliance with FDA software validation guidelines. This is verified through engineering tests and software verification documentation, not medical ground truth like pathology.

8. The sample size for the training set:

• Not applicable. This device is not a machine learning or AI algorithm that requires a training set in the conventional sense.

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

• Not applicable.

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The KARL STORZ SCB/Covidien ForceTriad Interface Module is designed for integration in the SCB Interface Control, SCB Media Control or ACC Control and enables the COVIDIEN ForceTriad Energy Platform to be controlled remotely with the KARL STORZ SCB control NEO system.

The Karl Storz SCB/Covidien ForceTriad Interface Module is an accessory to integrate the third party device Covidien ForceTriad Energy Platform (K102913) into the SCB network and allow remote settings adjustment.

The provided text describes a 510(k) summary for the Karl Storz SCB/Covidien ForceTriad Interface Module. This device is an accessory designed to integrate the Covidien ForceTriad Energy Platform into the Karl Storz SCB network, allowing for remote settings adjustment.

The submission is for a device that seems to be a hardware accessory with software that facilitates communication and control between two existing medical devices. The primary purpose of the supporting "study" (software testing) is to demonstrate that this integration functions as intended and is equivalent to a previously cleared predicate device.

Here's an analysis of the provided information based on your requested criteria:

1. Table of Acceptance Criteria and Reported Device Performance:

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

• Sample Size: Not explicitly stated. The document mentions "software verification and validation testing," but does not specify the number of test cases, test runs, or the amount of data used in these tests.
• Data Provenance: Not explicitly stated. It's internal company testing ("software verification and validation testing"). It's highly likely to be a prospective test conducted by the manufacturer, but no country of origin for the data is mentioned.

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

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

Given the nature of the device (an interface module for control), the "ground truth" would likely be defined by specific functional requirements and expected outputs, which would be established by internal engineering and quality assurance teams, rather than medical experts in a clinical context.

4. Adjudication method for the test set:

• Adjudication Method: Not mentioned. For software verification and validation, adjudication typically involves comparing test results against predefined expected outcomes or specifications.

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 type of study is typically performed for diagnostic or AI-assisted devices where human interpretation of medical images or data is involved. The device described here is an interface module for controlling surgical equipment, not an AI or diagnostic tool.

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

• Standalone Performance: Not directly applicable in the terms of an "algorithm only" performance study as commonly understood for AI/diagnostic devices. The software component of this device is operating "standalone" in the sense that it carries out its programmed functions (interfacing and remote control) without continuous human intervention during its operation. However, the performance is in the context of its function as an interface, not as a decision-making algorithm. The "software verification and validation testing" did evaluate the device's functional integrity.

7. The type of ground truth used:

• Type of Ground Truth: The ground truth for this device would be based on functional specifications and expected operational behavior. Essentially, the device functions correctly if:
  • It successfully integrates the Covidien ForceTriad Energy Platform into the SCB network.
  • It allows for accurate remote adjustment of settings of the ForceTriad Energy Platform via the SCB-RUI.
  • It performs these actions reliably and without errors that could compromise safety or effectiveness.
    This is established through engineering design, requirements specifications, and direct testing against those specifications.

8. The sample size for the training set:

• Training Set Sample Size: Not applicable. This device is an interface module, not a machine learning or AI algorithm that requires a "training set" in the conventional sense. Its functionality is hard-coded or configured based on engineering design.

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 type of device. The "ground truth" for its design and functionality is established through engineering principles, regulatory requirements, and the specifications of the devices it is designed to interface with.

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SYSTEMS INTEGRATION ENDOALPHA (MEDICAL CONTROL UNIT FOR ENDOSURGERY UCES-3) Software Version 2
The Systems Integration ENDOALPHA has been designed to be used with an Olympus endoscope and ancillary equipment for central operation, central display, automatic initial setting, and interlocking operation of the ancillary equipment.
UPGRADE KIT FOR UCES-3 (Version 2) MAJ-1943
This equipment has been designed to install software on the ENDOALPHA UCES-3, MAJ-1831, MAJ-1832, MAJ-1834, and MAJ-1862.

The Systems Integration ENDOALPHA has been designed to be used with an Olymous endoscope and ancillary equipment for central operation, central display, automatic initial setting, and interlocking operation of the ancillary equipment.
The ENDOALPHA integrated electrosurgical system has the following system functions.

1. Centralized control from a non-sterilized area
2. Centralized control from a sterilized area
3. Centralized display
4. Message display
5. Voice reply
6. Automatic settings
7. Scene settings
8. Display customization
9. Still image viewing
10. Automatic smoke evacuation

The Olympus Medical Systems Corp. 510(k) summary for the SYSTEMS INTEGRATION ENDOALPHA (MEDICAL CONTROL UNIT FOR ENDOSURGERY UCES-3) Software Version 2 describes design verification and software validation activities rather than a clinical study with human subjects. The device is a system for central operation, display, and control of ancillary endosurgery equipment, performing functions like centralized control, display customization, and automatic settings, with software modifications including additional compatible devices, improved interfacing, increased surgical light capacity, reduced startup time, and an additional language setting.

Here's the breakdown of the acceptance criteria and study information:

1. Table of Acceptance Criteria and Reported Device Performance

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

The document describes software validation and design verification tests, not a clinical study involving human patients or a traditional "test set" of patient data. Therefore, information regarding human sample size or data provenance (country of origin, retrospective/prospective) is not applicable or provided. The "test set" in this context refers to the system itself, its components, and its software functions.

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

Not applicable. The "ground truth" for this type of device (an integration system for endosurgery equipment) is based on engineering specifications, functional requirements, and safety standards rather than expert clinical consensus on patient data.

4. Adjudication Method for the Test Set

Not applicable, as this was not a study involving human readers or interpretation of medical images. The acceptance of testing results for design verification and software validation would have been based on engineering and quality assurance protocols.

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

No, an MRMC comparative effectiveness study was not done. The device is a control system for endosurgery equipment, not a diagnostic or AI-assisted interpretation tool that would involve human readers or image analysis.

6. Standalone Performance Study

Yes, the testing described appears to be for the standalone system in a simulated environment. The "Operation/verification testing" and "Performance testing" for various aspects like interfacing, new equipment compatibility, surgical light capacity, startup time, and language settings, along with "Unit level testing," "Integration level testing," "System level testing," and "Simulated use testing" for software, all represent standalone performance evaluation of the device's functionality. This is "algorithm only without human-in-the-loop performance" in the sense that the system's functions are verified independently of live clinical use.

7. Type of Ground Truth Used

The ground truth used for this type of device is primarily based on:

• Engineering Specifications: Adherence to defined parameters for electrical, mechanical, and communication characteristics, as well as operational principles.
• Functional Requirements: Verification that the system performs its designed functions (e.g., centralized control, display, automatic settings, smoke evacuation) as intended.
• Safety Standards: Compliance with relevant medical device safety standards like ISO 14971:2007 for risk management.
• Software Design Documents: Verification against software requirements specifications and design documents.

8. Sample Size for the Training Set

Not applicable. This device is a control system, not an AI/ML algorithm that requires a "training set" of data in the typical sense (e.g., for image recognition or clinical prediction). The software developed for this system follows conventional software engineering principles rather than machine learning.

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 Systems Integration ENDOALPHA has been designed to be used with an Olympus endoscope and ancillary equipment for central operation, central display, automatic initial setting, and interlocking operation of the ancillary equipment.

The Systems Integration ENDOALPHA has been designed to be used with an Olymous endoscope and ancillary equipment for central operation, central display, automatic initial setting, and interlocking operation of the ancillary equipment.

The ENDOALPHA integrated electrosurgical system has the following system functions.

1. Centralized control from a non-sterilized area
   2} Centralized control from a sterilized area
2. Centralized display
3. Message display
4. Voice reply .
5. Automatic settings
6. Scene settings
7. Display customization
8. Still image viewing
9. Automatic smoke evacuation

The modifications made to the subject device are as follows:
A) Addition or deletion of recommended ancillary equipment
B) A change involving the communication interface
C) A change in the design of the Graphical User interface (GUI)
D) Addition of a voice reply function
E) Deletion of Surgeon's controller for EndoALPHA (MAJ-1140)
F) Transfer of Input/Output function for video signals to the Advanced system
G) Addition of additional connectable touch panel
H) Addition of Scene settings function
l) Addition of system components
Changes to the specifications of system components S

This document is a 510(k) summary for the Olympus Systems Integration ENDOALPHA, a system designed to integrate and control various ancillary equipment used with Olympus endoscopes. The summary focuses on establishing substantial equivalence to a predicate device, rather than providing a detailed study proving the device meets specific acceptance criteria in terms of performance metrics like accuracy, sensitivity, or specificity.

Therefore, many of the requested details about acceptance criteria, study design, and performance metrics (like sample size, ground truth, expert qualifications, MRMC study, standalone performance, and training set details) are not available in the provided text because the 510(k) premarket notification process for this type of device (an integration system, not a diagnostic or AI-driven tool) does not typically require such detailed performance studies in the same way as, for example, an AI diagnostic algorithm. The primary goal here is to demonstrate that the new device is as safe and effective as a legally marketed predicate device.

However, I can extract the available information and explain why other details are absent.

1. Table of Acceptance Criteria and Reported Device Performance

Explanation for Missing "Acceptance Criteria" Details:
For a device like the ENDOALPHA, which is an integration and control system, "acceptance criteria" for demonstrating substantial equivalence are typically evaluated against the predicate device's functionality and safety profile, rather than quantitative performance metrics like accuracy or sensitivity. The FDA's review for a 510(k) largely focuses on whether the new device is as safe and effective as the predicate. The "performance" in this context refers to its functional capabilities and how they compare to the predicate, and whether any changes introduce new risks or questions of effectiveness.

2. Sample Size Used for the Test Set and Data Provenance
Not applicable/Not provided in the document.
This document does not describe a clinical performance study using a test set of data (e.g., images, patient records). The evaluation for this device appears to be based on engineering verification and validation, functional testing, and comparison to the predicate device's specifications and intended use.

3. Number of Experts Used to Establish the Ground Truth for the Test Set and Their Qualifications
Not applicable/Not provided in the document.
Since there's no clinical performance study involving a "test set" with "ground truth" (e.g., diagnosing conditions), expert review for establishing ground truth is not part of this 510(k) summary for the ENDOALPHA system.

4. Adjudication Method for the Test Set
Not applicable/Not provided in the document.
No clinical test set requiring adjudication is described.

5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study
No.
This document does not mention or report the results of an MRMC study. The device is a system integration tool, not a diagnostic aid that would typically involve human readers interpreting output to improve diagnostic performance.

6. Standalone Performance Study
Not applicable/Not provided in the document in the context of diagnostic performance.
While the system itself has standalone functional performance (e.g., its ability to control equipment, display messages), the concept of "standalone performance" as it relates to AI algorithms or diagnostic tools (i.e., algorithm only without human-in-the-loop performance) is not relevant or evaluated for this type of system in this document. The document describes its functional capabilities in isolation and in integration.

7. Type of Ground Truth Used
Not applicable/Not provided in the document.
There is no mention of "ground truth" (e.g., pathology, outcomes data) as this pertains to diagnostic accuracy studies. For this system, the "truth" would refer to whether the system correctly performs its programmed functions (e.g., does it centralize control as intended, do automatic settings work). This is validated through engineering and functional testing rather than clinical "ground truth" data.

8. Sample Size for the Training Set
Not applicable/Not provided in the document.
This device is an integration and control system, not an AI/machine learning algorithm that requires a training set of data.

9. How the Ground Truth for the Training Set Was Established
Not applicable/Not provided in the document.
As there is no training set mentioned, this information is not relevant.

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The Karl Storz VOICE1® SCB R-UI Speech Control Application is indicated for use in speech control of Karl Storz-approved devices and peripherals in conjunction with the Karl Storz SCB® R-UI software.

The Karl Storz VOICE1® SCB R-UI Speech Control Application is an independent application that works in the background of the SCB R-UI. VOICE1® recognizes commands spoken by the user and sends corresponding commands to the SCB R-UI system, enabling the user to control endoscopic and other ancillary surgical equipment and adjust device parameters entirely by speech control.

The provided documents for Karl Storz VOICE1® SCB R-UI Speech Control Application do not contain any information regarding acceptance criteria, device performance results, or details of a study that would prove the device meets specific criteria.

The documents primarily consist of:

• A 510(k) Summary (K083598, page 1) which describes the device, its intended use, technological characteristics, and claims substantial equivalence to a predicate device.
• A Substantial Equivalence Table (page 2) which compares the new device to the predicate device in terms of device type, user input, controlled devices, added functions, other software requirements, and intended use.
• An FDA letter (pages 3-5) confirming the substantial equivalence determination and outlining regulatory responsibilities.
• An Indications for Use statement (page 6).

Therefore, I cannot provide the requested table or answer questions 2 through 9, as no such study details are presented in the provided text.

Based on the information available:

1. Table of acceptance criteria and the reported device performance:
* No acceptance criteria or reported device performance metrics are mentioned in the provided text. The submission focuses on substantial equivalence to a predicate device, rather than presenting a performance study against predefined criteria.

2. Sample sized used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective):
* No information provided.

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):
* No information provided.

4. Adjudication method (e.g. 2+1, 3+1, none) for the test set:
* No information provided.

5. If a multi reader multi case (MRMC) comparative effectiveness study was done, If so, what was the effect size of how much human readers improve with AI vs without AI assistance:
* No information provided.

6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done:
* No information provided. The device is a speech control application, inherently designed for human-in-the-loop interaction.

7. The type of ground truth used (expert concensus, pathology, outcomes data, etc):
* No information provided.

8. The sample size for the training set:
* No information provided.

9. How the ground truth for the training set was established:
* No information provided.

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