Search Results

Medicsight ColonCAD™ API is a non-invasive computer aided detection (CAD) image analysis software tool designed to assist radiologists in the detection of colorectal polyps during their review of digital images derived from CT colonography (CTC). This CAD software post-processes the CTC image data obtained from multi-detector computed tomography (MDCT) scanners.

The device is intended to be used on patients referred for a CT Colonography examination, as an overlay tool to prompt the radiologist to colonic findings that have been identified by the device. The CAD can assist radiologists after they have made an initial review of all the CTC image data, supporting their evaluation ("second read").

Medicsight ColonCAD API is a medical imaging software tool designed to assist radiologists in the detection of polyps in CT scans of the product is packaged as an Application Programming Interface (API) which allows it to be integrated into existing medical imaging solutions.

The ColonCAD API assists the radiologist in detecting colorectal polyps using mathematical image processing techniques. The CAD assists the radiologist by highlighting potential polyps in 2D and 3D image views. The results are displayed in the form of "CAD marks" on or near the potential polyps. The radiologist must assess every CT scan image to search for polyps and review the CAD marked images to determine if the indicated findings are polyps.

Patient management decisions should not be made solely on the results of ColonCAD analysis.

The information provided details the Medicsight ColonCAD API device, its intended use, and a summary of studies conducted. However, the document does not explicitly state specific acceptance criteria (e.g., a required sensitivity or specificity value) or the reported device performance in a numerical table. It only mentions that the device's accuracy was "significantly higher" with CAD assistance.

Therefore, I cannot populate a table of acceptance criteria and reported device performance with specific numerical values based on this document.

Here's an analysis of the provided information concerning the study:

1. Table of Acceptance Criteria and Reported Device Performance

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

• Sample Size for Test Set: Not explicitly stated in the provided document.
• Data Provenance: Not explicitly stated in the provided document (e.g., country of origin, retrospective or prospective).

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

• Number of Experts: Not explicitly stated in the provided document.
• Qualifications of Experts: Not explicitly stated in the provided document.

4. Adjudication Method for the Test Set

• Adjudication Method: Not explicitly stated in the provided document.

5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study

• Was an MRMC study done? Yes. The document states: "The results of the MRMC study demonstrated that radiologists' accuracy for detecting colorectal polyps of any size was significantly higher with CAD than in the unassisted read, as measured by the segment-level area under the ROC curve (AUC)."
• Effect Size: The document states that accuracy was "significantly higher" with CAD, and mentions "segment-level area under the ROC curve (AUC)" as the metric. However, it does not provide a numerical effect size (e.g., the specific AUC values for assisted vs. unassisted read, or the magnitude of improvement).

6. Standalone Performance Study

• Was a standalone study done? The document focuses on the C-CAD (Computer Aided Detection) device assisting radiologists, implying a human-in-the-loop scenario. While it mentions "internal clinical evaluations" as part of non-clinical studies, it does not detail a standalone algorithm-only performance study with specific metrics. The primary clinical study discussed is an MRMC study evaluating human-in-the-loop performance.

7. Type of Ground Truth Used

• Type of Ground Truth: Not explicitly stated in the provided document for the clinical study. It refers to "detecting colorectal polyps," implying a definitive diagnosis, but the method for establishing this truth is not detailed (e.g., pathology, expert consensus).

8. Sample Size for the Training Set

• Sample Size for Training Set: Not explicitly stated in the provided document. The document mentions the ColonCAD API uses the "same underlying image processing technology" and "same algorithm" as the predicate ColonCAR 1.2 device (K042674), suggesting the training might have occurred prior to this specific API version's development or relies on pre-trained models.

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

• How Ground Truth Was Established (Training Set): Not explicitly stated in the provided document. As with the test set, the method for establishing the ground truth for training data (if new training was performed) is not detailed.

Ask a specific question about this device

iCAD's VeraLook™ CTC CAD is intended to automatically detect potential polyps in CT Colonography exams. The identified polyps can then be highlighted to the interpreting physician after initial review of the CTC exam with the intent of identifying additional potential polyps that may not have been identified on initial review.

The VeraLook is a software-based CAD system for detecting the location and extent of polyps in CTC exams. The product is intended to receive CTC images in standard DICOM format from any 3D workstation manufacturer, perform automated analysis on the images to identify polyps, and then produce information about the identified regions that can be received and displayed by CTC review workstations to help radiologists in the detection of polyps.

This looks like a medical device submission, specifically a 510(k) summary for iCAD's VeraLook™ CTC CAD Software. The provided text doesn't contain a detailed study report with specific acceptance criteria and performance metrics. Instead, it offers a high-level overview of the device, its intended use, and a general statement about clinical data being supplied to satisfy the FDA's requirements.

Therefore, I cannot populate all the requested information directly from the provided text. I can only extract what is explicitly stated or can be reasonably inferred. Many of the requested details (like sample sizes, number of experts, adjudication methods, ground truth types for training and testing, and specific performance metrics) are not present in this summary document.

Here's a response based on the available information:

Acceptance Criteria and Study to Prove Device Meets Acceptance Criteria

The provided 510(k) summary states that "iCAD has supplied clinical data in addition to bench testing and simulations to assess safety and effectiveness of this claim" (referring to the claim of identifying "additional potential polyps that may not have been identified on initial review"). However, the summary does not provide specific acceptance criteria or the detailed results of that study.

Based on the provided text, the specific acceptance criteria and the detailed study proving the device meets them are not described. The summary only mentions that clinical data was supplied to address potential safety and effectiveness issues related to the device's specific claim.

1. Table of Acceptance Criteria and Reported Device Performance

Cannot be fully populated from the provided text. The document does not list specific acceptance criteria (e.g., sensitivity, specificity thresholds) nor does it provide a table of reported device performance metrics against such criteria.

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

The provided text does not specify the sample size used for the test set or the data provenance (e.g., country of origin, retrospective/prospective). It only generally states that "clinical data was supplied."

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

The provided text does not specify the number of experts used or their qualifications for establishing ground truth.

4. Adjudication Method for the Test Set

The provided text does not specify any adjudication method (e.g., 2+1, 3+1, none) for the test set.

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

The provided text does not explicitly state whether an MRMC comparative effectiveness study was done. It mentions the intent to identify "additional potential polyps that may not have been identified on initial review," which suggests an assist-read paradigm, but the details of such a study are not included. Therefore, the effect size of human readers' improvement with AI vs. without AI assistance is not provided.

6. If a Standalone (Algorithm Only Without Human-in-the-Loop Performance) Was Done

The provided text does not explicitly state whether a standalone (algorithm only) performance study was done. The indication for use describes the device as providing "highlighted" polyps "after initial review of the CTC exam," implying human-in-the-loop performance.

7. The Type of Ground Truth Used (Expert Consensus, Pathology, Outcomes Data, etc.)

The provided text does not specify the type of ground truth used for either the test set or the training set.

8. The Sample Size for the Training Set

The provided text does not specify the sample size for the training set.

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

The provided text does not describe how the ground truth for the training set was established.

Ask a specific question about this device