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iRAD Image Enhancement System is an image processing software than can be used for image enhancement of MRI, CT, and X-Ray images. Enhanced images will be sent to PACS server and exist in conjunction to the original images.

iRAD v1.0 Image Enhancement System is a medical image enhancement software, i.e., a Software as a Medical Device (SaMD), that can be used to enhance images of MRI, CT and X-Ray. iRAD takes as input DICOM [Digital Imaging and Communications in Medicine] files of MRI, CT, and X-Ray images, and produces an enhanced output of the same file, in DICOM format that can be sent to a PACS server. The objective is to enhance the DICOM files that are obscured and not clearly visible, to be more visible, sharper, and clearer through the iRAD image enhancement process. The iRAD image enhancement is done by the implementation of an image enhancement algorithm.

iRAD is intended to be used by medical doctors, radiologists and clinicians in hospitals, radiology centers and clinics, as well as by medical universities and research intuitions. The system allows selection of input DICOM images from PACS servers. DICOM images are sent to the iRAD image enhancement server, where they are processed and sent back to the PACS server after enhancement. The enhanced and original images exist in conjunction and can be compared. The system provides the user with a set of adjustable parameters through which to control the degree of contrast and strength enhancement and noise suppression.

iRAD implements a modified contrast limited adaptive histogram equalization algorithm to improve the visibility of the image and it uses the iRAD guided filter to reduce noise. The original image is deconstructed into overlapping rectangular components. The equalization and matching algorithm is executed in overlapping rectangular regions, resulting in several level-of-detail layers. The enhanced and denoised image is reconstructed using the level-of-detail layers based on user-controlled parameters of noise suppression and detail enhancement.

Here's the breakdown of the acceptance criteria and the study proving the device meets them, based on the provided text:

Key Takeaway Regarding the Study: The provided document describes performance testing for an image enhancement system (iRAD). This system aims to improve the visibility, sharpness, and clarity of medical images (MRI, CT, X-Ray) by increasing contrast and reducing noise. It's crucial to understand that this is not a diagnostic AI system that classifies or detects pathologies. Instead, it's a tool to improve image quality for human interpretation. Therefore, the "acceptance criteria" and "study that proves the device meets the acceptance criteria" are focused on the technical performance of the image enhancement rather than diagnostic accuracy comparisons.

1. Table of Acceptance Criteria and Reported Device Performance

Note: The document describes these as "test cases" that "passed successfully" or "passed." These are implicit acceptance criteria based on the device's intended function to enhance images. Specific quantitative thresholds for "increase" or "improvement" are not explicitly stated as numerical acceptance limits in this summary, but the successful passing of these tests implies that the observed improvements met the internal criteria.

2. Sample Sizes Used for the Test Set and Data Provenance

• Mathematical Derenzo Phantom: Used for controlled testing of contrast ratio, entropy, and SNR improvement. (Quantitative count of phantoms not specified, but referred to as "the mathematical Derenzo Phantom" and "two mathematical phantoms").
• X-Ray Scans: A collection of 82 lower resolution and 21 high resolution X-Ray scans (total 103) processed.
• MRI Scans: 100 MRI scans processed.
• CT Scans: CT scans were used for SNR improvement testing (quantitative count not specified, but implied to be part of the "all images tested").
• Camera Scans: 38 camera scans processed (though the device is for medical images, these were used in one phase of testing).

Data Provenance: The document does not specify the country of origin for the X-Ray, MRI, CT, or camera scans. It also does not explicitly state whether the data was retrospective or prospective. Given the nature of performance testing for an image enhancement algorithm, it's highly likely to be retrospective data.

3. Number of Experts Used to Establish the Ground Truth for the Test Set and Their Qualifications

The concept of "ground truth" for a diagnostic task (e.g., presence/absence of a disease) is not directly applicable here, as iRAD is an image enhancement system, not a diagnostic AI. The "ground truth" in this context refers to the ideal enhanced image or the known properties of the phantom.

• For Phantoms: Mathematical phantoms inherently have a known "ground truth" for their properties (e.g., known contrast, known signal, known noise levels). No human experts are needed to establish this.
• For Clinical Images: The "ground truth" for enhancement is assessed by the measured improvement in objective metrics like contrast ratio, entropy, and SNR, rather than expert labels of image content. The document does not mention human readers or experts for grading image quality or establishing a "ground truth" for the test sets used to measure these quantitative improvements.

4. Adjudication Method for the Test Set

Not applicable, as the evaluation relies on quantitative, objective metrics for image enhancement (contrast ratio, entropy, SNR) calculated algorithmically, rather than subjective human assessment where adjudication might be needed.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study was Done

No, an MRMC comparative effectiveness study was not described in the provided text. This type of study is typically performed for diagnostic AI systems to show how AI assistance impacts human reader performance (e.g., changes in sensitivity, specificity, reading time). Since iRAD is an image enhancement tool, its primary evaluation is on the objective improvement of image characteristics, not directly on diagnostic accuracy.

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

Yes, the testing described is primarily standalone algorithm performance. The device's ability to improve contrast ratio, entropy, and SNR values was measured objectively, without direct human interaction or diagnostic performance evaluation.

7. The Type of Ground Truth Used

• Mathematical/Computational Ground Truth: For the phantom studies, the ground truth of the image properties (contrast, signal, noise) is known conceptually from the phantom design specification.
• Objective Metric-Based Ground Truth: For the clinical image sets, the "ground truth" for success is measured by the objective improvement in quantitative metrics (contrast ratio, entropy, SNR) calculated before and after processing by the iRAD algorithm. There is no "pathology" or "outcomes data" ground truth involved, as the device does not perform diagnosis.

8. The Sample Size for the Training Set

The document does not specify a sample size for a training set. Given that iRAD implements a "modified contrast limited adaptive histogram equalization algorithm" and "iRAD guided filter" (Section 5.6), which are described as algorithmic processes rather than deep learning models requiring large-scale data training, it's possible that a traditional "training set" in the machine learning sense was not used, or if it was, its size is not disclosed. The description points to a rules-based or filter-based image processing approach.

9. How the Ground Truth for the Training Set was Established

As no training set (in the machine learning sense) is explicitly mentioned, the method for establishing its ground truth is also not described. The algorithms appear to be designed based on image processing principles rather than being learned from labeled data.

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