(95 days)
PeekMed web is a system designed to help healthcare professionals carry out pre-operative planning for several surgical procedures, based on their imported patients' imaging studies. Experience in usage and a clinical assessment is necessary for the proper use of the system in the revision and approval of the output of the planning. The multi-platform system works with a database of digital representations related to surgical materials supplied by their manufacturers.
This medical device consists of a decision support tool for qualified healthcare professionals to quickly and efficiently perform the pre-operative planning for several surgical procedures, using medical imaging with the additional capability of planning the 2D or 3D environment. The system is designed for the medical specialties within surgery and no specific use environment is mandatory, whereas the typical use environment is a room with a computer. The patient target group is adult patients who have an injury or disability diagnosed previously. There are no other considerations for the intended patient population.
PeekMed web is a system designed to help healthcare professionals carry out pre-operative planning for several surgical procedures, based on their imported patients' imaging studies. Experience in usage and a clinical assessment are necessary for the proper use of the system in the revision and approval of the output of the planning.
The multi-platform system works with a database of digital representations related to surgical materials supplied by their manufacturers.
As the PeekMed web is capable of representing medical images in a 2D or 3D environment, performing relevant measurements on those images, and also capable of adding templates, it can then provide a total overview of the surgery. Being software, it does not interact with any part of the body of the user and/or patient.
Here's a breakdown of the acceptance criteria and the study proving the device's performance, based on the provided FDA 510(k) clearance letter:
1. Table of Acceptance Criteria and Reported Device Performance
The document does not explicitly state the "reported device performance" against each acceptance criterion. It only states that the comparison of efficacy results met the acceptance criteria. Thus, the "Reported Device Performance" column reflects this qualitative statement.
| ML Model | Acceptance Criteria | Reported Device Performance |
|---|---|---|
| Segmentation | DICE is no less than 90%HD-95 is no more than 8STD DICE is between +/- 10%Precision is more than 85%Recall is more than 90% | Comparison of the efficacy results using the testing and external validation datasets against the predefined ground truth met the acceptance criteria for ML model performance, demonstrating substantial equivalence. |
| Landmarking | MRE is no more than 7mmSTD MRE is between +/- 5mm | Comparison of the efficacy results using the testing and external validation datasets against the predefined ground truth met the acceptance criteria for ML model performance, demonstrating substantial equivalence. |
| Classification | Accuracy is no less than 90%.Precision is no less than 85%Recall is no less than 90%F1 score is no less than 90% | Comparison of the efficacy results using the testing and external validation datasets against the predefined ground truth met the acceptance criteria for ML model performance, demonstrating substantial equivalence. |
| Detection | MAP is no less than 90%.Precision is no less than 85%Recall is no less than 90% | Comparison of the efficacy results using the testing and external validation datasets against the predefined ground truth met the acceptance criteria for ML model performance, demonstrating substantial equivalence. |
Note: The document only confirms that the performance met the criteria, not the exact values achieved.
2. Sample Sizes and Data Provenance
- Test Set Sample Sizes (External Validation Data):
- Segmentation ML model: 402 unique datasets
- Landmarking ML model: 367 unique datasets
- Classification ML model: 347 unique datasets
- Detection ML model: 198 unique datasets
- Data Provenance: The document states that ML models were developed with datasets from "multiple sites." It doesn't specify the country of origin but mentions that the development, training, and testing data, as well as the external validation data, were designed to cover the intended use population while ensuring variety and diverse patient characteristics. It implies the data is retrospective as it refers to "datasets" collected for model development and validation.
3. Number of Experts and Qualifications for Ground Truth
The document does not specify the number of experts or their qualifications for establishing the ground truth for the test set. It only mentions that the "External validation...was collected independently of the development data to prevent bias, ensuring the reliability of the results. For the external validation, a fully independent dataset, labeled by a separate team, was employed..." The qualifications of this "separate team" are not detailed.
4. Adjudication Method for the Test Set
The document does not explicitly describe an adjudication method (e.g., 2+1, 3+1). It states that the external validation dataset was "labeled by a separate team." This suggests a single labeling event by that team, rather than an explicit multi-reader adjudication process.
5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study
No, an MRMC comparative effectiveness study comparing human readers with AI assistance versus without AI assistance was not reported. The study focuses purely on the standalone performance of the ML models against established ground truth.
6. Standalone (Algorithm Only) Performance
Yes, a standalone performance evaluation of the algorithm (ML models) was done. The performance metrics (DICE, HD-95, MRE, Accuracy, Precision, Recall, F1 score, MAP) and acceptance criteria are applied directly to the output of the ML models.
7. Type of Ground Truth Used
The type of ground truth used is referred to as "predefined ground truth" which was established through a "truthing process" and labeled by a "separate team." While it doesn't explicitly state "expert consensus" or "pathology," for image segmentation and landmarking in medical imaging, ground truth is typically established by trained human experts (e.g., radiologists, orthopedic surgeons, or technicians with specific training) through manual annotation or expert review, which often involves some form of consensus. For classification and detection tasks, ground truth similarly relies on definitive labels provided by experts or established from patient records/outcomes, though the document does not elaborate on the specific method for each ML model type.
8. Sample Size for the Training Set
The training set comprised 80% of the total datasets available for ML model development, which included:
- Total X-rays: 2852
- Total CT scans: 1903
- Total MRIs: 151
Therefore, the approximate sample sizes for the training set are:
- X-rays: 0.80 * 2852 = 2281.6 (approx. 2282)
- CT scans: 0.80 * 1903 = 1522.4 (approx. 1522)
- MRIs: 0.80 * 151 = 120.8 (approx. 121)
9. How the Ground Truth for the Training Set Was Established
The document states, "ML models were developed with datasets from multiple sites... We trained the ML models with 80% of the datasets, developed with 10%, and tested with the remaining 10%." It also mentions that "the validation dataset...has never been used for the algorithm training or for tuning the algorithm, and leakage between development and validation data sets did not occur."
While the process for the training set's ground truth is not explicitly detailed in the same way as the external validation "labeled by a separate team," it is implicitly established through the "development" process. Typically, for ML models of this nature, ground truth for training data would also be established through manual annotation by qualified personnel (e.g., clinicians, trained annotators) following established protocols. The document's emphasis on data independence for external validation suggests that the development/training data was also accurately labeled for its purpose.
U.S. Food & Drug Administration 510(k) Clearance Letter
Page 1
U.S. Food & Drug Administration
10903 New Hampshire Avenue
Silver Spring, MD 20993
www.fda.gov
Doc ID # 04017.08.00
July 14, 2025
Peek Health, S.A.
Sara Silva
Chief Quality Officer (CQO)
Centro de Negócios Ideia Atlântico, Rua Padres Carmelitas
Braga, 4719-005
Portugal
Re: K251096
Trade/Device Name: PeekMed web
Regulation Number: 21 CFR 892.2050
Regulation Name: Medical Image Management And Processing System
Regulatory Class: Class II
Product Code: QIH, LLZ
Dated: June 16, 2025
Received: June 16, 2025
Dear Sara Silva:
We have reviewed your section 510(k) premarket notification of intent to market the device referenced above and have determined the device is substantially equivalent (for the indications for use stated in the enclosure) to legally marketed predicate devices marketed in interstate commerce prior to May 28, 1976, the enactment date of the Medical Device Amendments, or to devices that have been reclassified in accordance with the provisions of the Federal Food, Drug, and Cosmetic Act (the Act) that do not require approval of a premarket approval application (PMA). You may, therefore, market the device, subject to the general controls provisions of the Act. Although this letter refers to your product as a device, please be aware that some cleared products may instead be combination products. The 510(k) Premarket Notification Database available at https://www.accessdata.fda.gov/scripts/cdrh/cfdocs/cfpmn/pmn.cfm identifies combination product submissions. The general controls provisions of the Act include requirements for annual registration, listing of devices, good manufacturing practice, labeling, and prohibitions against misbranding and adulteration. Please note: CDRH does not evaluate information related to contract liability warranties. We remind you, however, that device labeling must be truthful and not misleading.
If your device is classified (see above) into either class II (Special Controls) or class III (PMA), it may be subject to additional controls. Existing major regulations affecting your device can be found in the Code of Federal Regulations, Title 21, Parts 800 to 898. In addition, FDA may publish further announcements concerning your device in the Federal Register.
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Additional information about changes that may require a new premarket notification are provided in the FDA guidance documents entitled "Deciding When to Submit a 510(k) for a Change to an Existing Device" (https://www.fda.gov/media/99812/download) and "Deciding When to Submit a 510(k) for a Software Change to an Existing Device" (https://www.fda.gov/media/99785/download).
Your device is also subject to, among other requirements, the Quality System (QS) regulation (21 CFR Part 820), which includes, but is not limited to, 21 CFR 820.30, Design controls; 21 CFR 820.90, Nonconforming product; and 21 CFR 820.100, Corrective and preventive action. Please note that regardless of whether a change requires premarket review, the QS regulation requires device manufacturers to review and approve changes to device design and production (21 CFR 820.30 and 21 CFR 820.70) and document changes and approvals in the device master record (21 CFR 820.181).
Please be advised that FDA's issuance of a substantial equivalence determination does not mean that FDA has made a determination that your device complies with other requirements of the Act or any Federal statutes and regulations administered by other Federal agencies. You must comply with all the Act's requirements, including, but not limited to: registration and listing (21 CFR Part 807); labeling (21 CFR Part 801); medical device reporting (reporting of medical device-related adverse events) (21 CFR Part 803) for devices or postmarketing safety reporting (21 CFR Part 4, Subpart B) for combination products (see https://www.fda.gov/combination-products/guidance-regulatory-information/postmarketing-safety-reporting-combination-products); good manufacturing practice requirements as set forth in the quality systems (QS) regulation (21 CFR Part 820) for devices or current good manufacturing practices (21 CFR Part 4, Subpart A) for combination products; and, if applicable, the electronic product radiation control provisions (Sections 531-542 of the Act); 21 CFR Parts 1000-1050.
All medical devices, including Class I and unclassified devices and combination product device constituent parts are required to be in compliance with the final Unique Device Identification System rule ("UDI Rule"). The UDI Rule requires, among other things, that a device bear a unique device identifier (UDI) on its label and package (21 CFR 801.20(a)) unless an exception or alternative applies (21 CFR 801.20(b)) and that the dates on the device label be formatted in accordance with 21 CFR 801.18. The UDI Rule (21 CFR 830.300(a) and 830.320(b)) also requires that certain information be submitted to the Global Unique Device Identification Database (GUDID) (21 CFR Part 830 Subpart E). For additional information on these requirements, please see the UDI System webpage at https://www.fda.gov/medical-devices/device-advice-comprehensive-regulatory-assistance/unique-device-identification-system-udi-system.
Also, please note the regulation entitled, "Misbranding by reference to premarket notification" (21 CFR 807.97). For questions regarding the reporting of adverse events under the MDR regulation (21 CFR Part 803), please go to https://www.fda.gov/medical-devices/medical-device-safety/medical-device-reporting-mdr-how-report-medical-device-problems.
For comprehensive regulatory information about medical devices and radiation-emitting products, including information about labeling regulations, please see Device Advice (https://www.fda.gov/medical-devices/device-advice-comprehensive-regulatory-assistance) and CDRH Learn (https://www.fda.gov/training-and-continuing-education/cdrh-learn). Additionally, you may contact the Division of Industry and Consumer Education (DICE) to ask a question about a specific regulatory topic. See
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the DICE website (https://www.fda.gov/medical-devices/device-advice-comprehensive-regulatory-assistance/contact-us-division-industry-and-consumer-education-dice) for more information or contact DICE by email (DICE@fda.hhs.gov) or phone (1-800-638-2041 or 301-796-7100).
Sincerely,
for
Jessica Lamb, Ph.D.
Assistant Director
DHT8B: Division of Radiological Imaging
Devices and Electronic Products
OHT8: Office of Radiological Health
Office of Product Evaluation and Quality
Center for Devices and Radiological Health
Enclosure
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Indications for Use
Please type in the marketing application/submission number, if it is known. This textbox will be left blank for original applications/submissions.
Please provide the device trade name(s).
PeekMed web
Please provide your Indications for Use below.
PeekMed web is a system designed to help healthcare professionals carry out pre-operative planning for several surgical procedures, based on their imported patients' imaging studies. Experience in usage and a clinical assessment is necessary for the proper use of the system in the revision and approval of the output of the planning. The multi-platform system works with a database of digital representations related to surgical materials supplied by their manufacturers.
This medical device consists of a decision support tool for qualified healthcare professionals to quickly and efficiently perform the pre-operative planning for several surgical procedures, using medical imaging with the additional capability of planning the 2D or 3D environment. The system is designed for the medical specialties within surgery and no specific use environment is mandatory, whereas the typical use environment is a room with a computer. The patient target group is adult patients who have an injury or disability diagnosed previously. There are no other considerations for the intended patient population.
Please select the types of uses (select one or both, as applicable).
☑ Prescription Use (Part 21 CFR 801 Subpart D)
☐ Over-The-Counter Use (21 CFR 801 Subpart C)
PeekMed web
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510(k) summary
K251096
This 510(k) summary of safety and effectiveness is being submitted in accordance with the requirements of 21 CFR 807.92.
1. Submitter
Peek Health, S.A.
Centro de Negócios Ideia Atlântico
Rua Padres Carmelitas
4719-005 Braga
Portugal
Contact Person: Sara Silva
Chief Quality Officer (CQO)
Email: regulatory@peekmed.com
Office number: + 351 253 128 941
Date Summary Prepared: July 14, 2025
2. Device
2.1 PeekMed web
Trade Name: PeekMed web
Common or Usual Name: Medical image management and processing system
Classification Name: System, Image Processing, Radiological (21 C.F.R. § 892.2050)
Regulatory Class: Class II
Product Code: QIH, LLZ
510(k) Summary - PeekMed web
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3. Legally Marketed Predicate Device
3.1 PeekMed web
| 510(k) | Product Name | Clearance Date |
|---|---|---|
| K250042 | PeekMed web | March 19, 2025 |
4. Device Description Summary
PeekMed web is a system designed to help healthcare professionals carry out pre-operative planning for several surgical procedures, based on their imported patients' imaging studies. Experience in usage and a clinical assessment are necessary for the proper use of the system in the revision and approval of the output of the planning.
The multi-platform system works with a database of digital representations related to surgical materials supplied by their manufacturers.
As the PeekMed web is capable of representing medical images in a 2D or 3D environment, performing relevant measurements on those images, and also capable of adding templates, it can then provide a total overview of the surgery. Being software, it does not interact with any part of the body of the user and/or patient.
5. Intended Use/Indications for Use
PeekMed web is a system designed to help healthcare professionals carry out pre-operative planning for several surgical procedures, based on their imported patients' imaging studies. Experience in usage and a clinical assessment are necessary for the proper use of the system in the revision and approval of the output of the planning.
The multi-platform system works with a database of digital representations related to surgical materials supplied by their manufacturers.
This medical device consists of a decision support tool for qualified healthcare professionals to quickly and efficiently perform the pre-operative planning for several surgical procedures, using medical imaging with the additional capability of planning the 2D or 3D environment. The system is designed for the medical specialties within surgery, and no specific use environment is mandatory, whereas the typical use environment is a room with a computer. The patient target group is adult patients who have an injury or disability diagnosed previously. There are no other considerations for the intended patient population.
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3. Legally Marketed Predicate Device
3.1 PeekMed web
| 510(k) | Product Name | Clearance Date |
|---|---|---|
| K250042 | PeekMed web | March 19, 2025 |
4. Device Description Summary
PeekMed web is a system designed to help healthcare professionals carry out pre-operative planning for several surgical procedures, based on their imported patients' imaging studies. Experience in usage and a clinical assessment are necessary for the proper use of the system in the revision and approval of the output of the planning.
The multi-platform system works with a database of digital representations related to surgical materials supplied by their manufacturers.
As the PeekMed web is capable of representing medical images in a 2D or 3D environment, performing relevant measurements on those images, and also capable of adding templates, it can then provide a total overview of the surgery. Being software, it does not interact with any part of the body of the user and/or patient.
5. Intended Use/Indications for Use
PeekMed web is a system designed to help healthcare professionals carry out pre-operative planning for several surgical procedures, based on their imported patients' imaging studies. Experience in usage and a clinical assessment are necessary for the proper use of the system in the revision and approval of the output of the planning.
The multi-platform system works with a database of digital representations related to surgical materials supplied by their manufacturers.
This medical device consists of a decision support tool for qualified healthcare professionals to quickly and efficiently perform the pre-operative planning for several surgical procedures, using medical imaging with the additional capability of planning the 2D or 3D environment. The system is designed for the medical specialties within surgery, and no specific use environment is mandatory, whereas the typical use environment is a room with a computer. The patient target group is adult patients who have an injury or disability diagnosed previously. There are no other considerations for the intended patient population.
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5.1. Contraindications
No contraindications specific to this device.
5.2 Indications for Use Comparison
There are NO differences between the indications for use of this device and its predicate.
6. Technological Comparison to Predicate
PeekMed web was compared to its respective predicate device in intended use, indications for use, design, function, and technology, and it was demonstrated that they are substantially equivalent. Any technological differences within this 510(k), between the subject device and the predicate device, do not impact substantial equivalence, or safety and effectiveness.
The subject device and its predicate are both medical software that allow healthcare professionals to perform orthopedic pre-surgical planning efficiently in the musculoskeletal system of adults in a healthcare environment, therefore sharing the same intended use, intended user, and intended patient population. To properly and fully use both devices, clinical judgment and experience are mandatory.
Both devices have the same workflows, use requirements (e.g., internet connection, output validation), and planning features (e.g., model representation, digital overlap of prosthetic material, possible 2D and 3D environments). Both devices generate a final report of the planning, which consists of the selected images with templates, measurements, and textual information describing the patient and/or the surgical procedure to be performed.
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Table 1: Summary of Predicate and Subject Device Characteristics to Demonstrate Substantial Equivalence
| Characteristic | PeekMed web Predicate device K250042 | PeekMed web Subject device | Substantially Equivalent? | Justification and rationale |
|---|---|---|---|---|
| Product Code | QIH, LLZ | QIH, LLZ | Yes | --- |
| Regulation Number | 21 CFR 892.2050 | 21 CFR 892.2050 | Yes | --- |
| Regulation Name | Medical Image Management And Processing System | Medical Image Management And Processing System | Yes | --- |
| Intended use/Indications for use | PeekMed web is a system designed to help healthcare professionals carry out pre-operative planning for several surgical procedures, based on their imported patients' imaging studies. Experience in usage and a clinical assessment is necessary for the proper use of the system in the revision and approval of the output of the planning. The multi-platform system works with a database of digital representations related to surgical materials supplied by their manufacturers. This medical device consists of a decision support tool for qualified healthcare professionals to quickly and efficiently perform the pre-operative planning for several surgical procedures, using medical imaging with the additional | PeekMed web is a system designed to help healthcare professionals carry out pre-operative planning for several surgical procedures, based on their imported patients' imaging studies. Experience in usage and a clinical assessment is necessary for the proper use of the system in the revision and approval of the output of the planning. The multi-platform system works with a database of digital representations related to surgical materials supplied by their manufacturers. This medical device consists of a decision support tool for qualified healthcare professionals to quickly and efficiently perform the pre-operative planning for several surgical procedures, using medical imaging with the additional capability of | Yes | --- |
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| Characteristic | PeekMed web Predicate device K250042 | PeekMed web Subject device | Substantially Equivalent? | Justification and rationale |
|---|---|---|---|---|
| capability of planning the 2D or 3D environment. The system is designed for the medical specialties within surgery and no specific use environment is mandatory, whereas the typical use environment is a room with a computer. The patient target group is adult patients who have an injury or disability diagnosed previously. There are no other considerations for the intended patient population. | planning the 2D or 3D environment. The system is designed for the medical specialties within surgery and no specific use environment is mandatory, whereas the typical use environment is a room with a computer. The patient target group is adult patients who have an injury or disability diagnosed previously. There are no other considerations for the intended patient population. | |||
| Contraindications | No contraindications specific to this device. | No contraindications specific to this device. | Yes | --- |
| Clinical purpose | PeekMed web allows the surgeon to perform orthopedic pre-surgical planning efficiently in the musculoskeletal system (e.g., Hip procedures, Knee procedures) | PeekMed web allows the surgeon to perform orthopedic pre-surgical planning efficiently in the musculoskeletal system (e.g., Hip procedures, Knee procedures) | Yes | --- |
| Anatomical regions | PeekMed web allows the surgeon to perform the pre-surgical planning efficiently in the following anatomical regions: - Hip - Knee - Upper limb - Foot | PeekMed web allows the surgeon to perform the pre-surgical planning efficiently in the following anatomical regions: - Hip - Knee - Upper limb - Foot | Yes | --- |
| Patient Population | Adults | Adults | Yes | --- |
| End users | Healthcare Professionals | Healthcare Professionals | Yes | --- |
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| Characteristic | PeekMed web Predicate device K250042 | PeekMed web Subject device | Substantially Equivalent? | Justification and rationale |
|---|---|---|---|---|
| Contraindications | No contraindications specific to this device. | No contraindications specific to this device. | Yes | --- |
| Clinical purpose | PeekMed web allows the surgeon to perform orthopedic pre-surgical planning efficiently in the musculoskeletal system (e.g., Hip procedures, Knee procedures) | PeekMed web allows the surgeon to perform orthopedic pre-surgical planning efficiently in the musculoskeletal system (e.g., Hip procedures, Knee procedures) | Yes | --- |
| Anatomical regions | PeekMed web allows the surgeon to perform the pre-surgical planning efficiently in the following anatomical regions: - Hip - Knee - Upper limb - Foot | PeekMed web allows the surgeon to perform the pre-surgical planning efficiently in the following anatomical regions: - Hip - Knee - Upper limb - Foot | Yes | --- |
| Patient Population | Adults | Adults | Yes | --- |
| End users | Healthcare Professionals | Healthcare Professionals | Yes | --- |
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| Characteristic | PeekMed web Predicate device K250042 | PeekMed web Subject device | Substantially Equivalent? | Justification and rationale |
|---|---|---|---|---|
| Device availability | Software is cloud-based (not installable) and can be displayed on any personal device or workstation that can run on a web browser | Software is cloud-based (not installable) and can be displayed on any personal device or workstation that can run on a web browser | Yes | --- |
| Software Architecture | Distributed system (cloud-based). This distributed system is a combination of software modules placed on servers that are able to communicate with each other. | Distributed system (cloud-based). This distributed system is a combination of software modules placed on servers that are able to communicate with each other. | Yes | --- |
| Workflow | The workflow is as follows: Import case images, configure images, identify the case, pre-surgical planning, and export the case. | The workflow is as follows: Import case images, configure images, identify the case, pre-surgical planning, and export the case. | Yes | --- |
| Internet connection | Required | Required | Yes | --- |
| Images source | Receives medical images from various sources | Receives medical images from various sources | Yes | --- |
| Data processing | The software processes data to provide an overlap and dimensioning of digital representations of the prosthetic material | The software processes data to provide an overlap and dimensioning of digital representations of the prosthetic material | Yes | --- |
| Digital overlap of templates | Allows the overlap of models and the intersection of the models | Allows the overlap of models and the intersection of the models | Yes | --- |
| Interactive model positioning | Yes | Yes | Yes | --- |
| Interactive model dimensioning | Yes | Yes | Yes | --- |
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| Characteristic | PeekMed web Predicate device K250042 | PeekMed web Subject device | Substantially Equivalent? | Justification and rationale |
|---|---|---|---|---|
| Model rotation | Yes | Yes | Yes | --- |
| Support for digital prosthetic materials provided by the manufacturers | Yes | Yes | Yes | --- |
| Pre-surgical planning | Yes | Yes | Yes | --- |
| Type of pre-surgical planning | Automatic or Manual | Automatic or Manual | Yes | --- |
| Contact with the patient | No | No | Yes | --- |
| Control of life supporting devices | No | No | Yes | --- |
| Human intervention for image interpretation | Yes | Yes | Yes | --- |
| Ability to add additional modules when available | Yes | Yes | Yes | --- |
| Automatic bone segmentation | Yes - Hip (X-ray and CT scan) - Knee (X-ray and CT scan) | Yes - Hip (X-ray and CT scan) - Knee (X-ray, CT scan, and MRI) | Yes | The subject device includes a new ML model variant for segmentation. Both devices allow planning for the |
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| Characteristic | PeekMed web Predicate device K250042 | PeekMed web Subject device | Substantially Equivalent? | Justification and rationale |
|---|---|---|---|---|
| - Upper limb (CT scan) - Foot (X-ray and CT scan) | - Upper limb (CT scan) - Foot (X-ray and CT scan) | knee region with X-rays and CT scans, but the subject device also allows with MRI. This does not constitute an intended purpose update, nor does it raise questions of safety and performance, since the development, verification, validation, and deployment processes are the same for both devices. | ||
| Type of landmarking | Automatic or Manual - Hip - Knee - Upper limb - Foot | Automatic or Manual - Hip - Knee - Upper limb - Foot | Yes | --- |
PeekMed web shares the same intended use, indications for use, end users, and has the patient population, overall technical and functional capabilities, and therefore is substantially equivalent to the predicate device. The subject device has the same design and function as the predicate device for the modes of operation and use. The subject device includes a new ML variant.
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7. Performance Data
Nonclinical performance testing performed on the subject device, PeekMed web, supports substantial equivalence to the predicate device. The following testing was performed on the subject device:
A. Verification activities were conducted to ensure that features were implemented following the requirements and covering the acceptance criteria.
B. ML models incorporated into the PeekMed web were also developed, trained, tested, and externally validated for their performance according to the internal procedures.
- A dedicated validation dataset containing different data from the ML development dataset was used. Specifically, the validation dataset was not a sampling of the development dataset, has never been used for the algorithm training or for tuning the algorithm, and leakage between development and validation data sets did not occur.
Training, development, testing and external validation data information
ML models were developed with datasets from multiple sites in a total of 2852 X-rays datasets, 1903 CT scans, and 151 MRIs. We trained the ML models with 80% of the datasets, developed with 10%, and tested with the remaining 10%. External validation is performed by sample size with a total unique dataset, for Segmentation ML model: 402; Landmarking ML model: 367; Classification ML model: 347; and Detection ML model: 198. This comprehensive dataset was designed to cover the intended use population while ensuring a variety of data, maintaining diverse patient characteristics.
Subgroup definition (generalizability)
Datasets were divided according to the subgroups listed below:
- Demographics
- Patient Sex
- Patient Age
- Equipment and Protocols for Image Collection
- Institution Name
- Manufacturer
- Manufacturer Model Name
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Acceptance criteria
The acceptance criteria for each ML model are shown in the following table:
| ML model | Acceptance Criteria |
|---|---|
| Segmentation | DICE is no less than 90%HD-95 is no more than 8STD DICE is between +/- 10%Precision is more than 85%Recall is more than 90% |
| Landmarking | MRE is no more than 7mmSTD MRE is between +/- 5mm |
| Classification | Accuracy is no less than 90%.Precision is no less than 85%Recall is no less than 90%F1 score is no less than 90% |
| Detection | MAP is no less than 90%.Precision is no less than 85%Recall is no less than 90% |
Reference Standard ("Truthing" Process)
Comparison of the efficacy results of the Segmentation ML model using the testing and external validation datasets against the predefined ground truth met the acceptance criteria for ML model performance, demonstrating the substantial equivalence of the subject device to its predicate.
Comparison of the efficacy results of the Landmarking ML model using the testing and external validation datasets against the predefined ground truth met the acceptance criteria for ML model performance, demonstrating the substantial equivalence of the subject device to its predicate.
Comparison of the efficacy results of the Classification ML model using the testing and external validation datasets against the predefined ground truth met
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the acceptance criteria for ML model performance, demonstrating the substantial equivalence of the subject device to its predicate.
Comparison of the efficacy results of the Detection ML model using the testing and external validation datasets against the predefined ground truth met the acceptance criteria for ML model performance, demonstrating the substantial equivalence of the subject device to its predicate.
Independence of Training and External Validation Data
External validation datasets were collected independently of the development data to prevent bias, ensuring the reliability of the results. For the external validation, a fully independent dataset, labeled by a separate team, was employed to provide an accurate assessment of the model's performance, over the whole population and for each sub-group mentioned before, to prove that it generalizes well to unseen, real-world data. All the testing and external validation performed indicate acceptable performance of the ML models for their intended population.
C. Validation tests were performed internally before the release to the market by qualified personnel, in an environment simulating the real end-user environment. This validation follows a pre-defined test script document, according to the tests defined in the test cases management software.
Nonclinical performance testing allowed us to understand that there are no related problems in the subject device. Furthermore, these tests will be repeated and updated when appropriate to ensure that the software is always properly validated, making it possible to understand in which version the problems arise and in which they are solved. Consequently, any problem that may appear in a given PeekMed web version will be identified and can be solved in subsequent versions, as all steps are traceable. All anatomical areas were tested, as well as other main areas of the software, such as the planning final report, and saved planning, ML models, among others.
After these successful validation tests, it is possible to deem the subject device, PeekMed web, as substantially equivalent to its predicate device.
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9. Conclusion
Based on the information provided in this 510(k) submission, it was determined that the subject device, PeekMed web, is substantially equivalent to the legally marketed predicate device concerning indications for use, intended use, design, technology, and performance.
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§ 892.2050 Medical image management and processing system.
(a)
Identification. A medical image management and processing system is a device that provides one or more capabilities relating to the review and digital processing of medical images for the purposes of interpretation by a trained practitioner of disease detection, diagnosis, or patient management. The software components may provide advanced or complex image processing functions for image manipulation, enhancement, or quantification that are intended for use in the interpretation and analysis of medical images. Advanced image manipulation functions may include image segmentation, multimodality image registration, or 3D visualization. Complex quantitative functions may include semi-automated measurements or time-series measurements.(b)
Classification. Class II (special controls; voluntary standards—Digital Imaging and Communications in Medicine (DICOM) Std., Joint Photographic Experts Group (JPEG) Std., Society of Motion Picture and Television Engineers (SMPTE) Test Pattern).