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510(k) Data Aggregation
(295 days)
Triple Jump Israel Ltd.
The Inessa System is intended for the subcutaneous delivery of insulin, at set and variable rates, for the management of diabetes mellitus in persons requiring insulin. The Inessa System is able to reliably and securely communicate with compatible, digitally connected devices, including automated insulin dosing software, to receive, and confirm commands from these devices. The Inessa System is intended for single patient, home use and requires a prescription. The Inessa System is indicated for use in individuals 6 years of age and greater.
The Inessa System ("System") is intended for subcutaneous delivery of insulin at set and variable rates, bolus or basal. The Inessa System includes a skin-adhered Patch Pump that is programmed and controlled wirelessly by a handheld Controller. The System main components include: Patch Pump: a skin adhered, syringe pump type, designed for insulin delivery at set and variable basal and/or bolus doses. The Patch Pump includes two parts: Pump: a reusable part that includes motor, electronics, drive mechanism, and o rechargeable battery. Two Pumps are provided, one is charged (P2) while the other is in use (P1). Cartridge: a sterile disposable part that includes insulin Reservoir. o Controller: The System user interface is a handheld, Alternate Controller Enabled (ACE), providing instructions to the Pump and receiving information from the Pump using wireless Bluetooth Low Energy (BLE) communication. The System includes the following accessories: Inserter: disposable, for insertion of Soft Cannula and retraction of Insertion needle. Filling kit: disposable, for filling of the Cartridge. It includes Filling needle, Filling syringe and Vial adaptor . Charger: reusable, for charging the Pump and the Controller. Docker: reusable, docking station for pumps for charging and protecting the Pump when not in use. The System also includes a Bolus Calculator, accessible through the System Controller. Based on user inputs of blood glucose (current and targeted), carbohydrate intake (meals), patient's insulin characteristics (i.e., Insulin Duration of Action, Insulin Correction Factor, Insulin-to-Carbs Ratio), this feature calculates suggested and estimated values for: Correction Bolus (amount of insulin needed to correct elevated blood glucose (BG) ● level): . Meal Bolus (amount of insulin needed to cover carbohydrates in an upcoming meal); and "Insulin on Board" or "Bolus on Board" (estimation of how much Active Insulin (Al) . remains in the body from previous boluses).
The provided text is a 510(k) summary for the Inessa System, an insulin pump. It details the device's classification, intended use, technological characteristics, and safety and performance data. However, it does not include information about a study that assesses the device's performance against specific acceptance criteria in the context of AI (Artificial Intelligence) or Diagnostic Accuracy.
The document focuses on demonstrating substantial equivalence to a predicate device (Omnipod DASH™ Insulin Management System) for regulatory clearance, primarily through engineering and safety testing (e.g., sterilization, biocompatibility, electrical safety, delivered volume accuracy, occlusion testing, human factors, and usability).
Therefore, I cannot provide a table of acceptance criteria and reported device performance, or details about sample size, data provenance, expert involvement, adjudication methods, MRMC studies, standalone AI performance, or ground truth establishment related to AI or diagnostic accuracy, because this information is not present in the provided text.
The text does mention "automated insulin dosing software" as something the Inessa System can communicate with, but it does not describe any specific studies or acceptance criteria related to the performance of such AI/software in a diagnostic or prescriptive context. The "software verification and validation" mentioned refers to the general software within the device, not necessarily an AI component with specific diagnostic performance metrics.
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