FDA 510(k) Search - By Innolitics (SaMD and AI Experts)

The MOTO PFJ is designed for cemented use in partial knee arthroplasty, if there is evidence of enough sound bone to seat and support the components. Patellofemoral replacement is indicated in the following cases:

Osteoarthritis, post-traumatic arthritis, polyarthritis, severe chondrocalcinosis of the patellofemoral joint.

Previously failed surgical attempts (i.e. arthroscopy, lateral release, tibial tubercle elevation, cartilage transplantation). History of patellar dislocation or fracture, resulting in cartilage degeneration of the patellofemoral joint.

Degeneration induced by dysplasia.

If the surgeon evaluates an unequivocal indication for replacement of the patellofemoral joint, with or without a patella resurfacing, which outweighs the risks associated with the surgery, PFJ replacement may be considered, particularly for young patients.

Device Description

The MOTO PFJ System, subject of this submission, consists of:

o Patello Femoral Joint, made of Cobalt-Chromium-Molybdenum alloy
MOTO Patella, made of UHMWPE ●

The MOTO PFJ System is intended for replacement of the femoral trochlea of the patellafemoral joint affected by injury and/or disease process.

The MOTO PFJ System is intended for cemented use only.

The MOTO PFJ System may be used alone or in combination with the MOTO Partial Knee System Unicompartmental Prosthesis (Medial K161741 and Lateral K183029) and GMK UNI Prosthesis (K162084), to treat multiple conditions of patellofemoral and tibiofemoral regions of the natural knee. The Patello Femoral Joint component is designed to articulate with natural patella or with the dedicated MOTO Patella.

AI/ML Overview

The provided document is a 510(k) summary for the Medacta International SA MOTO PFJ System. It describes the device, its indications for use, and a comparison to predicate devices, along with performance data. However, this document does not describe an AI/ML medical device. It details a knee joint patellofemoral polymer/metal semi-constrained cemented prosthesis.

Therefore, most of the requested information regarding acceptance criteria and studies for an AI/ML medical device (such as sample size for test sets, ground truth establishment, expert adjudication, MRMC studies, and standalone performance) is not applicable or present in this document.

The document focuses on non-clinical performance data to demonstrate substantial equivalence to predicate devices, which is typical for implantable medical devices of this type.

Here's an analysis of what is available and what is not for an AI/ML context:

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

For an AI/ML device: This would typically involve metrics like sensitivity, specificity, accuracy, F1-score, AUC, etc., with pre-defined thresholds for acceptance.
For this device: The acceptance criteria are based on mechanical and material performance, and biological safety. The "performance" is demonstrated through various non-clinical tests.
- Acceptance Criteria (Implied from tests): The device must demonstrate sufficient fatigue endurance under walking and squatting scenarios, proper articular surface congruence, acceptable range of motion, comparable constraints to predicate, comparable contact pressure and areas to predicate, and acceptable wear behavior. Biocompatibility (pyrogenicity, LAL test) is also an acceptance criterion.
- Reported Device Performance:
  - Non-Clinical Studies (Validation & Characterization):
    - Shape and Dimension Validation
    - Cadaveric workshop validation
    - Fatigue Endurance Test (Walking Scenario) - Test Reports A3
    - Fatigue Endurance Test (Squatting Scenario) - Test Reports A4
    - Articular Surface Fully Congruent - Test Report B1
    - Range of Motion - Test Report B2
    - Comparison within Medacta MOTO Patella and Zimmer NexGen Patella Constraints - Test Report B3
    - Comparison within Medacta MOTO Patella and Zimmer NexGen Patella in relation to Contact Pressure and Areas - Test Report B4
    - Wear Behaviour - Test Report B5
    - Bacterial Endotoxin Test (LAL test) - passed (equivalent to USP chapter )
    - Pyrogen test - passed (according to USP )
Table (Reinterpreting for this hardware device):

Acceptance Criterion (Implied)	Reported Device Performance
Adequate Shape and Dimension Validation	Test Report A1 (passed)
Functional validation in cadaveric workshop	Test Report A2 (passed)
Meets Fatigue Endurance (Walking Scenario)	Test Reports A3 (passed)
Meets Fatigue Endurance (Squatting Scenario)	Test Reports A4 (passed)
Articular Surface Fully Congruent	Test Report B1 (passed)
Adequate Range of Motion	Test Report B2 (passed)
Comparable Constraints to Predicate (Zimmer NexGen Patella)	Test Report B3 (passed)
Comparable Contact Pressure/Areas to Predicate (Zimmer NexGen Patella)	Test Report B4 (passed)
Acceptable Wear Behavior	Test Report B5 (passed)
Non-pyrogenic (meets Bacterial Endotoxin Test and Pyrogen Test)	Passed LAL test (European Pharmacopoeia §2.6.14/USP chapter ); Passed Pyrogen test (USP chapter )

2. Sample size used for the test set and the data provenance (e.g., country of origin of the data, retrospective or prospective)

For an AI/ML device: This would refer to the number of cases/images in the independent test set.
For this device: The "test set" here refers to the number of physical devices or components subjected to mechanical and biological testing. The document does not specify the exact number of samples for each test (e.g., how many femoral components were tested for fatigue), nor the specific origin of these physical samples beyond being manufactured by Medacta International SA (Switzerland). The testing is "prospective" in the sense that the tests were performed on newly manufactured devices.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts

For an AI/ML device: This refers to human expert annotations.
For this device: "Ground truth" is established by engineering specifications, biomechanical principles, and established international standards (e.g., ISO, ASTM, Pharmacopoeia) for material properties and mechanical performance. Experts would be engineers, material scientists, and toxicologists interpreting these results. The document does not list the number or qualifications of these experts analyzing the test results, as it's typically part of the company's internal quality system.

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

For an AI/ML device: This refers to resolving disagreements between human annotators.
For this device: Not applicable. Performance is measured against physical and chemical standards, not subjective human interpretations.

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

For an AI/ML device: Refers to studies evaluating AI's impact on human performance.
For this device: Not applicable, as this is a physical implant, not a diagnostic AI. The document explicitly states: "No clinical studies were conducted."

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

For an AI/ML device: Refers to algorithm performance metrics without human interaction.
For this device: Not applicable. There is no algorithm. The performance is the inherent mechanical and biological performance of the device itself.

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

For an AI/ML device: Refers to the definitive determination of the condition being detected/diagnosed.
For this device: The 'ground truth' is based on engineering and material science standards and protocols. For example, fatigue endurance is measured against a specific load cycle standard, wear is measured according to a wear test standard, and biocompatibility is measured against pharmacopoeia standards. There is no "disease state" ground truth for this type of device.

8. The sample size for the training set

For an AI/ML device: Refers to the data used to train the algorithm.
For this device: Not applicable, as there is no AI/ML algorithm requiring a training set. The "training" for such devices would be the iterative design and manufacturing process, and knowledge gained from previous designs, but not in the context of data used to train a machine learning model.

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

For an AI/ML device: Refers to how training data was annotated or labeled.
For this device: Not applicable.

Summary

Regulation Number and Section

§ 888.3540 Knee joint patellofemoral polymer/metal semi-constrained cemented prosthesis.

(a)
Identification. A knee joint patellofemoral polymer/metal semi-constrained cemented prosthesis is a two-part device intended to be implanted to replace part of a knee joint in the treatment of primary patellofemoral arthritis or chondromalacia. The device limits translation and rotation in one or more planes via the geometry of its articulating surfaces. It has no linkage across-the-joint. This generic type of device includes a component made of alloys, such as cobalt-chromium-molybdenum or austenitic steel, for resurfacing the intercondylar groove (femoral sulcus) on the anterior aspect of the distal femur, and a patellar component made of ultra-high molecular weight polyethylene. This generic type of device is limited to those devices intended for use with bone cement (§ 888.3027). The patellar component is designed to be implanted only with its femoral component.(b)
Classification. Class II. The special controls for this device are:(1) FDA's:
(i) “Use of International Standard ISO 10993 ‘Biological Evaluation of Medical Devices—Part I: Evaluation and Testing,’ ”
(ii) “510(k) Sterility Review Guidance of 2/12/90 (K90-1),”
(iii) “Guidance Document for Testing Orthopedic Implants with Modified Metallic Surfaces Apposing Bone or Bone Cement,”
(iv) “Guidance Document for the Preparation of Premarket Notification (510(k)) Applications for Orthopedic Devices,” and
(v) “Guidance Document for Testing Non-articulating, ‘Mechanically Locked’ Modular Implant Components,” and
(2) International Organization for Standardization's (ISO):
(i) ISO 5832-3:1996 “Implants for Surgery—Metallic Materials—Part 3: Wrought Titanium 6-Aluminum 4-Vandium Alloy,”
(ii) ISO 5832-4:1996 “Implants for Surgery—Metallic Materials—Part 4: Cobalt-Chromium-Molybdenum Casting Alloy,”
(iii) ISO 5832-12:1996 “Implants for Surgery—Metallic Materials—Part 12: Wrought Cobalt-Chromium-Molybdenum Alloy,”
(iv) ISO 5833:1992 “Implants for Surgery—Acrylic Resin Cements,”
(v) ISO 5834-2:1998 “Implants for Surgery—Ultra-high Molecular Weight Polyethylene—Part 2: Moulded Forms,”
(vi) ISO 6018:1987 “Orthopaedic Implants—General Requirements for Marking, Packaging, and Labeling,”
(vii) ISO 7207-2:1998 “Implants for Surgery—Components for Partial and Total Knee Joint Prostheses—Part 2: Articulating Surfaces Made of Metal, Ceramic and Plastic Materials,” and
(viii) ISO 9001:1994 “Quality Systems—Model for Quality Assurance in Design/Development, Production, Installation, and Servicing,” and
(3) American Society for Testing and Materials':
(i) F 75-92 “Specification for Cast Cobalt-28 Chromium-6 Molybdenum Alloy for Surgical Implant Material,”
(ii) F 648-98 “Specification for Ultra-High-Molecular-Weight Polyethylene Powder and Fabricated Form for Surgical Implants,”
(iii) F 799-96 “Specification for Cobalt-28 Chromium-6 Molybdenum Alloy Forgings for Surgical Implants,”
(iv) F 1044-95 “Test Method for Shear Testing of Porous Metal Coatings,”
(v) F 1108-97 “Titanium-6 Aluminum-4 Vanadium Alloy Castings for Surgical Implants,”
(vi) F 1147-95 “Test Method for Tension Testing of Porous Metal Coatings,”
(vii) F 1537-94 “Specification for Wrought Cobalt-28 Chromium-6 Molybdenum Alloy for Surgical Implants,” and
(viii) F 1672-95 “Specification for Resurfacing Patellar Prosthesis.”