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The device is intended for use by a qualified physician for ultrasound evaluation of Small Parts, Abdominal, Cardiac, Peripheral Vascular, Fetal, Intra-operative, Trans-vaginal , Trans-rectal, Gynecological, Musculo-sketal and Neonatal Cephalic applications. The device is not indicated for Ophthalmic applications.

The Prosound F75 Diagnostic Ultrasound System is a full feature imaging and analysis system. It consist of a mobile console that provides acquisition, processing and display capability. The user interface includes a computer type keyboard, specialized controls and a display.

The provided document is a 510(k) summary for the Aloka Prosound F75 Diagnostic Ultrasound System. This document focuses on demonstrating substantial equivalence to a predicate device, rather than proving performance against specific acceptance criteria through a clinical study for a novel device. Therefore, many of the requested categories for acceptance criteria and clinical study details are not applicable in this context.

Here's a breakdown based on the information provided:

1. Table of Acceptance Criteria and Reported Device Performance

For a traditional medical device like an ultrasound system undergoing 510(k) clearance, the "acceptance criteria" are generally established through engineering and performance testing against recognized standards to demonstrate that the device performs as intended and is safe and effective. The "reported device performance" is then the outcome of these non-clinical tests. The key acceptance criterion for a 510(k) is substantial equivalence to a predicate device.

• Acoustic output
• Biocompatibility
• Cleaning & disinfection effectiveness
• Electromagnetic compatibility
• Electrical and mechanical safety.
  They were found to conform with applicable medical device safety standards. |
  | Quality System Compliance | The design, development, and quality process conforms with 21 CFR 820, ISO 9001:2000, and ISO 13485 quality systems. |
  | Intended Use Alignment with Traditional Clinical Practices | Intended uses and other key features are consistent with traditional clinical practices and FDA guidelines. |

Study Details:

For this 510(k) submission, the primary "study" is the demonstration of substantial equivalence through non-clinical testing and comparison to a legally marketed predicate device.

2. Sample size 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: Not applicable for this type of submission. This 510(k) does not involve a clinical test set in the way a novel AI device might. The review is based on internal technical evaluations, compliance with standards, and comparison to a predicate device.
• Data Provenance: Not applicable. The "data" are primarily technical specifications, test reports for acoustic output, biocompatibility, etc., and a comparison matrix to the predicate device. These are internal company documents and safety standard compliance reports.

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 context of clinical studies with expert consensus, is not part of this 510(k) pathway for an ultrasound system. The "ground truth" here is adherence to engineering specifications and safety standards, and functional equivalence to the predicate device.

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

• Adjudication Method: Not applicable. This concept pertains to resolving discrepancies in expert labeling or diagnoses in clinical studies, which were not conducted for this submission.

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 Comparative Effectiveness Study: No. This device is a diagnostic ultrasound system, not an AI-powered image analysis tool designed to assist human readers, or a novel therapy. Therefore, an MRMC study is not relevant to its clearance.

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

• Standalone Performance Study: No. This device is an imaging system operated by a human physician, not an algorithm, and the concept of "standalone performance" for an algorithm is not applicable.

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

• Type of Ground Truth: The "ground truth" in this context is the established safety and effectiveness of the predicate device (Aloka Alpha 10), and the objective measurements demonstrating that the new device meets recognized safety and performance standards (e.g., acoustic output limits, electrical safety, biocompatibility, cleaning efficacy).

8. The sample size for the training set

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

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

• Ground Truth for Training Set Establishment: Not applicable. As this is not an AI/machine learning device, there is no training set or associated ground truth for it.

In summary: The Aloka Prosound F75 Diagnostic Ultrasound System obtained 510(k) clearance by demonstrating substantial equivalence to a predicate device (Aloka Alpha 10) through technical comparisons and compliance with recognized safety and performance standards. It did not involve clinical trials, expert consensus studies, or AI algorithm performance evaluations as typically seen for novel software or AI/ML devices.

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Diagnostic ultrasound imaging or fluid flow analysis of the human body as follows: Trans-esoph. (non-Card.), Airways and tracheobronchial tree.

OLYMPUS XBF- UC180F-DT8 ULTRASONIC BRONCHOFIBERVIDEOSCOPE has been designed to be used with the SSD-Alpha5 (K041916) and SSD-Alpha10(K043196) diagnostic ultrasound systems (ALOKA CO.,LTD.), video system center, light source, documentation equipment, display monitor, and endo-therapy accessories such as aspiration blopsy needle. The subject device is designed for endoscopic real-time ultrasound imaging, for performing endoscopic ultrasound (EUS) guided fine needle aspiration (FNA) within the airway, tracheobronchial tree, esophagus, and surrounding organs.

The provided document (K070983) is a 510(k) premarket notification decision letter from the FDA for the Olympus XBF-UC180F-DT8 Ultrasonic Bronchofibervideoscope. This type of document primarily focuses on demonstrating substantial equivalence to a predicate device rather than detailing specific performance studies with acceptance criteria in the manner expected for a de novo device or a more complex AI-driven medical device.

Therefore, the document does not contain the detailed information requested regarding acceptance criteria, study design, sample sizes, expert ground truth, adjudication methods, MRMC studies, standalone performance, or training set details typical for an AI/ML device.

Here's why and what information can be extracted/inferred:

• Type of Device: The device is an ultrasonic broncho-fibervideoscope, which is a hardware device for endoscopy with integrated ultrasound capabilities. It's not an AI/ML diagnostic software.
• Regulatory Pathway: The 510(k) pathway demonstrates substantial equivalence to a legally marketed predicate device. This typically involves showing that the new device has the same intended use, fundamental technological characteristics, and safety and effectiveness as the predicate, or that any differences do not raise new questions of safety or effectiveness. Formal clinical studies with performance metrics against acceptance criteria are often not required if engineering and non-clinical bench testing are sufficient to support equivalence.
• Date: The document is dated 2017, predating widespread FDA guidance and requirements for AI/ML device performance studies outlining the specific details you've asked for.

Based on the available document, here's what can be stated about the device and its assessment:

1. Table of Acceptance Criteria and Reported Device Performance:

The document does not explicitly state acceptance criteria in terms of specific performance metrics (like sensitivity, specificity, accuracy) for a diagnostic output. Instead, the "performance" demonstrated for a 510(k) device like this typically involves:

• Bench Testing: Verification of technical specifications (e.g., ultrasound frequency, imaging depth, image quality, mechanical dimensions, electrical safety, biocompatibility, sterilization efficacy).
• Verification and Validation (V&V): Ensuring the device meets its design inputs and intended use through various tests.

The document implicitly "accepts" the device's performance by finding it substantially equivalent to the predicate devices. This means its performance is considered comparable and safe/effective for its intended use.

No explicit table of acceptance criteria or numerical performance results (e.g., sensitivity, specificity, AUC) are provided in this regulatory letter. The letter is a communication of regulatory decision, not a detailed study report.

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

• Test Set Sample Size: Not applicable in the context of typical AI/ML performance studies. For a hardware device, V&V tests would use units of the device itself and potentially phantoms or in-vivo animal models (not specified in this letter).
• Data Provenance: Not applicable in the AI/ML sense. The "data" here would be the results of engineering tests, not patient data for algorithm training/testing.

3. Number of Experts Used to Establish Ground Truth and Qualifications:

• Experts: Not explicitly stated. For a physical device, ground truth during testing would likely be established through engineering measurements, expert medical assessment of image quality (e.g., by radiologists, pulmonologists, gastroenterologists familiar with endoscopic ultrasound), and potentially histological evaluation if biopsy results were part of functional testing (again, not detailed here).
• Qualifications: Not specified within the letter.

4. Adjudication Method:

• Adjudication Method: Not applicable or specified for this type of device and regulatory submission.

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

• MRMC Study: No, this type of study is not mentioned as having been conducted or required for this 510(k) submission. MRMC studies are typically used to assess the impact of diagnostic aids (like AI) on physician performance. The device itself is a diagnostic tool, not an AI aid to another diagnostic process.

6. Standalone Performance:

• Standalone Performance: Not applicable in the context of an AI algorithm. The performance of the device (XBF-UC180F-DT8) and its associated ultrasound system (ALOKA SSD-Alpha 5/10) is its standalone performance as a diagnostic imaging system. The letter doesn't isolate specific performance metrics outside of the combined system's functionality.

7. Type of Ground Truth Used:

• Ground Truth: For a traditional diagnostic imaging device like this, ground truth in V&V trials would typically involve:
  • Phantom studies: Known physical properties measured by other means.
  • Pathology/Histology: For biopsy capabilities.
  • Clinical correlation: Experienced clinician interpretation or follow-up.
  • Engineering specifications: Meeting design requirements.
    The document doesn't specify which ground truth methods were primarily used for the 510(k) submission.

8. Sample Size for the Training Set:

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

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

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

In summary, the provided FDA 510(k) letter for the Olympus XBF-UC180F-DT8 Ultrasonic Bronchofibervideoscope is a regulatory outcome document demonstrating substantial equivalence. It does not contain the detailed performance study information that would be found in a submission for an AI/ML-driven diagnostic device.

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