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The Apache Ultrasound System is intended for diagnostic ultrasound imaging and fluid flow analysis in the following applications: Abdominal, Fetal/Obstetric, Gynecological, Fetal Echo, Musculo-skeletal (conventional and superficial), Small Organ (including breast, scrotum, thyroid), Cephalic (adult), Peripheral Vessel, Carotid, Urology, FAST/EFAST, Lung, Ocular, Nerve, Cardiac (Pediatric and Adult) and Pediatric. The system provides diaging in B, Color Doppler, M, Power Doppler and Combined (B+M; B+CD ; B+PD) modes. The clinical environments where the system can be used include physician offices, clinics, hospitals, and clinical point of care for diagnosis of patients except environments where intensity of electromagnetic disturbances is high. The Apache Ultrasound System is a portable ultrasound system intended for use in environments where is provided by trained healthcare professionals.

The Aco Apache Ultrasound System is a portable color ultrasound imaging system, capable of producing high detail resolution intended for clinical diagnostic imaging applications through wireless communication with an off-the-shelf (OTS) mobile device. This system is a general purpose, software controlled, diagnostic ultrasound system. Its basic function is to acquire ultrasound data and display the image in B-Mode, M-Mode, Color Doppler mode, Power Doppler mode, PW mode or a combination of these modes.

The Apache Probe processes the ultrasound signal and transfers real-time scan image data to the Apache App through the wireless connection with the mobile device. The 64-channel beamforming architecture of the probe maximizes the utility of all imaging transducer elements to enhance the diagnostic utility and confidence provided by the system. The Apache App provides the interface for mode/setting control and image display, acquisition, and storage functions. The Apache App is compatible with Android and iOS based mobile devices. Verified devices include Samsung Galaxy Tab S6, Samsung Galaxy Tab S6 Lite, Samsung Galaxy Tab S7, Samsung Galaxy Tab S7 FE, Samsung Galaxy Tab S8, Samsung Galaxy S10, Samsung Galaxy S21, Xiaomi Pad 5, Google Pixel 4, Google Pixel 6, HTC U11, LG G85 ThinQ, iPhone 11, iPhone 12, iPhone 14, iPad Pro 11-inch (Gen2), iPad Air (Gen4), iPad Air (Gen5), iPad 9.

The clinical environments where the system can be used include physician offices, clinics, hospitals, and clinical point-of-care for diagnosis of patients except environments where intensity of electromagnetic disturbances is high.

This document describes the FDA clearance for the Aco Apache Ultrasound System, a portable color ultrasound imaging system. The submission focuses on demonstrating substantial equivalence to a predicate device, the Clarius Ultrasound Scanner (K213436).

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

Unfortunately, the provided FDA 510(k) summary does not contain details about specific acceptance criteria, reported device performance metrics, or a formal study that proves the device meets those criteria, especially in a comparative effectiveness context. The document primarily focuses on non-clinical testing and a claim of substantial equivalence to a predicate device based on similar intended use and technology.

Let's break down what information is available and what is missing based on your requested categories:

1. Table of Acceptance Criteria and Reported Device Performance

Detailed breakdown of requested information:

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

• No information provided. The document explicitly states: "Aco Apache Ultrasound System introduces no new indications for use, modes, features, or technologies relative to the predicate devices (Clarius Ultrasound Scanner) that require clinical testing." This indicates that no separate clinical test set or study data, with specific sample sizes or provenance, was generated or presented for this 510(k) submission to demonstrate clinical performance.

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. As no clinical test set was presented or required, there was no need for experts to establish ground truth for a clinical study.

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

• No information provided. No clinical test set or study requiring adjudication was conducted or described.

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 comparative effectiveness study was done or reported. The device is a general-purpose diagnostic ultrasound system, not an AI-assisted diagnostic tool in the sense of providing automated readings or interpretations that would typically warrant an MRMC study. The comparison is primarily against a predicate device, not against human reader performance or human reader improvement with AI assistance.

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

• Not applicable / No such study reported. The Aco Apache Ultrasound System is a diagnostic imaging device controlled by a human operator, not a standalone AI algorithm performing diagnoses. While it is software-controlled, its core function is to produce images for human interpretation.

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

• No ground truth data from a clinical study is discussed. The basis for clearance is substantial equivalence to a predicate device, supported by non-clinical verification against established engineering and safety standards.

8. The sample size for the training set

• Not applicable / No information provided. The document does not describe the use of machine learning or AI models that would require a "training set" in the context of diagnostic interpretation. It is a general imaging system. While the device is software-controlled, the software development would adhere to IEC 62304 (Medical Device Software - Software Life Cycle Processes), which focuses on software quality and safety, not on training data for a diagnostic algorithm.

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

• Not applicable / No information provided. As no training set for a diagnostic algorithm is indicated, the method of establishing its ground truth is not relevant or provided.

Summary of Device Verification Methods Presented:

The 510(k) submission for the Aco Apache Ultrasound System primarily relies on two pillars for demonstrating substantial equivalence:

1. Comparison to a legally marketed predicate device (Clarius Ultrasound Scanner K213436): The document highlights that the subject device has the same intended use, indications for use (with some minor variations in specific applications that are still covered by the predicate), product codes, regulation numbers, portability, power source, and fundamental imaging modes (B, M, Color Doppler, Power Doppler, PW Doppler, and combined modes) as the predicate.
2. Non-clinical performance testing and compliance with recognized standards: The document lists numerous international and national standards (e.g., electrical safety, EMC, usability, biological compatibility, acoustic output, software lifecycle, risk management) that the device complies with. These tests ensure the device's technical specifications, safety, and functionality meet established industry benchmarks.

The basis for clearance is that the new device does not introduce new technological characteristics or indications for use that would raise new questions of safety or effectiveness compared to the predicate device, thereby negating the need for de novo clinical studies for this 510(k) submission.

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The Apache Ultrasound System is intended for diagnostic ultrasound imaging and fluid flow analysis in the following applications: Abdominal, Fetal/Obstetric, Gynecological, Fetal Echo, Musculo-skeletal (conventional and superficial), Small Organ (including breast, scrotum, thyroid), Peripheral Vessel, Carotid, Urology, and Pediatric. The system provides diagnostic ultrasound imaging in B, Color Doppler, M, PW and Combined (B+M; B+CD) modes. The clinical environments where the system can be used include physician offices, clinics, and clinical point-of-care for diagnosis of patients except environments where intensity of electromagnetic disturbances is high. The Apache Ultrasound System is a portable ultrasound system intended for use in environments where is provided by trained healthcare professionals.

The Apache Ultrasound System is a portable color ultrasound imaging system, capable of producing high detail resolution intended for clinical diagnostic imaging applications through wireless communication with an off-the-shelf (OTS) mobile device. This system is a general purpose, software controlled, diagnostic ultrasound system. Its basic function is to acquire ultrasound data and display the image in B-Mode, M-Mode, PW mode and Color Doppler or a combination of these modes.

The Apache Probe processes the ultrasound signal and transfers real-time scan image data to the Apache App through the wireless connection with the mobile device. The 64-channel beamforming architecture of the probe maximizes the utility of all imaging transducer elements to enhance the diagnostic utility and confidence provided by the system. The Apache App provides the interface for mode/setting control and image display, acquisition, and storage functions. The Apache App is compatible with Android and iOS based mobile devices. Verified devices include Samsung Galaxy Tab S6, Samsung Galaxy Tab S7, Samsung Galaxy S10, Samsung Galaxy S21, Google Pixel 4, HTC U11, LG G85 ThinQ, iPhone 11, iPhone 12, iPad Pro 11-inch (Gen2), and iPad Air (Gen4).

The clinical environments where the system can be used include physician offices, clinics, hospitals, and clinical point-of-care for diagnosis of patients except environments where intensity of electromagnetic disturbances is high.

The Apache Ultrasound System is a portable ultrasound system intended for use in environments where healthcare is provided by trained healthcare professionals.

This FDA 510(k) summary for the Apache Ultrasound System does not contain the detailed information necessary to fully answer your request regarding acceptance criteria and a study proving the device meets those criteria.

The document states that the Apache Ultrasound System is a "Track 3 system" and that its substantial equivalence is primarily determined by comparison to a predicate device (Clarius Ultrasound Scanner K192107), specifically focusing on non-clinical performance tests rather than clinical efficacy studies in the way you've outlined.

Here's a breakdown of what the document does provide, and what it lacks in relation to your specific questions:

Key Takeaways from the Document:

• Substantial Equivalence: The primary method for proving the device is acceptable is by demonstrating "substantial equivalence" to a legally marketed predicate device (Clarius Ultrasound Scanner K192107). This is a common pathway for medical devices where new clinical efficacy studies are not deemed necessary because the fundamental technology and intended use are similar to existing, cleared devices.
• Focus on Non-Clinical Tests: The document explicitly states: "Non-clinical performance tests include measurement accuracy, system sensitivity, thermal, mechanical, electrical safety, patient-contact materials, cleaning and disinfection, software and acoustic output. The non-clinical test results show the device is compliant with the following standards, and it is safe and effective for its intended use and performance."
• No New Indications/Technologies Requiring Clinical Testing: "Apache Ultrasound System introduces no new indications for use, modes, features, or technologies relative to the predicate devices (Clarius Scanner) that require clinical testing. The clinical safety and effectiveness of ultrasound systems with these characteristics are well accepted for both predicate and subject devices."

Addressing Your Specific Questions Based on the Provided Text:

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

   • Acceptance Criteria: The document lists standards (e.g., AAMI/ANSI ES60601-1, IEC 60601-1-2, ISO 10993-1, IEC 62304, ISO 14971) that the device must comply with for non-clinical performance (electrical safety, EMC, usability, biological evaluation, software lifecycle, risk management). These standards implicitly define acceptance criteria in terms of meeting their requirements.
   • Reported Device Performance: The document states, "The non-clinical test results show the device is compliant with the following standards, and it is safe and effective for its intended use and performance." However, it does not provide a detailed table with specific numerical performance metrics (e.g., measurement accuracy values, sensitivity values) or thresholds for these non-clinical tests. It merely states compliance.

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

   • Not Applicable/Not Provided: Since the substantial equivalence is primarily based on non-clinical performance and a comparison to a predicate device without new clinical indications, there is no mention of a clinical test set sample size or data provenance (e.g., country of origin, retrospective/prospective clinical data). The testing described is laboratory-based non-clinical testing.

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

   • Not Applicable/Not Provided: This information pertains to studies involving human interpretation or ground truth establishment for diagnostic accuracy, which was explicitly not performed as a clinical study for this 510(k) submission.

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

   • Not Applicable/Not Provided: Same as above, not relevant for the type of submission described.

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 states, "Apache Ultrasound System introduces no new indications for use, modes, features, or technologies relative to the predicate devices (Clarius Scanner) that require clinical testing." This device is an ultrasound system itself, not an AI assisting human readers, and no MRMC study was performed as part of this 510(k) submission.

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

   • No: This device is a diagnostic ultrasound system, not an AI algorithm intended for standalone performance evaluation in the usual sense of algorithm-only accuracy. Its "performance" is in generating images and fluid flow analysis, and its safety and effectiveness are established through engineering and non-clinical tests to meet standards.

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

   • Not Applicable/Not Provided: For the non-clinical tests, "ground truth" would be defined by calibrated instruments, reference materials, and engineering specifications. It's not clinical "ground truth" (e.g., pathology, expert diagnosis).

8. The sample size for the training set:

   • Not Applicable/Not Provided: This refers to the training of an AI model. While the device (like any modern medical device) contains software, the 510(k) summary does not describe it as an AI/ML-driven device that underwent a training phase on a specific dataset for diagnostic interpretation. Its software lifecycle processes are governed by IEC 62304, which is about software engineering, not AI model training.

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

   • Not Applicable/Not Provided: Same as above.

In summary: The provided document is a 510(k) summary from the FDA, which outlines the basis for demonstrating "substantial equivalence" for a medical device. For the Apache Ultrasound System, this was primarily achieved through comparison to a predicate device and rigorous non-clinical testing against established safety and performance standards. It explicitly states that no new clinical efficacy testing was required because the device did not introduce new indications, modes, features, or technologies. Therefore, the detailed information about clinical studies, expert-derived ground truth, sample sizes for test/training sets, and AI-related performance metrics (like MRMC studies) you requested are not present in this type of submission for this particular device.

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The Apache Ultrasound System (Model: C62) is intended for diagnostic ultrasound imaging and fluid flow analysis in the following applications: Abdominal, Fetal/Obstetric, Gynecological, Fetal Echo, Musculo-skeletal (conventional), Urology, and Pediatric. The system provides diagnostic ultrasound imaging in B, Color Doppler, and M modes. The clinical environments where the system can be used include physician offices, clinics, and clinical point-of-care for diagnosis of patients except environments where intensity of electromagnetic disturbances is high. The Apache Ultrasound System is a portable ultrasound system intended for use in environments where healthcare is provided by trained healthcare professionals.

The Apache Ultrasound System is a portable color ultrasound imaging system, capable of producing high detail resolution intended for clinical diagnostic imaging applications through wireless communication with an off-the-shelf (OTS) mobile device.

This system is a general purpose, software controlled, diagnostic ultrasound system. Its basic function is to acquire ultrasound data and display the image in B-Mode, M-Mode and Color Doppler or a combination of these modes. The Apache Processes the ultrasound signal and transfers real-time scan image data to the Apache App through the wireless connection with the mobile device. The 64-channel beamforming architecture of the probe maximizes the utility of all imaging transducer elements to enhance the diagnostic utility and confidence provided by the system. The Apache App provides the user interface for mode/setting control and image display, acquisition and storage functions. The Apache App is compatible with Android based mobile devices. Verified devices include Samsung Galaxy S10, Google Pixel 4, HTC U11, and LG G85 ThinQ.

The clinical environments where the system can be used include physician offices, clinics, hospitals, and clinical point-of-care for diagnosis of patients except environments where intensity of electromagnetic disturbances is high.

The Apache Ultrasound System is a portable ultrasound system intended for use in environments where healthcare is provided by trained healthcare professionals.

The provided text describes a 510(k) premarket notification for the "Apache Ultrasound System (Model C62)". This document focuses on demonstrating substantial equivalence to a predicate device, rather than proving that the device meets specific performance acceptance criteria through the kind of studies typically performed for AI/ML-based diagnostic devices (e.g., studies evaluating sensitivity, specificity, or reader performance).

Therefore, many of the requested details regarding acceptance criteria for diagnostic performance and specific study methodologies (like sample sizes for test/training sets, expert adjudication, MRMC studies, standalone performance, and ground truth establishment) are not present in this type of FDA submission.

Instead, this submission primarily relies on:

1. Comparison to a Predicate Device: Demonstrating that the new device has the same intended use, indications for use, and similar technological characteristics as a legally marketed device.
2. Compliance with Non-Clinical Standards: Showing that the device meets established safety and performance standards for ultrasound equipment (e.g., electrical safety, electromagnetic compatibility, acoustic output, usability, biocompatibility, software lifecycle, risk management).

Here's an attempt to answer your questions based only on the provided text, highlighting where information is absent or not applicable to this type of submission:

Acceptance Criteria and Device Performance (as per 510(k) for substantial equivalence)

Since this is a substantial equivalence submission for an ultrasound imaging system, the "acceptance criteria" are primarily related to safety, fundamental performance characteristics, and similarity to the predicate device, rather than diagnostic accuracy metrics of an AI/ML algorithm.

1. Table of Acceptance Criteria and Reported Device Performance

• AAMI/ANSI ES60601-1 (Medical Electrical Equipment - General Requirements for Basic Safety and Essential Performance)
• IEC 60601-1-2 (Electromagnetic Compatibility)
• IEC 60601-1-6 (Usability)
• IEC 60601-2-37 (Particular requirements for ultrasonic medical diagnostic and monitoring equipment)
• IEC 62133 (Secondary Cells and Batteries Safety)
• IEC 62366 (Application of Usability Engineering)
• ISO 10993-1, -5, -10 (Biological Evaluation - Cytotoxicity, Irritation, Skin Sensitization)
• IEC 62304 (Medical Device Software Life Cycle Processes)
• ISO 14971 (Risk Management)
• NEMA UD 2 (Acoustic Output Measurement Standard)
  Performance: The device has undergone non-clinical testing demonstrating compliance with these safety and performance standards. |

Regarding Specific Diagnostic Performance Studies (AI/ML context):

The provided 510(k) summary is for a general-purpose diagnostic ultrasound system, not an AI/ML diagnostic algorithm. Therefore, many of your questions about diagnostic performance studies (sample size, experts, ground truth, MRMC, standalone performance, training sets) are not applicable to this document because such studies are not typically required for a 510(k) submission for a conventional ultrasound device, especially when claiming substantial equivalence and introducing "no new indications for use, modes, features, or technologies relative to the predicate devices (Clarius Scanner) that require clinical testing."

Here's an explicit breakdown of why these questions cannot be answered from the text:

2. Sample size used for the test set and the data provenance: Not applicable. No diagnostic performance test set described.
3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts (e.g. radiologist with 10 years of experience): Not applicable. No diagnostic performance test set described.
4. Adjudication method (e.g. 2+1, 3+1, none) for the test set: Not applicable. No diagnostic performance test set described.
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 is a conventional ultrasound system, not an AI-assisted diagnostic device described in this submission.
6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done: Not applicable. This is a conventional ultrasound system, not an AI-enabled algorithm with standalone performance metrics.
7. The type of ground truth used (expert consensus, pathology, outcomes data, etc.): Not applicable. No diagnostic performance study for which ground truth would be established is mentioned.
8. The sample size for the training set: Not applicable. There is no mention of an AI/ML model that would require a training set.
9. How the ground truth for the training set was established: Not applicable. There is no mention of an AI/ML model that would require a training set.

Conclusion from the text:

The submission concludes: "The Apache Ultrasound System is substantially equivalent to the predicate device. The Apache Ultrasound System function in a manner similar to and are intended for the same use as the predicate device. Based on the predicate device comparison of indications for use, labeling, acoustic output and general safety and effectiveness information, as well as the non-clinical performance test results, it is concluded that this device is as safe and effective as the predicate device for its intended use and performance, and is substantially equivalent to the predicate device."

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