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The reprocessed AllFit Oximetry sensor, OXY-AFR, is a single-patient-use, adhesive sensor for short-term or long-term monitoring of functional oxygen saturation and pulse rate. The device is indicated for use by qualified medical personnel only.

This OXY-AFR sensor has a sensor head, consisting of a red LED and two infrared LEDS mounting on a leadframe, wired to a detector with a 1 meter cable and custom "happisnap" connector. There are two optical windows where the LED light is transmitted between the detector and LEDS. These components are packaged in a heat sealed package. The OXY-AFR is identical to the OXY-AF sensor with the exception of the new patient window adhesive. The ICU, a FR Sensor is for use in small adult and pediatric patients in the hospital, ICU, anesthesia, respiratory therapy, and during transport.

The Datex-Ohmeda OXY-AFR Sensor has the following differences when compared to the Datex-Ohmeda OXY-AF predicate device. The OXY-AFR sensor is a reprocessed OXY-AF sensor During reprocessing the used OXY-AF sensor is tested, cleaned, inspected and decontaminated. After decontamination new patient adhesive is installed over the optical windows and the reprocessed sensor is repacked with new removable tape. The OXY-AFR Sensor and the predicate OXY-AF SENSOR have the same performance specifications. The changes in the labels and instructions for use were made to add the reprocessed part numbers and name of the new device to the IFU. No changes in the warnings, cautions or contraindications have been made

The provided text describes the Datex-Ohmeda OXY-AFR Sensor, a reprocessed pulse oximetry sensor, and its substantial equivalence to a predicate device (Datex-Ohmeda OXY-AF SENSOR, K040831). The submission focuses on demonstrating that the reprocessed sensor maintains the safety and effectiveness of the original device.

Here's a breakdown of the requested information based on the provided text:

1. Table of Acceptance Criteria and Reported Device Performance

The document does not explicitly state numerical "acceptance criteria" for performance metrics like accuracy, sensitivity, or specificity in the way one might expect for a novel AI device. Instead, the core acceptance criterion is substantial equivalence to the predicate device. This means the reprocessed device must perform equivalently to the original.

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

The document does not specify a sample size for a "test set" in the context of a typical AI/ML study (i.e., a dataset used for performance evaluation of an algorithm on unseen data). The testing mentioned (Verification and Validation data, safety testing, and accuracy testing) refers to engineering/device-level performance validation rather than AI model evaluation.

• Sample Size: Not specified for a "test set" in the AI/ML sense.
• Data Provenance: Not applicable as this is a device reprocessing submission, not an AI/ML study with data provenance. The context implies testing was done on the physical reprocessed sensors.

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

This information is not provided in the document. Ground truth as typically understood in AI/ML (e.g., expert labels on images) is not relevant to this submission, which is about the substantial equivalence of a medical device (pulse oximeter sensor). The "ground truth" for this type of device would be established through direct physiological measurement or comparison to a gold standard during accuracy testing.

4. Adjudication Method for the Test Set

This information is not provided and is not applicable given this is not an AI/ML study with a reader study component.

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

This information is not provided and is not applicable. This submission is for a reprocessed pulse oximetry sensor, which is a hardware device for physiological measurement, not an AI diagnostic tool that assists human readers.

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

This information is not provided and is not applicable. The device is a reprocessed pulse oximetry sensor, not a standalone algorithm. Its performance is inherent to the physical sensor and its components.

7. The Type of Ground Truth Used (expert consensus, pathology, outcomes data, etc.)

The document mentions "Accuracy testing" and "Verification and Validation data." For a pulse oximeter, the ground truth for accuracy testing would typically involve:

• Physiological measurements: Comparing SpO2 readings to arterial blood gas measurements (co-oximetry) in human subjects or animal models under controlled desaturation conditions.
• Reference standards: Using a calibrated reference oximeter or a phantom for certain functional tests.

The document does not explicitly detail the specific ground truth method, but it would be based on these types of direct physiological or metrological comparisons, not expert consensus on images or pathology.

8. The Sample Size for the Training Set

This information is not provided and is not applicable. This is not an AI/ML study involving a "training set."

9. How the Ground Truth for the Training Set was Established

This information is not provided and is not applicable. This is not an AI/ML study involving a "training set."

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