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510(k) Data Aggregation
(141 days)
Flexi-Seal PROTECT Fecal Management System
The Flexi-Seal® PROTECT ™ Fecal Management System is an indwelling fecal management catheter intended for use to manage fecal incontinence through the collection of liquid stool and to provide access to administer medications. The device is not intended for use on pediatric patients.
The new device is called Flexi-Seal® PROTECT™ Fecal Management System which is an upqrade to the predicate device K150350. Flexi-Seal® Signal™ Fecal Management System (also known as Flexi-Seal® Signal™ Fecal Management System with Odor Barrier), launched in October 2015. The new device uses the same design and components except
- (1) the change from the 1-dome fill indicator to a new 2-dome fill indicator to provide visual cue by using
- (a) a green color dome to indicate an optimal fill status of retention balloon which is similar to the existing fill indicator except the color, and
- (b) a red color dome to indicate the over inflation status of retention balloon,
- (2) adding a white cap that can close off the inflation port,
- (3) adding a dark blue color in the sample port, and
- (4) Privacy bag (one bag instead of 3).
The device is comprised of the following components (in a clamshell pack with label card, IFU and 5 date labels): see diagram
The provided document is a 510(k) premarket notification for a medical device called the Flexi-Seal® PROTECT™ Fecal Management System. It primarily focuses on demonstrating substantial equivalence to a predicate device (Flexi-Seal® SIGNAL™ Fecal Management System) rather than presenting a study to prove acceptance criteria for a new, AI-powered device.
Therefore, the information required to answer your specific questions about acceptance criteria and a study proving device performance (especially in the context of AI, multi-reader multi-case studies, or standalone algorithm performance) is not present in the provided text.
The document discusses non-clinical testing for functional and structural parameters to demonstrate that modifications to the predicate device do not affect safety and effectiveness. These are not acceptance criteria for an AI algorithm's performance.
Here's an analysis based on the information that is available, highlighting the absence of the requested details:
1. A table of acceptance criteria and the reported device performance
The document provides a comparison table (pages 7 and 8) outlining similarities and differences between the subject device (Flexi-Seal® PROTECT™) and the predicate device (Flexi-Seal® SIGNAL™). This table details parameters and their values for both devices, mainly focusing on physical characteristics and some functional aspects.
This is NOT a table of acceptance criteria for an AI study. It describes product specifications and how the new device compares to the old one. For example:
| Parameter | Acceptance Criteria (from document, interpreted as "same as predicate") | Reported Device Performance (from document, interpreted as "new device value") |
|---|---|---|
| Fill indication for optimal inflation volume | Green dome inflated at 35 - 45 ml (Predicate was White dome inflated at 35-45ml) | Green dome inflated at 35 - 45 ml |
| Fill Indicator for overfilling status | Red dome inflated beyond 45 ml and above ("A new feature in the subject device") | Red dome inflated beyond 45 ml and above |
| Cap for inflation port | Removable silicone cap ("A new feature in the subject device") | Removable silicone cap with sufficient removal force and joint strength |
| Sampling port color | Dark blue color (Predicate was natural color) | Dark blue color |
| Collection bag configuration | One bag (Predicate was three bags) | One bag with gas filter |
| Collection bag - front panel color | White with a clear window (Predicate was Clear) | White with a clear window |
| Other parameters (e.g., Catheter tube material, tensile strength, balloon size) | Generally "Both devices are the same" as the predicate device (e.g., Silicone rubber, 245N, 56.82mm) | Match the predicate device's specifications |
The "Performance Testing - Bench" section (page 5-6) lists tests conducted (e.g., fill indicator testing, cap testing, sampling port testing, privacy bag testing, journey hazard testing) but does not provide specific numerical acceptance criteria or performance results within the provided text. It only states that "Based on non-clinical testing of the subject device for functional and structural parameters, the modifications in the subject device do not affect the safety and effectiveness of the predicate device."
2. Sample size used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective)
This information is not provided in the document. The document refers to "non-clinical testing" (bench testing) and does not involve patient data or clinical studies with test sets in the context of an AI device.
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. No expert review or ground truth establishment relevant to an AI model is mentioned, as this is not an AI device submission.
4. Adjudication method (e.g. 2+1, 3+1, none) for the test set
This information is not provided in the document.
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 in the document. No MRMC study is mentioned, as this is not an AI device.
6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done
This information is not provided in the document. No standalone algorithm performance is mentioned.
7. The type of ground truth used (expert consensus, pathology, outcomes data, etc)
This information is not provided in the document. The "ground truth" for the non-clinical tests would be reference measurements or engineering specifications, not clinical outcomes or expert consensus for diagnoses.
8. The sample size for the training set
This information is not provided in the document. No training set is involved as this is not an AI/ML device.
9. How the ground truth for the training set was established
This information is not provided in the document. No training set or associated ground truth establishment is mentioned.
In summary: The provided FDA 510(k) submission pertains to a conventional medical device (Fecal Management System) and its modifications. It details bench testing to show equivalence to a predicate device. None of the questions related to AI-specific acceptance criteria, study designs (test/training sets, experts, MRMC, standalone performance), or ground truth establishment for AI models are applicable or answerable using this document.
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