(155 days)
Simpleware ScanlP Medical is intended for use as a software interface and image segmentation system for the transfer of medical imaging information to an output file. It is also intended as pre-operative software for diagnostic and surgical planning.
For these purposes, output files can also be used for the fabrication of physical replicas using traditive manufacturing methods. The physical replicas can be used for diagnostic purposes in the field of orthopedic, maxillofacial and cardiovascular applications.
The software is intended to be used in conjunction with other diagnostic tools and expert clinical judgment.
Simpleware ScanIP Medical is image processing software that enables users to import, visualize, and segment medical images, and export digital 3D models. These models can be used in the software for pre-surgical tasks, and can also be used to produce output files suitable for additive manufacturing (3D printing). Simpleware ScanIP Medical also has functionality for transferring from and to third-party software packages.
Simpleware ScanIP Medical is a modular product, including the following functionalities:
- Import of medical images in various formats
- Transferring files from and to computer-aided design (CAD) software packages
- Image filtering and segmentation tools
- 2D and 3D visualization of image data and CAD drawings
- Analysis, measurements, and statistics from 3D image data and CAD drawings
- Generating and exporting meshes to Finite Element (FE) packages.
- Generating and exporting models to CAD software
- Support for scripting in a number of programming languages
The provided text is a 510(k) summary for the device "Simpleware ScanIP Medical". It describes the device, its intended use, and compares it to a predicate device. However, it does not contain specific acceptance criteria, detailed study designs with sample sizes for test sets, expert qualifications, or adjudication methods for establishing ground truth. It states that "Validation was carried out for the workflow of going from 3D image to printed model, demonstrating that the anatomical models for cardiovascular, orthopedic, and maxillofacial applications can be printed accurately when using compatible 3D printers." but does not provide the specifics of this validation study in the format you requested.
The document focuses on demonstrating substantial equivalence to a predicate device (Simpleware ScanIP, K142779) through non-clinical bench testing and technological comparisons, rather than providing detailed performance studies with acceptance criteria for a new clinical efficacy claim.
Therefore, many of your requested items cannot be extracted from this document.
Here's an attempt to answer what can be gathered:
1. Table of Acceptance Criteria and Reported Device Performance
This information is not explicitly provided in the document in a quantifiable format with specific acceptance criteria and reported performance metrics. The document broadly states: "Validation of the subject device shows it to be equivalent in performance to the predicate device" and "Non-clinical bench-testing results demonstrate that the subject device is as safe, effective, and functional as the predicate device." It also mentions "demonstrating that the anatomical models for cardiovascular, orthopedic, and maxillofacial applications can be printed accurately when using compatible 3D printers." without providing specific accuracy metrics or thresholds.
| Acceptance Criteria | Reported Device Performance |
|---|---|
| Not explicitly stated and quantified in the document. | Not explicitly stated and quantified in the document. |
2. Sample size used for the test set and the data provenance
The document mentions "Validation was carried out for the workflow of going from 3D image to printed model, demonstrating that the anatomical models for cardiovascular, orthopedic, and maxillofacial applications can be printed accurately when using compatible 3D printers." However, it does not specify the sample size for this validation. The provenance of the data (e.g., country of origin, retrospective/prospective) is also not mentioned.
3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts
This information is not provided in the document. The document describes non-clinical bench testing, not studies involving expert interpretation for ground truth.
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
No, a multi-reader multi-case (MRMC) comparative effectiveness study was not done. The document explicitly states: "No clinical tests were conducted to determine substantial equivalence." The device is intended as a software interface and image segmentation system, and pre-operative software, not as an AI-powered diagnostic aid that improves human reader performance.
6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done
The document describes "bench-testing" and "validation for the workflow of going from 3D image to printed model". This implies a standalone assessment of the software's ability to produce accurate models from images. However, it does not provide specific metrics or a detailed study design for this standalone performance.
7. The type of ground truth used (expert consensus, pathology, outcomes data, etc.)
For the "validation for the workflow of going from 3D image to printed model," the ground truth likely refers to the "accurate" measurements or representations in the physical replicas compared to the original imaging data. The method for establishing this "accuracy" (e.g., precise measurements of physical models, comparison to established standards) is not detailed but it's reasonable to infer a comparison against the input image data or engineering specifications, rather than clinical ground truth like pathology.
8. The sample size for the training set
This document describes a medical device software (Simpleware ScanIP Medical) which is a tool for image processing and segmentation, not a machine learning or AI model that typically requires a "training set" in the context of deep learning. Therefore, the concept of a training set for this type of device is not applicable and is not mentioned in the document.
9. How the ground truth for the training set was established
As the device described is not an AI/ML model with a typical "training set," this question is not applicable.
§ 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).