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DEKA Loop is intended for use with compatible integrated continuous glucose monitors (iCGM) and the DEKA alternate controller enabled (ACE) insulin infusion pump to automatically increase, and suspend delivery of basal insulin based on iCGM readings and predicted glucose values. It can also recommend, and with the user's confirmation, control the delivery of correction boluses when glucose values are predicted to exceed user configurable thresholds.

DEKA Loop is intended for the management of Type 1 diabetes mellitus in persons six years of age and greater.

DEKA Loop is intended for single patient use and requires a prescription.

DEKA Loop is an interoperable Alternate Glycemic Controller (iAGC) and works to control an ACE (Alternate Controller Enabled) insulin pump to automatically increase, decrease, and suspend delivery of basal insulin based on readings from an iCGM (integrated continuous glucose monitor) and glucose values predicted by DEKA Loop. DEKA Loop can also recommend, and with the user's confirmation, control the delivery of correction boluses when glucose values are predicted to exceed user configurable thresholds. It is controlled by an iOS app that is downloaded to a user's iPhone.

The provided document, an FDA 510(k) K234055 clearance letter for the DEKA Loop device, focuses on demonstrating substantial equivalence to a predicate device (Tidepool Loop K203689). It details the device's technological characteristics and mentions performance data primarily through in silico testing for clinical equivalence. However, the document does not directly provide specific acceptance criteria or detailed results of a study designed to prove the device meets those criteria in the format requested (e.g., a table with numerical acceptance values and reported performance). Nor does it describe patient-level ground truth establishment, expert adjudication, or MRMC studies.

The information below is extracted and inferred from the provided text, highlighting what is available and what is explicitly not mentioned or detailed in relation to your specific questions.

Here's a breakdown based on the provided text:

Device Performance Acceptance Criteria and Study Details (Based on available information)

The document primarily relies on demonstration of substantial equivalence to a predicate device (Tidepool Loop) based on technological, functional, and performance characteristics, rather than establishing de novo performance criteria. The "Performance Data" section specifically states: "Additionally, in-silico software challenge testing demonstrated clinical equivalence to the predicate device."

1. Table of Acceptance Criteria and Reported Device Performance:

The document does not provide a table with specific numerical acceptance criteria and corresponding reported device performance values. Instead, it states that "in-silico testing proves that the DEKA Loop algorithm is clinically equivalent to the Tidepool Loop Algorithm." This implies that the 'acceptance' for clinical performance was demonstrating equivalence through in silico methods.

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

• Sample Size (Test Set): Not explicitly stated. The document mentions "in-silico software challenge testing." This implies a simulated patient cohort, but the size of this cohort is not provided in terms of "samples."
• Data Provenance: The nature of in silico testing means it's not based on ex vivo or in vivo patient data in the traditional sense for the test set. It's a computational simulation.
  • For the predicate device's clinical performance (which the subject device aims to be equivalent to), the document states: "Tidepool Loop clinical performance is supported by representative 1,250 participants in a 15 months duration real-world, observational, single arm study of DIY Loop including both pediatric and adult participants." This refers to the predicate's data, not the subject device's in silico test set.

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

• Not applicable / Not stated. Ground truth, in the context of an in silico study for a glycemic controller, would likely refer to the accuracy of the simulated physiological model against known physiological principles or real-world data characteristics, rather than expert annotation of medical images or diagnoses. No human experts are mentioned for establishing ground truth for the in silico test set.

4. Adjudication Method for the Test Set:

• Not applicable / Not stated. Given the in silico nature and lack of human expert involvement in "ground truth" establishment as typically understood in AI imaging, no adjudication method is described.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study was done:

• No. An MRMC study is relevant for human-in-the-loop performance studies, particularly in medical imaging where radiologists or clinicians interpret cases. The DEKA Loop is an automated glycemic controller. The document does not describe any study where human readers (e.g., clinicians) used the DEKA Loop (or a simulated version) to assess its comparative effectiveness against a standard of care or the predicate with outcomes like improved blood glucose control. The stated clinical performance evaluation was in silico device-to-predicate algorithm equivalence.

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

• Yes, implicitly. The "in-silico software challenge testing" is an algorithm-only (standalone) performance evaluation. It assessed the DEKA Loop algorithm's performance against the predicate's algorithm in a simulated environment, without direct human intervention in the loop of glucose regulation within the test.

7. The Type of Ground Truth Used:

• For the in-silico testing, the ground truth would be based on the simulated physiological model's behavior, which is designed to accurately represent human glucose metabolism and insulin action under various conditions. This is a form of simulated data / model-based ground truth rather than expert consensus, pathology, or direct patient outcomes data from a clinical trial for the subject device itself. The goal was to prove "clinical equivalence to the predicate device," meaning the in silico performance mirrored what the predicate device achieved in its real-world clinical study.

8. The Sample Size for the Training Set:

• Not stated. As this is a 510(k) submission for an existing algorithm (the "Loop" algorithm, which DEKA Loop is shown to be equivalent to), and not a novel AI/ML algorithm requiring de novo training, details about its original training set (if any, as an "algorithm" might be more deterministic control logic than a learned AI model in some cases) are not provided in this regulatory document. The focus is on the validation that the DEKA Loop implementation of the algorithm is equivalent to the predicate's.

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

• Not stated. Refer to point 8. If the algorithm involved machine learning, its original training (if any) would have required a separate dataset and ground truth establishment method, which is not detailed in this 510(k) summary. Given the description ("predicts glucose levels... based on prior iCGM readings, insulin delivery history, and user input... uses that prediction to adjust insulin delivery"), it sounds more like a model-based predictive control algorithm rather than a deep learning model trained on a large dataset with ground truth labels.

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