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The iLet Dosing Decision Software is intended for use with compatible integrated continuous glucose monitors (iCGM) and alternate controller enabled (ACE) pumps. A self-monitoring of blood glucose (SMBG) meter may also be used for manual input of blood glucose values to continue insulin dosing for a limited period of time when input from the iCGM is temporarily not available. The iLet Dosing Decision Software autonomously determines and commands an increase, decrease, maintenance, or suspension of all basal doses of insulin and autonomously determines and commands correction doses of insulin based on input from an iCGM, and it autonomously determines and commands meal doses of insulin based on meal announcements. iLet Dosing Decision Software is intended for the management of type 1 diabetes mellitus in people 6 years of age or older. iLet Dosing Decision Software is intended for single patient use and requires a prescription.

Device Description

The iLet Dosing Decision Software works in conjunction with a compatible alternate controller enabled (ACE) pump. The iLet Dosing Decision Software only requires initialization with the user's body mass (body weight).

The iLet Dosing Decision Software does not require carbohydrate counting by the user or the use of carbohydrate- to-insulin ratios. Although the iLet system does not require a user to enter an exact carb amount to calculate and administer a meal bolus, it does require that the user announce the meal (e.g., breakfast, lunch, dinner) AND provide an estimated carb content as "Usual", "More", or "Less" than is routine for that meal type.

The iLet Dosing Decision Software does not require any information about the user's total daily dose of insulin, basal or long-acting insulin requirements, or insulin correction factors. It is an insulin titration system that requires no insulin-dose determinations by the user or provider. During normal operation, the iLet bionic pancreas (iLet ACE Pump with the iLet Dosing Decision Software installed) autonomously responds every five minutes to a glucose signal, from an iCGM that is worn by the user, by computing a control signal that translates to a dose of insulin, which is delivered to the user through the subcutaneous (SC) route. The iLet dosing decision software has three insulin controllers (algorithms) running in parallel: an adaptive basal insulin controller, which continually adapts to each individual's basal metabolic need for insulin, an adaptive bolus controller which provides doses that are required above and beyond the basal metabolic needs, and an adaptive meal dose controller which provides insulin in response to a meal announcement.

The iLet is intended to dose insulin based on CGM data. In the events where CGM stops providing glucose data to the iLet Dosing Decision Software BG-run mode feature will serve to temporarily continue insulin delivery. BG-run mode will determine and command basal insulin based on past requirements and will allow announcement of meals and entry of fingerstick BG measurements, which will be treated as iCGM data and may result in commanding administration of insulin or temporary suspension of basal insulin. BG-run mode use should always be for the shortest duration possible with the goal to resume CGM.

AI/ML Overview

Here's a breakdown of the acceptance criteria and the study details for the iLet Dosing Decision Software, based on the provided document:

1. Table of Acceptance Criteria and Reported Device Performance

The document does not explicitly state formal "acceptance criteria" in a quantitative manner (e.g., "HbA1c must decrease by X%"). Instead, it presents key outcomes from the clinical study and concludes that the modified device's performance regarding safety and effectiveness is comparable to the predicate device.

Metric	Acceptance Criteria (Implicit - Comparability to Predicate)	Reported Device Performance (6-17 Year Olds, Fiasp)
Effectiveness Metrics
Decrease in HbA1c	Comparable decrease	0.56% decrease from baseline to 13 weeks
Increase in Time in Range (TIR) (70-180 mg/dL)	Comparable increase	12.0% increase from baseline
Decrease in Mean CGM glucose	Comparable decrease	18 mg/dL decrease
Safety Metrics
Increase in Time

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K Number

K220916

Device Name

iLet® Dosing Decision Software

Manufacturer

Beta Bionics, Inc.

Date Cleared

2023-05-19

(415 days)

Product Code

QJI,OJI

Regulation Number

862.1356

Type

Traditional

Panel

Clinical Chemistry (CH)

Reference & Predicate Devices

K200467

Intended Use

Device Description

The iLet Dosing Decision Software is an iAGC indicated for the management of type 1 diabetes mellitus. It autonomously determines and commands an increase, decrease, maintenance, or suspension of all basal doses of insulin and autonomously determines and commands correction doses of insulin based on input from an iCGM, and it autonomously determines and commands meal doses of insulin based on meal announcements. The iLet Dosing Decision Software is intended for the management of type 1 diabetes in people 6 years of age or older. The iLet Dosing Decision Software works in conjunction with a compatible alternate controller enabled (ACE) pump. The dosing decision software includes adaptive control algorithms that autonomously and continually adapt to the ever-changing insulin requirements of each individual to enable lifelong adaptive learning. The iLet Dosing Decision Software only requires initialization with the user's body mass (body weight). The iLet Dosing Decision Software does not require carbohydrate counting by the user or the use of carbohydrate- to-insulin ratios. Although the iLet system does not require a user to enter an exact carb amount to calculate and administer a meal bolus, it does require that the user announce the meal (e.g., breakfast, lunch, dinner) AND provide an estimated carb content as "Usual", "More", or "Less" than is routine for that meal type. The iLet Dosing Decision Software does not require any information about the user's total daily dose of insulin, basal or long-acting insulin requirements, or insulin correction factors. It is an insulin titration system that requires no insulin-dose determinations by the user or provider. During normal operation, the iLet bionic pancreas (iLet ACE Pump with the iLet Dosing Decision Software installed) autonomously responds every five minutes to a glucose signal, from an iCGM that is worn by the user, by computing a control signal that translates to a dose of insulin, which is intended to be delivered to the user through the subcutaneous (SC) route. The iLet dosing decision software has three insulin controllers (algorithms) running in parallel: an adaptive basal insulin controller, which continually adapts to each individual's basal metabolic need for insulin, an adaptive bolus controller which provides doses that are required above and beyond the basal metabolic needs, and an adaptive meal dose controller which provides insulin in response to a meal announcement. The iLet is intended to dose insulin based on CGM data. In the events where CGM stops providing glucose data to the iLet Dosing Decision Software BG-run mode feature will serve to temporarily continue insulin delivery. BG-run mode will determine and command basal insulin based on past requirements and will allow announcement of meals and entry of fingerstick BG measurements, which will be treated as iCGM data and may result in commanding administration of insulin or temporary suspension of basal insulin. BG-run mode use should always be for the shortest duration possible with the goal to resume CGM.

AI/ML Overview

The provided text describes the iLet® Dosing Decision Software, an interoperable automated glycemic controller (iAGC), and the study conducted to demonstrate its performance.

Here's an analysis of the acceptance criteria and study as requested:

1. A table of acceptance criteria and the reported device performance

The document doesn't explicitly list "acceptance criteria" in a bulleted or numbered format with corresponding performance metrics like a typical FDA performance table. However, the "Endpoints" section in the Clinical Performance summary serves as the de facto acceptance criteria for the clinical study outcomes. The "Conclusions" section then describes how the device performed against these.

Acceptance Criteria (Study Endpoint)	Reported Device Performance (Conclusion)
Primary Endpoint:
HbA1c at 13 weeks	The study concluded that use of the bionic pancreas (with iLet Dosing Decision Software) with Novolog/Humalog or Fiasp was safe when compared with standard of care. (Implicitly, the changes in HbA1c in the iLet group were considered clinically acceptable and superior based on results not fully detailed in this summary for the exact change, but the substantial equivalence claim implies positive results.)
Key Secondary Endpoints:
Time 180 mg/dL	(Details not explicitly provided in the "Conclusion" section of the summary, but implied to be acceptable for safety and efficacy.)
Time > 250 mg/dL	(Details not explicitly provided in the "Conclusion" section of the summary, but implied to be acceptable for safety and efficacy.)
Standard deviation	(Details not explicitly provided in the "Conclusion" section of the summary, but implied to be acceptable for safety and efficacy.)
Additional CGM metrics	(Details not explicitly provided in the "Conclusion" section of the summary, but implied to be acceptable for safety and efficacy.)
Safety Outcomes:
Severe hypoglycemia	Use of the bionic pancreas was safe when compared with standard of care.
Diabetic ketoacidosis (DKA)	Two DKA events occurred in the iLet Group related to infusion set failures (not directly attributed to the software's dosing decision). Overall, the conclusion states it was "safe".
Other serious adverse events	Use of the bionic pancreas was safe when compared with standard of care.
BG-run feature performance (Ancillary Study)	The bionic pancreas can be safely used with blood glucose meter input temporarily instead of CGM should this become necessary for a user.

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 the Clinical Study (RCT): 440 adult and child participants.
Country of Origin: United States (16 clinical sites).
Study Design: Prospective, multi-center, randomized controlled trial (RCT).
Ancillary Study (BG-run feature): Participants in the BP Groups had the option of participating in this ancillary study, but a specific sample size for this ancillary study is not provided, only that it followed the RCT.

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)

This information is not provided in the document. The ground truth for the clinical study was established by the actual physiological responses and clinical outcomes of the participants with Type 1 Diabetes, measured by standard medical metrics (HbA1c, CGM data, adverse events). There is no mention of external experts establishing a "ground truth" for the device's dosing decisions themselves, as the device is designed to operate autonomously. The study evaluated the effectiveness and safety of the device's autonomous decisions.

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

This is not applicable to this type of study. Adjudication methods like 2+1 or 3+1 refer to expert consensus processes for evaluating medical images or diagnoses, typically used when establishing ground truth for AI algorithms in diagnostic imaging. For this device, which makes automated dosing decisions for diabetes management, the "ground truth" is physiological response, not expert interpretation. Adverse events would typically be adjudicated by a Clinical Events Committee (CEC), but the specific method (e.g., how many members reviewed each event) is not detailed.

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

A Multi-Reader Multi-Case (MRMC) comparative effectiveness study was not done.
This type of study is primarily relevant for diagnostic imaging AI where human readers interpret medical images. The iLet Dosing Decision Software is an automated glycemic controller, not an imaging interpretation aid.
The study was a randomized controlled trial comparing the iLet system (which is the AI, managing insulin autonomously) to "standard care" (human-managed insulin delivery, either by pump or injections, though with CGM monitoring). It assesses the device's performance versus standard human-led care, not how human readers improve with AI assistance.

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

Yes, a standalone study was done in the sense that the iLet Dosing Decision Software operates autonomously, commanding insulin doses without real-time human intervention in its decision-making process. The clinical trial directly evaluated this autonomous "algorithm only" performance within the iLet Bionic Pancreas System.
The comparison was between the iLet system (operating autonomously) and standard human-managed care.

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

The ground truth for evaluating the iLet Dosing Decision Software's performance in the clinical study was primarily outcomes data and physiological measurements:

HbA1c (a measure of average blood glucose over time).
Continuous Glucose Monitoring (CGM) metrics (e.g., time in target range, time spent in hypo/hyperglycemia, mean glucose, standard deviation).
Safety outcomes (severe hypoglycemia, diabetic ketoacidosis, other serious adverse events).

8. The sample size for the training set

The document does not provide information regarding the sample size used for the training set of the iLet Dosing Decision Software algorithm. It only details the clinical study for validation of the device.

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

The document does not provide information on how the ground truth for the training set was established. The iLet Dosing Decision Software employs "adaptive control algorithms that autonomously and continually adapt to the ever-changing insulin requirements of each individual to enable lifelong adaptive learning." This suggests a machine learning or adaptive control approach, which would have been trained on or developed using a dataset, but the specifics of that training data and ground truth establishment are not disclosed in this summary.

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