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DX Series Pulse Oximeter Sensors are multi-patient-use sensors intended to provide continuous, non-invasive monitoring of functional arterial oxygen saturation and pulse rate in neonatal, infant, pediatric and adult patients, in environments where pulse oximetry monitors or modules in multi-parameter systems are indicated for use, when in the judgment of a licensed medical practitioner/physician pulse oximetry is required.

The DX Series Pulse Oximeter Sensors combine the operational characteristics of pulse oximetry sensors that have already received Substantially Equivalent determination from FDA (K993979 and K010451) into new Finger Sensor and Y-Sensor configurations that, like the predicate devices, share the same outward physical designs and fundamental scientific technology. The modification enables a sensor to be used with Dixtal's existing pulse oximeter monitor devices as currently listed, as well as to function with other pulse oximetry products. The modified sensors allow healthcare practitioners to more widely standardize pulse oximetry sensors and applicator accessories, and to minimize potential Human Factors issues that might result from using sensors and applicator accessories from multiple manufacturers.

Acceptance Criteria and Study Details for Dixtal DX Series SpO2 Sensors

1. Table of Acceptance Criteria and Reported Device Performance

The provided document does not explicitly state numerical acceptance criteria in a dedicated table format. However, it indicates that the DX Series Sensors' performance was deemed "equivalent" to predicate devices based on various tests.

2. Sample Size and Data Provenance for Test Set

• Sample Size for Test Set:
  • Controlled de-Sat Testing: 24 healthy, non-smoker subjects.
  • Clinical Evaluation (Neonates/Pediatrics): The exact number of patients is not specified, but it was stated to be focused on "Neonatal patients (and some Pediatric patients)".
• Data Provenance:
  • Controlled de-Sat Testing: Conducted under IRB authorization from the University of Southern California at San Francisco (Bickler, formerly Severinghaus). This implies clinical data collected from subjects in the United States, and it was a prospective study given the controlled de-saturation.
  • Clinical Evaluation (Neonates/Pediatrics): Initiated in a Brazilian Healthcare Facility (Irmandade da Santa Casa de Misericórdia de Porto Alegre) after receipt of written authorization from the Facility's registered IRB. This indicates data from Brazil, and it was likely a prospective observational study given the focus on compliance with FDA guidance.

3. Number of Experts and Qualifications for Ground Truth

The document does not explicitly state the number of experts used to establish ground truth or their specific qualifications for the clinical studies. However, for the controlled de-saturation study, it states conducted under "IRB authorization from University of Southern California at San Francisco (Bickler, formerly Severinghaus)." This implies the involvement of clinical professionals and researchers who are experts in pulse oximetry and clinical studies, but specific numbers and detailed qualifications are not provided. The term "medical practitioner/physician" is mentioned in the "Statement of Intended Use" for general context, but not for defining ground truth experts in the studies.

4. Adjudication Method for Test Set

The document does not describe any specific adjudication method (e.g., 2+1, 3+1) for establishing the ground truth or evaluating the test results. It states that "clinical performance and accuracy of the DX Series Sensors is equivalent to the tested predicate devices" based on "statistical standards."

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

No Multi-Reader Multi-Case (MRMC) comparative effectiveness study was done. The studies focused on the performance of the device itself, comparing its accuracy against predicate devices and existing standards, rather than evaluating human readers' improvement with or without AI assistance. The device is a sensor, not an AI-assisted diagnostic tool for human readers.

6. Standalone (Algorithm Only) Performance Study

The clinical performance studies (Controlled de-Sat Testing and Clinical Evaluation) focused on the standalone performance of the DX Series Sensors in measuring SpO2 accuracy. The device itself is a sensor that outputs data, not an algorithm requiring human-in-the-loop for its primary function. Therefore, the clinical studies essentially evaluated its "standalone" performance in a real-world clinical setting, albeit with human subjects.

7. Type of Ground Truth Used

• Controlled de-Sat Testing: The ground truth was established by comparing the device's SpO2 readings against a reference method during controlled de-saturation, typically arterial blood gas analysis (though not explicitly stated, this is standard for such studies to determine true arterial oxygen saturation). This is often referred to as a physiological ground truth.
• Clinical Evaluation (Neonates/Pediatrics): The evaluation aimed to ensure compliance with FDA Draft Guidance for SpO2 accuracy in neonates. This would likely involve comparison against a gold standard for oxygen saturation in these patient populations, again implying a physiological ground truth, often derived from arterial blood measurements.

8. Sample Size for the Training Set

The document does not mention any "training set" as it pertains to machine learning algorithms. The DX Series Sensors are described as hardware devices (pulse oximeter sensors) that utilize "the same fundamental scientific technology as the predicate devices." Therefore, there isn't a machine learning model that would require a distinct "training set" in the context of this submission. The "training" in this context refers to the established scientific principles of pulse oximetry.

9. How Ground Truth for Training Set Was Established

As there is no mention of a machine learning "training set" for the DX Series Sensors, this question is not applicable. The device relies on established physical principles and previous designs rather than a data-driven model that requires a training set and corresponding ground truth.

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