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SmartBag is intended for use inside and out of hospitals for any patient with a diversionary urinary or fecal stoma. It works in conjunction with 11 Health's care management platform known as Alfred. The SmartBag system uses integrated sensors to continuously monitor bag filling and drainage, providing cumulative output data to the patient and clinical team. The system also monitors skin condition, the occurrence of leaks and visual condition of the stoma.

SmartBag's intended use is to help ostomates acclimate to their lifestyle. It works in conjunction with 11 Health's care management platform known as Alfred, designed for patients and medical professionals. The SmartBag system (including the wafer and Hub), offers a continuous ostomy monitoring system, tracks the estimated volumetric filling of the bag and visual condition of the stoma using integrated sensor technology.

The SmartBag Sytem is for adult use only. (22 years and above)

The SmartBag with integrated thermistors and capacitive sensors can be used in place of a traditional ostomy bag. It notifies patients or medical professionals of any potential leaks around the peristomal skin as well as giving an estimate of the output volume within the bag.

Alfred software is a companion software suite for SmartBag. It consists of Alfred mobile app and Alfred hospital app. Alfred mobile app is a companion application for mobile phone. The application provides SmartBag user easy access to bag status, hydration tracking and restroom search functionalities. The SmartBag could operate without Alfred mobile app.

The sensors in the bag and the wafer will be able to transmit data to the hub via NFC protocol communication. The data from the hub will be transmitted to the cloud via LTE-M data transfer via secure MQTT protocol and then this data can be downloaded to web supported devices – such as an iPhone.

The provided text is a 510(k) summary for the SmartBag (SmartPouch) device. While it describes the device's indications for use, comparison to predicate devices, and non-clinical performance data, it does not contain a table of acceptance criteria or a detailed study proving the device meets such criteria in terms of performance metrics like accuracy, sensitivity, or specificity against a defined ground truth on a test set.

The document primarily focuses on demonstrating the functionality and usability of the device's volumetric and leakage detection features through a series of bench experiments and simulations. It concludes that the SmartBag prototype is "proven to be functional for volumetric measurement and leakage detection from our simulated bench tests."

Therefore, I cannot provide the requested information regarding acceptance criteria, reported device performance, sample sizes for test and training sets, expert involvement for ground truth, adjudication methods, MRMC studies, or standalone performance. The document does not describe a clinical study with these elements.

However, I can extract information about the types of non-clinical tests performed, which could be considered a form of "acceptance criteria demonstration" for basic functionality:

Non-Clinical Performance Data (Functionality Testing):

Here's why the other requested information cannot be provided from this document:

• Sample size for the test set and data provenance: The document details "in-house non-clinical bench experiments" and "simulated" tests, some on "human volunteers." It doesn't specify a rigorous, statistically powered "test set" in the context of typical AI/medical device clinical studies (where cases are adjudicated against a ground truth). It refers to test reports like "028_Testing Report 3_SmartBag Prototype Water and Apple Infusion Simulation Test," suggesting a focus on engineering verification rather than clinical validation. The provenance is "in-house."
• Number of experts and qualifications, adjudication method: Not applicable as the testing described is primarily engineering bench testing and simulation, not expert-adjudicated clinical data.
• Multi-reader multi-case (MRMC) comparative effectiveness study: No such study is mentioned or implied. The device's primary function is continuous monitoring and data collection, not an AI assisting human readers with interpreting medical images or data for diagnostic purposes in a comparative setting.
• Standalone (algorithm only) performance: While the device collects data autonomously, the "performance" described is functional operation in simulated environments rather than a quantifiable diagnostic or predictive accuracy.
• Type of ground truth used: The "ground truth" for these tests appears to be the known conditions of the simulations (e.g., specific volumes of fluid infused, presence/absence of simulated leaks, known temperatures, known viscosities). It's not a clinical ground truth like pathology or patient outcomes.
• Sample size for the training set and how ground truth for training was established: The document does not describe a machine learning model that was "trained" in the typical sense with a separate dataset. The "sensors" and "algorithms" (implicitly) seem to be based on physical principles and engineering design, not data-driven machine learning from a large training dataset.

In summary, this 510(k) summary focuses on demonstrating the engineering functionality and safety of the SmartBag device through non-clinical bench tests and simulations, rather than providing clinical performance data from a statistically designed study involving ground truth established by experts.

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The OSTOM-i™ Alert is intended to be used as an accessory to any ostomy bag by monitoring the filling of the bag which information is sent via Bluetooth to a tablet computer to warn healthcare personnel when a patient's bag is close to being full. The Tablet computer automatically captures the data as to the volume and timing of output for each patient.

The OSTOM-i™ Alert is indicated for all patient populations.

The sensor-based OSTOM-i™ Alert attaches to any ostomy bag and is able to send messages via Bluetooth to a mobile app to warn patient when ostomy bag is close to being full. The sensor-based OSTOM-i™ Alert is intended to be used by the hospital environment.

The OSTOM-i™ Alert is indicated for all patient populations.

The sensor-based OSTOM-i™ Alert attaches to any ostomy bag and is able to send messages via Bluetooth to a mobile app to warn the health care provider when their patients' bags are close to being full, or to the patient in the post-hospital setting.

The flex sensor changes its resistance according to curvature and converts resistance into mls, thereby detecting the progressive filling of the bag. This information is relayed to the tablet generating an additional report on bag status versus the visual one now available.

The provided document describes the Ostom-i™ Alert, an accessory for ostomy bags. The device monitors the filling of the bag and sends an alert via Bluetooth to a tablet computer when the bag is close to being full. The document focuses on demonstrating the device's substantial equivalence to a predicate device (Hollister, Inc.'s Two Piece Ostomy System, K813269) rather than presenting a traditional clinical study with acceptance criteria and statistical performance metrics for diagnostic accuracy.

Here's an attempt to extract and synthesize the requested information based on the provided text:

1. Table of Acceptance Criteria and Reported Device Performance

The document does not explicitly state quantitative acceptance criteria or provide specific diagnostic accuracy metrics (like sensitivity, specificity, accuracy) for the Ostom-i™ Alert. Instead, it focuses on qualitative performance and safety aspects as part of demonstrating substantial equivalence. The "performance" reported is primarily about its function and usability, not diagnostic accuracy against a ground truth.

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

The document does not specify a numerical sample size for the "test set" in the traditional sense of a diagnostic performance study.

• For the "Usability and Label Comprehension Study by Intended User," it mentions "nurses who would use this in a hospital setting" but does not give a number of participants.
• For "Patient Usability Testing," it refers to "Patients" but does not specify how many patients or simulations were involved.

The data provenance is not explicitly stated in terms of country of origin, nor is it clearly categorized as retrospective or prospective for a clinical study. The usability and performance testing described appear to be prospective tests conducted as part of the device development and submission process.

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

This information is not provided. Since the device's function is to alert based on physical fill rather than interpret complex imagery or physiological signals, the "ground truth" for the fill level would likely be objective (e.g., measured volume, physical observation of a full bag). The "experts" involved were primarily "nurses" for the usability study, but their role was to evaluate usability, not to establish ground truth for a diagnostic task.

4. Adjudication Method

No adjudication method is mentioned for establishing ground truth, as the studies described are not focused on diagnostic accuracy requiring expert consensus.

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

No MRMC study is mentioned. The document does not describe a study comparing human readers (with vs. without AI assistance) or quantifying an effect size of improvement. The device acts as a direct monitor and alert system, not an AI to assist human interpretation.

6. Standalone Performance Study

Yes, a standalone performance assessment was conducted, though it's not a "standalone algorithm performance" in the typical sense of a diagnostic AI. The "Performance Testing" section describes various tests (patient usability, environmental robustness, biocompatibility, EMC, wireless coexistence) that evaluate the device's function and safety independently. The device's core function is to detect and report bag fullness, which was tested by simulating an alert condition.

7. Type of Ground Truth Used

The ground truth implicitly used for the device's primary function (alerting when the bag is full) is physical observation/simulation of a full bag. This is supported by:

• "An alert was simulated by curving the bag to the filling of the bag thereby creating the alarm condition."
• The "flex sensor changes its resistance according to curvature and converts resistance into mls, thereby detecting the progressive filling of the bag."

For usability studies, the ground truth is subjective user feedback and observation of task completion.

8. Sample Size for the Training Set

This information is not applicable or provided. The Ostom-i™ Alert appears to be a sensor-based device that directly measures and converts physical changes (flex/curvature) to volume, rather than an "algorithm" in the machine learning sense that requires a training set of data.

9. How Ground Truth for the Training Set Was Established

This information is not applicable or provided, as there is no mention of a training set for a machine learning algorithm.

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