(68 days)
The Lympha Press Optimal is intended for the following Indications for Use: Primary lymphedema (for example, congenital lymphedema/ milroy's disease) Secondary lymphedema (for example, post-mastectomy, chronic edema, post-traumatic edema) Venous disorders (for example, venous insufficiency, varicose veins, venous stasis ulcers) Dysfunction of the muscle pump (for example, promotion of wound recovery, reduction of edema and lower limb pain following trauma and sports injuries) The device is intended to be used by the patient at home, as well as by physicians at clinics or hospitals.
Mego Afek's Lympha Press Optimal Model 1201AP Compression Therapy Device is a modification of the original Lympha Press Plus device. It utilizes a software controlled air compression pump, which sequentially inflates and deflates cells within a compression garment (sleeve) that is put around a limb. This helps to push excessive interstitial fluid in the treated limb, back into the venous and lymphatic systems; improve limb circulation; and thus treat the symptoms of a variety of lymphatic disorders, venous disorders and dysfunction of the "muscle pump". The device consists of a main console and compression garments. The main console contains an air compressor that is regulated by an electro-mechanical mechanism, including pressure sensors and a rotating disc controlling air outflow. The regulated compressed air is transferred via an air distributor through a series of hoses to the sleeve garments. In the Model 1201 device, each garment contains up to 12 overlapping pressure cells. The sleeve fits on the affected limb and can be easily adjusted to any limb size within the sleeve tolerance.
This document (K082149) describes the Lympha Press Optimal Model 1201AP Compression Therapy Device, a compressible limb sleeve, and its substantial equivalence to a predicate device.
However, the provided text does not contain acceptance criteria or detailed results from a study that proves the device meets specific performance criteria in the way typically expected for an AI/CADe device.
Instead, this 510(k) summary focuses on safety, general performance, and substantial equivalence to a predicate device for a physical medical device (a compression therapy device), rather than a diagnostic algorithm. Therefore, many of the requested categories for AI/CADe studies are not applicable.
Here's an analysis based on the information provided, highlighting what is present and what is absent:
Analysis of Acceptance Criteria and Device Performance
1. A table of acceptance criteria and the reported device performance
| Acceptance Criteria (Explicitly Stated) | Reported Device Performance |
|---|---|
| No specific quantitative performance acceptance criteria (e.g., sensitivity, specificity, F1-score) are provided. The basis for acceptance is "substantial equivalence" to a predicate device for safety and efficacy, and meeting functional specifications. | The device has been "subjected to extensive safety, performance testing, and validation before release, as required by the risk analysis performed for device modifications." "Final testing of the Model 1201AP Compression Therapy device included various performance tests and software validation tests, designed to ensure that the device met all its functional specifications." "Tests have been performed to ensure the device complies with industry and safety standards." "Inflation/ Deflation pressures and sequences are similar to those of predicate devices. Operating modes are similar to those of predicate devices." The conclusion states the device is "substantially equivalent in safety and efficacy to the predicate devices." No specific metrics (e.g., pressure accuracy, cycle timing deviations, or number of cycles tested) are quantified. |
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 Test Set: Not applicable/Not provided. This device is a physical, mechanical system, and the testing described is engineering validation (functional, safety standards, software validation) rather than a clinical study with patient data 'test sets' in the AI sense.
- Data Provenance (country of origin, retrospective/prospective): Not applicable/Not provided. The testing relates to the device's operational characteristics, not clinical data from patients.
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)
- Number of Experts: Not applicable/Not provided. Ground truth in the context of diagnostic performance (e.g., for an AI algorithm) is not relevant here. The ground truth for this device would be established by engineering specifications and safety standards.
4. Adjudication method (e.g. 2+1, 3+1, none) for the test set
- Adjudication Method: Not applicable/Not provided. This concept is not relevant to the described testing of a physical compression therapy device.
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
- MRMC Study: No. This is a physical therapy device, not an AI/CADe system for interpretation.
6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done
- Standalone Performance: Not applicable. While there is software controlling the device, its performance is as an integrated system, not a standalone diagnostic algorithm.
7. The type of ground truth used (expert consensus, pathology, outcomes data, etc)
- Type of Ground Truth: For the engineering and safety testing, the "ground truth" would be the device's functional specifications, industry standards (e.g., for electrical safety, materials), and the performance of the predicate device. These are objective engineering and regulatory benchmarks, not clinical ground truth derived from patient outcomes or expert reads.
8. The sample size for the training set
- Sample Size for Training Set: Not applicable/Not provided. This device does not use machine learning that requires a 'training set' of data.
9. How the ground truth for the training set was established
- Ground Truth for Training Set: Not applicable/Not provided.
Summary regarding AI/CADe Relevance:
This 510(k) summary is for a physical medical device (a compressible limb sleeve) and not an AI or CADe system. The performance testing described is focused on general safety, functional specifications, software validation for device control, and demonstrating substantial equivalence to a predicate device. Therefore, the detailed questions pertaining to acceptance criteria and study designs typically used for AI/CADe devices (like sample sizes for test/training sets, expert ground truth, MRMC studies, standalone performance, etc.) are not addressed in this document because they are not applicable to the type of device and regulatory submission it represents.
§ 870.5800 Compressible limb sleeve.
(a)
Identification. A compressible limb sleeve is a device that is used to prevent pooling of blood in a limb by inflating periodically a sleeve around the limb.(b)
Classification. Class II (performance standards).