(98 days)
The AlgoMedica PixelShine System is intended for networking, communication, processing and enhancement of CT images in DICOM format. It is specifically indicated for assisting professional radiologists and specialists in reaching their own diagnosis. The device processing is not effective for lesion, mass or abnormalities of sizes less than 3.0 mm The AlgoMedica PixelShine is not intended for use with or for diagnostic interpretation of mammography images.
PixelShine is a medical imaging application that can receive, transfer and perform noise reduction of CT DICOM images over a user network. Received images are processed by the PixelShine to reduce noise, thereby enhancing image quality.
The provided text, a 510(k) summary for the AlgoMedica PixelShine, does not contain the specific details required to fully address all parts of your request regarding acceptance criteria and a detailed study proving device performance. The document focuses on regulatory clearance by demonstrating substantial equivalence to a predicate device, rather than providing the full technical report of a performance study with quantitative results and specific ground truth methodologies.
However, I can extract the available information and highlight what is missing based on your request.
Here's a breakdown of the information that is available and what is not:
1. A table of acceptance criteria and the reported device performance
- Acceptance Criteria (Implicit/General): The document states that "The results of the performance testing demonstrate the safety and effectiveness of the PixelShine." It also mentions "The device processing is not effective for lesion, mass or abnormalities of sizes less than 3.0 mm," which can be interpreted as a functional limitation or an implicit performance boundary, rather than an acceptance criterion for the algorithm's performance on such abnormalities.
- Reported Device Performance: The document does not provide quantitative performance metrics (e.g., sensitivity, specificity, AUC, noise reduction percentages, CR rates) from a study against specific acceptance criteria. It only states that "All of the testing was performed with the use of an approved testing protocol" and that "All of the testing was performed with satisfactory results."
Missing Information: Specific numerical acceptance criteria for metrics like noise reduction, contrast-to-noise ratio improvement, image quality scores, or diagnostic accuracy, and the corresponding quantitative results obtained by the device in a study.
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 specified. The document mentions "the use of phantoms and the comparison of collected image data," but does not give a number of images, cases, or patients in the test set.
- Data Provenance: Not specified (country of origin, retrospective/prospective). The mention of "phantoms" suggests synthetic data was used at least in part, but "collected image data" is vague.
Missing Information: Actual sample sizes for the test set (number of images/cases), and whether the data was clinical (retrospective/prospective) or entirely synthetic, and its geographical origin.
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)
Missing Information: The document does not describe how ground truth was established for any performance testing, nor does it mention the involvement of experts for this purpose.
4. Adjudication method (e.g. 2+1, 3+1, none) for the test set
Missing Information: No information is provided regarding any adjudication method for ground truth establishment.
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
Missing Information: The document does not mention any MRMC study. Its primary claim is substantial equivalence to a predicate device, not an improvement in human reader performance with AI assistance. The intended use statement says it is "for assisting professional radiologists and specialists in reaching their own diagnosis," but no study is presented to quantify this assistance.
6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done
- Standalone Performance: The document implies standalone performance testing was done, as it describes "PixelShine performance has been validated through the use of phantoms and the comparison of collected image data to ascertain image quality." However, the results are merely stated as "satisfactory" without quantitative details. It describes the device's function as "noise reduction processing."
Missing Information: Quantitative results from the standalone performance testing.
7. The type of ground truth used (expert consensus, pathology, outcomes data, etc)
- Type of Ground Truth: The document mentions "use of phantoms" for validation, which suggests engineered ground truth (known noise levels, known structures). For "collected image data," the method of establishing ground truth is not described.
Missing Information: Explicit details on the type of ground truth used for "collected image data."
8. The sample size for the training set
The document describes performance testing and validation but does not discuss the machine learning model's training process or the sample size of a training set. This is typical for a 510(k) summary, which focuses on device function and safety/effectiveness for regulatory clearance.
Missing Information: No information about a training set or its sample size is provided.
9. How the ground truth for the training set was established
Missing Information: As no training set information is provided, there is no discussion of how ground truth for a training set would have been established.
Summary of Available Information from the Document:
- Device: AlgoMedica PixelShine (K161625)
- Intended Use: Networking, communication, processing, and enhancement of CT images in DICOM format, specifically for assisting professional radiologists and specialists in reaching their own diagnosis. Not effective for lesions/abnormalities
§ 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).