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The Strados Remote Electronic Stethoscope Platform (RESP) is a non-invasive battery-operated device, including a wearable component, intended to longitudinally acquire, record, and store lung sounds from adult patients in a healthcare or outpatient setting including transition from healthcare setting to outpatient care without interruption. The device stores the data for later playback, review, and analysis by a clinician and comparison with earlier data from the same patient.

The Strados Remote Electronic Stethoscope Platform (RESP) is a non-invasive battery-operated device, including a wearable component, intended to longitudinally acquire, record, and store lung sounds from adult patients. The device stores the data for later playback, review, and analysis by a clinician and comparison with earlier data from the same patient. The Strados Remote Electronic Stethoscope Platform (RESP) is comprised of the Strados Wearable Device (SWD), Strados Charging Station (SCS) and external power supply, and Strados Patient Adhesive (SPA) used to adhere the SWD to the patient. The SWD is controlled by a mobile device running the Strados Mobile Application (SMA) via Bluetooth connection. The Strados Wearable Device (SWD) sits on the chest wall and passively records the patient's airway sounds, including but not limited to cough, wheeze, rhonchi, rales, crackles, coarse (bronchial) breath sounds, quiet breathing, and chest wall movement onto internal memory. The Strados Mobile App (SMA) on a smartphone allows playback of lung sounds from the wearable device in order for clinicians to listen to the patient's lung sounds. The device is used in a healthcare and outpatient setting including the home.

I cannot provide the acceptance criteria and related study details you've requested because the provided document does not contain this information. The document is an FDA 510(k) clearance letter for the Strados Remote Electronic Stethoscope Platform (RESP), which confirms that the device is substantially equivalent to a legally marketed predicate device.

The document discusses:

• Indications for Use: The device is for longitudinally acquiring, recording, and storing lung sounds from adult patients in healthcare or outpatient settings for playback, review, and analysis by a clinician.
• Device Description: Details the components of the RESP, including the Strados Wearable Device (SWD), Strados Charging Station (SCS), Strados Patient Adhesive (SPA), and Strados Mobile Application (SMA).
• Technological Characteristics: Provides a comparison between the subject device and the predicate device, highlighting their similarities in intended use, age of device use, principle of operation, condition of use, and classification.
• Non-Clinical Performance Data: Mentions adherence to various IEC and UL standards for medical electrical equipment, usability, and software life cycle processes. It also lists additional non-clinical tests performed: Frequency response equivalence, Device Cleaning, and Usability Testing.
• Clinical Performance Data: States that clinical performance testing was performed "to validate the quality of the subject device's recorded lung sounds when self-placed and recorded in an outpatient setting." However, it does not provide any specific acceptance criteria or results from this clinical study.

To answer your request, I would need a detailed study report that includes:

1. Acceptance criteria and reported device performance: This would typically involve specific metrics (e.g., sensitivity, specificity, accuracy for detecting certain lung sounds) and the threshold for acceptance.
2. Sample size and data provenance: Number of patients/recordings used and their origin.
3. Number and qualifications of experts: Details about the healthcare professionals who established ground truth.
4. Adjudication method: How consensus was reached for ground truth.
5. MRMC study effect size: If applicable, data on how human reader performance improved with the AI.
6. Standalone performance: Specific results of the algorithm without human intervention.
7. Type of ground truth: How the "truth" was determined (e.g., expert consensus, pathology).
8. Training set sample size: Number of samples used to train any AI components.
9. Ground truth for training set: How ground truth was established for the training data.

Without access to the actual study report mentioned, I cannot provide these details. The provided text only confirms that a clinical study was done and that non-clinical testing met relevant standards.

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