(15 days)
Philips Orthopaedic Applications is a suite of software applications designed to assist medical professionals such as orthopaedic surgeons, physicians and radiologists in planning and evaluating orthopaedic procedures using medical images.
The applications are intended to view and manipulate 2D and 3D medical images; to calibrate and make length, angle and area measurements on such images; to represent and manipulate surgical planning templates overlaid on such images; to plan and simulate the effect of treatments by transforming such images; and to print and store the results of these measurements and simulations.
The Philips Orthopaedic Applications software runs on "off the shelt" standard PC components using a Microsoft Operating System. It can be used as stand-alone SW applications or as a Plug-in on advanced image processing workstations or review workstations (PACS).
These applications provide digital alternatives for the tools medical specialists are used to work with when using conventional images printed on film: callipers, pencil, transparent sheets, scissors and tape. The software tools transform these conventional tools for working with digital images on a computer display.
The provided text is a 510(k) summary for the Philips Orthopaedic Applications, which is a software suite. This document focuses on establishing substantial equivalence to predicate devices rather than providing a detailed study proving performance against specific acceptance criteria.
Therefore, many of the requested details about a study evaluating the device's performance against acceptance criteria are not present in the provided text. The document primarily focuses on the device's description, intended use, and substantial equivalence to existing devices. It does not contain information about clinical trials, performance metrics, ground truth establishment, or sample sizes related to a performance study.
Based on the provided text, here's what can be extracted and what is missing:
1. A table of acceptance criteria and the reported device performance
- Acceptance Criteria: Not explicitly stated as quantifiable metrics. The acceptance is based on demonstrating substantial equivalence to predicate devices in terms of functionality and safety.
- Reported Device Performance: Not reported in terms of specific performance metrics (e.g., accuracy, sensitivity, specificity). The document states that the device "does not introduce new indications for use, nor does the use of the device result in any new potential hazard."
2. Sample sized used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective)
- Not provided. The document does not describe a test set or any performance study data.
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)
- Not provided. Ground truth establishment for a performance study is not discussed.
4. Adjudication method (e.g. 2+1, 3+1, none) for the test set
- Not provided. Adjudication methods are not discussed as no performance study is detailed.
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
- Not provided. The document does not mention an MRMC study or any AI assistance, as the device is a set of software tools for viewing, manipulating, and measuring medical images, and planning procedures. It's not described as an AI-driven diagnostic aid.
6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done
- Not fully applicable in the context of this document. The device is described as software applications to assist medical professionals, implying a human-in-the-loop interaction rather than a standalone algorithmic diagnosis. No standalone performance study is detailed.
7. The type of ground truth used (expert consensus, pathology, outcomes data, etc.)
- Not provided. As no performance study is detailed, the type of ground truth is not mentioned.
8. The sample size for the training set
- Not provided. Training data for algorithms is not discussed. This product is described as software tools, not a machine learning model that requires a training set in the typical sense.
9. How the ground truth for the training set was established
- Not provided. Ground truth establishment for a training set is not discussed.
Summary of what the document focuses on instead:
The Philips Orthopaedic Applications 510(k) summary focuses on demonstrating "substantial equivalence" to predicate devices (Agfa IMPAX® OT3000 Orthopedic Workstation and Sectra Orthopedic Package). This means the FDA concluded that the new device is as safe and effective as a legally marketed device and does not raise new questions of safety or effectiveness. The core argument for substantial equivalence is based on:
- Device Description: The software runs on standard PC components and can be standalone or a plug-in.
- Intended Use: To assist medical professionals in planning and evaluating orthopedic procedures using medical images through viewing, manipulation, calibration, measurement (length, angle, area), template overlay, and simulation.
- Safety and Effectiveness: Complies with ACR/NEMA DICOM standard, and importantly, "does not introduce new indications for use, nor does the use of the device result in any new potential hazard."
Essentially, the "study that proves the device meets the acceptance criteria" in this context is the submission and FDA's review of the 510(k), where the acceptance criteria are alignment with the predicate devices and the proof is the demonstrated lack of new safety/effectiveness concerns or new indications for use compared to those predicates. No specific performance metrics or clinical study results are detailed in this summary.
§ 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).