(347 days)
Not Found
Yes
The document explicitly states that the device uses a "deep learning algorithm" and "modern deep learning and computer vision techniques," and mentions "Machine Learning Methodology: Supervised Deep Learning."
No
Explanation: This device is a diagnostic aid intended to help physicians identify regions of interest on chest radiographs. It does not directly treat or cure any condition, which is the primary function of a therapeutic device.
No
Explanation: The device is described as a "computer-assisted detection (CADe) software device" and "a concurrent reading aid for physicians." It "does not replace the role of the physician or of other diagnostic testing in the standard of care," indicating it does not provide a definitive diagnosis on its own but rather assists the physician in their diagnostic process.
Yes
The device description explicitly states "Aorta-CAD is computer-assisted detection (CADe) software". The entire description focuses on the software's function of analyzing images and generating output overlays, with no mention of accompanying hardware components.
Based on the provided information, Aorta-CAD is not an In Vitro Diagnostic (IVD) device.
Here's why:
- IVD Definition: In Vitro Diagnostic devices are used to examine specimens taken from the human body (like blood, urine, tissue) to provide information about a person's health.
- Aorta-CAD's Function: Aorta-CAD analyzes medical images (chest radiographs), not biological specimens. It processes existing image data to identify potential findings.
- Intended Use: The intended use clearly states it's a "computer-assisted detection (CADe) software device that analyzes chest radiograph studies." It's a reading aid for physicians interpreting images.
Therefore, Aorta-CAD falls under the category of medical imaging software or a computer-assisted detection (CADe) device, not an In Vitro Diagnostic.
No
The letter does not state that the FDA has reviewed and approved or cleared a PCCP for this specific device.
Intended Use / Indications for Use
Aorta-CAD is a computer-assisted detection (CADe) software device that analyzes chest radiograph studies for suspicious regions of interest (ROIs). The device uses a deep learning algorithm to identify ROIs and produces boxes around the ROIs. The boxes are labeled with one of the following radiographic findings: Aortic calcification or Dilated aorta.
Aorta-CAD is intended for use as a concurrent reading aid for physicians looking for ROIs with radiographic findings suggestive of Aortic Atherosclerosis or Aortic Ectasia. It does not replace the role of the physician or of other diagnostic testing in the standard of care. Aorta-CAD is indicated for adults only.
Product codes (comma separated list FDA assigned to the subject device)
MYN
Device Description
Aorta-CAD is computer-assisted detection (CADe) software designed for physicians to increase the accurate detection of findings on chest radiographs that are suggestive of chronic conditions in the aorta. The ROIs are labeled with one of the following radiographic findings: Aortic calcification or Dilated aorta. Aorta-CAD is intended for use as a concurrent reading aid for physicians looking for suspicious ROIs with radiographic findings suggestive of Aortic Atherosclerosis or Aortic Ectasia. Aorta-CAD's output is available for physicians as a concurrent reading aid and does not replace the role of the physician or of other diagnostic testing in the standard of care for the distinct conditions. Aorta-CAD uses modern deep learning and computer vision techniques to analyze chest radiographs.
For each image within a study, Aorta-CAD generates a DICOM Presentation State file (output overlay). If any ROI is detected by Aorta-CAD in the study, the output overlay for each image includes which radiographic finding(s) were identified and what chronic condition in the aorta is suggested by these findings, such as "Aortic calcification suggestive of Aortic Atherosclerosis." In addition, if ROI(s) are detected in an image, bounding boxes surrounding each detected ROI are included in the output overlay for that image and are labeled with the radiographic findings, such as "Aortic calcification". If no ROI is detected by Aorta-CAD in the study, the output overlay for each image will include the text "No Aorta-CAD ROI(s)" and no bounding boxes will be included. Regardless of whether an ROI is detected, the overlay includes text identifying the X-ray study as analyzed by Aorta-CAD and a customer configurable message containing a link to our instructions for users to access labeling documents. The Aorta-CAD overlay can be toggled on or off by the physician within their Picture Archiving and Communication System (PACS) viewer, allowing for concurrent review of the X-ray study.
Mentions image processing
Yes
Mentions AI, DNN, or ML
Yes (deep learning, machine learning)
Input Imaging Modality
X-ray, Digital X-ray
Anatomical Site
Chest, Aorta
Indicated Patient Age Range
Adults only
Intended User / Care Setting
Physicians / Not Found
Description of the training set, sample size, data source, and annotation protocol
Not Found
Description of the test set, sample size, data source, and annotation protocol
Not Found
Summary of Performance Studies (study type, sample size, AUC, MRMC, standalone performance, key results)
Standalone performance assessment:
- Study type: Bench Testing, Standalone performance assessment
- Sample size: 5,000 chest radiograph cases
- Sensitivity: 0.910 (95% Wilson's Confidence Interval: 0.896, 0.922)
- Specificity: 0.896 (95% Wilson's Confidence Interval: 0.889, 0.902)
- AUC: 0.974 (95% Bootstrap Confidence Interval: 0.971, 0.977)
- AUC for Aortic calcification suggestive of Aortic Atherosclerosis: 0.972 (95% Bootstrap CI: 0.967, 0.976)
- AUC for Dilated aorta suggestive of Aortic Ectasia: 0.948 (95% Bootstrap CI: 0.939, 0.957)
- Key results: Aorta-CAD detects ROIs with high sensitivity, high specificity, and high AUC. AUCs remain high across the two categories.
Clinical Study (MRMC retrospective reader study):
- Study type: Clinical Data, fully-crossed multiple reader, multiple case (MRMC) retrospective reader study
- Sample size: Each clinical reader evaluated 244 cases.
- Key results: The accuracy of readers in the intended use population was superior when aided by Aorta-CAD than when unaided by Aorta-CAD for each category as calculated by the Dorfman, Berbaum, and Metz (DBM) modeling approach. Reader AUC estimates significantly improved for both categories (p-values
§ 892.2070 Medical image analyzer.
(a)
Identification. Medical image analyzers, including computer-assisted/aided detection (CADe) devices for mammography breast cancer, ultrasound breast lesions, radiograph lung nodules, and radiograph dental caries detection, is a prescription device that is intended to identify, mark, highlight, or in any other manner direct the clinicians' attention to portions of a radiology image that may reveal abnormalities during interpretation of patient radiology images by the clinicians. This device incorporates pattern recognition and data analysis capabilities and operates on previously acquired medical images. This device is not intended to replace the review by a qualified radiologist, and is not intended to be used for triage, or to recommend diagnosis.(b)
Classification. Class II (special controls). The special controls for this device are:(1) Design verification and validation must include:
(i) A detailed description of the image analysis algorithms including a description of the algorithm inputs and outputs, each major component or block, and algorithm limitations.
(ii) A detailed description of pre-specified performance testing methods and dataset(s) used to assess whether the device will improve reader performance as intended and to characterize the standalone device performance. Performance testing includes one or more standalone tests, side-by-side comparisons, or a reader study, as applicable.
(iii) Results from performance testing that demonstrate that the device improves reader performance in the intended use population when used in accordance with the instructions for use. The performance assessment must be based on appropriate diagnostic accuracy measures (
e.g., receiver operator characteristic plot, sensitivity, specificity, predictive value, and diagnostic likelihood ratio). The test dataset must contain a sufficient number of cases from important cohorts (e.g., subsets defined by clinically relevant confounders, effect modifiers, concomitant diseases, and subsets defined by image acquisition characteristics) such that the performance estimates and confidence intervals of the device for these individual subsets can be characterized for the intended use population and imaging equipment.(iv) Appropriate software documentation (
e.g., device hazard analysis; software requirements specification document; software design specification document; traceability analysis; description of verification and validation activities including system level test protocol, pass/fail criteria, and results; and cybersecurity).(2) Labeling must include the following:
(i) A detailed description of the patient population for which the device is indicated for use.
(ii) A detailed description of the intended reading protocol.
(iii) A detailed description of the intended user and user training that addresses appropriate reading protocols for the device.
(iv) A detailed description of the device inputs and outputs.
(v) A detailed description of compatible imaging hardware and imaging protocols.
(vi) Discussion of warnings, precautions, and limitations must include situations in which the device may fail or may not operate at its expected performance level (
e.g., poor image quality or for certain subpopulations), as applicable.(vii) Device operating instructions.
(viii) A detailed summary of the performance testing, including: test methods, dataset characteristics, results, and a summary of sub-analyses on case distributions stratified by relevant confounders, such as lesion and organ characteristics, disease stages, and imaging equipment.
0
September 20, 2022
Image /page/0/Picture/1 description: The image shows the logo for the U.S. Food and Drug Administration (FDA). The logo consists of two parts: the Department of Health & Human Services logo on the left and the FDA logo on the right. The FDA logo includes the letters "FDA" in a blue square, followed by the words "U.S. FOOD & DRUG ADMINISTRATION" in blue text.
Imagen Technologies, Inc % Robert Lindsey Chief Science Officer 151 West 26th Street, 10th Floor NEW YORK NY 10001
Re: K213353
Trade/Device Name: Aorta-CAD Regulation Number: 21 CFR 892.2070 Regulation Name: Medical Image Analyzer Regulatory Class: Class II Product Code: MYN Dated: August 15, 2022 Received: August 15, 2022
Dear Robert Lindsey:
We have reviewed your Section 510(k) premarket notification of intent to market the device referenced above and have determined the device is substantially equivalent (for the indications for use stated in the enclosure) to legally marketed predicate devices marketed in interstate commerce prior to May 28, 1976, the enactment date of the Medical Device Amendments, or to devices that have been reclassified in accordance with the provisions of the Federal Food, Drug, and Cosmetic Act (Act) that do not require approval of a premarket approval application (PMA). You may, therefore, market the device, subject to the general controls provisions of the Act. Although this letter refers to your product as a device, please be aware that some cleared products may instead be combination products. The 510(k) Premarket Notification Database located at https://www.accessdata.fda.gov/scripts/cdrh/cfdocs/cfpmn/pmn.cfm identifies combination product submissions. The general controls provisions of the Act include requirements for annual registration, listing of devices, good manufacturing practice, labeling, and prohibitions against misbranding and adulteration. Please note: CDRH does not evaluate information related to contract liability warranties. We remind you, however, that device labeling must be truthful and not misleading.
If your device is classified (see above) into either class II (Special Controls) or class III (PMA), it may be subject to additional controls. Existing major regulations affecting your device can be found in the Code of Federal Regulations, Title 21, Parts 800 to 898. In addition, FDA may publish further announcements concerning your device in the Federal Register.
Please be advised that FDA's issuance of a substantial equivalence determination does not mean that FDA has made a determination that your device complies with other requirements of the Act or any Federal statutes and regulations administered by other Federal agencies. You must comply with all the Act's requirements, including, but not limited to: registration and listing (21 CFR Part 807); labeling (21 CFR Part
1
801); medical device reporting of medical device-related adverse events) (21 CFR 803) for devices or postmarketing safety reporting (21 CFR 4, Subpart B) for combination products (see https://www.fda.gov/combination-products/guidance-regulatory-information/postmarketing-safety-reportingcombination-products); good manufacturing practice requirements as set forth in the quality systems (QS) regulation (21 CFR Part 820) for devices or current good manufacturing practices (21 CFR 4. Subpart A) for combination products; and, if applicable, the electronic product radiation control provisions (Sections 531-542 of the Act); 21 CFR 1000-1050.
Also, please note the regulation entitled, "Misbranding by reference to premarket notification" (21 CFR Part 807.97). For questions regarding the reporting of adverse events under the MDR regulation (21 CFR Part 803), please go to https://www.fda.gov/medical-device-safety/medical-device-reportingmdr-how-report-medical-device-problems.
For comprehensive regulatory information about mediation-emitting products, including information about labeling regulations, please see Device Advice (https://www.fda.gov/medicaldevices/device-advice-comprehensive-regulatory-assistance) and CDRH Learn (https://www.fda.gov/training-and-continuing-education/cdrh-learn). Additionally, you may contact the Division of Industry and Consumer Education (DICE) to ask a question about a specific regulatory topic. See the DICE website (https://www.fda.gov/medical-device-advice-comprehensive-regulatoryassistance/contact-us-division-industry-and-consumer-education-dice) for more information or contact DICE by email (DICE@fda.hhs.gov) or phone (1-800-638-2041 or 301-796-7100).
Sincerely,
Laurel Burk, Ph.D. Assistant Director Diagnostic X-Ray Systems Team DHT8B: Division of Radiological Imaging Devices and Electronic Products OHT8: Office of Radiological Health Office of Product Evaluation and Quality Center for Devices and Radiological Health
Enclosure
2
Indications for Use
510(k) Number (if known) K213353
Device Name Aorta-CAD
Indications for Use (Describe)
Aorta-CAD is a computer-assisted detection (CADe) software device that analyzes chest radiograph studies for suspicious regions of interest (ROIs). The device uses a deep learning algorithm to identify ROIs and produces boxes around the ROls. The boxes are labeled with one of the following radiographic findings: Aortic calcification or Dilated aorta.
Aorta-CAD is intended for use as a concurrent reading aid for physicians looking for ROls with radiographic findings suggestive of Aortic Atherosclerosis or Aortic Ectasia. It does not replace the role of the physician or of other diagnosic testing in the standard of care. Aorta-CAD is indicated for adults only.
| Type of Use (Select one or both, as applicable) | |
|---|---|
| ------------------------------------------------- | -- |
X Prescription Use (Part 21 CFR 801 Subpart D)
Over-The-Counter Use (21 CFR 801 Subpart C)
CONTINUE ON A SEPARATE PAGE IF NEEDED.
This section applies only to requirements of the Paperwork Reduction Act of 1995.
DO NOT SEND YOUR COMPLETED FORM TO THE PRA STAFF EMAIL ADDRESS BELOW.
The burden time for this collection of information is estimated to average 79 hours per response, including the time to review instructions, search existing data sources, gather and maintain the data needed and complete and review the collection of information. Send comments regarding this burden estimate or any other aspect of this information collection, including suggestions for reducing this burden, to:
Department of Health and Human Services Food and Drug Administration Office of Chief Information Officer Paperwork Reduction Act (PRA) Staff PRAStaff@fda.hhs.gov
"An agency may not conduct or sponsor, and a person is not required to respond to, a collection of information unless it displays a currently valid OMB number."
3
In accordance with 21 CFR 807.87(h) (and 21 CFR 807.92) the 510(k) Summary for Aorta-CAD is provided below.
SUBMITTER 1.
| Applicant: | Imagen Technologies, Inc.
594 Broadway, #701
New York, NY 10012 |
|---------------------------------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| Contact and Primary
Correspondent: | Robert Lindsey, Ph.D.
Chief Technology Officer
Imagen Technologies, Inc.
594 Broadway, #701
New York, NY 10012
917-830-4721
rob@imagen.ai |
| Secondary Correspondent: | Alex J. Cadotte, Ph.D.
Director, Software & Digital Health
MCRA
803 7th Street, NW, 3rd Floor
Washington, DC 20001
202-742-3828
acadotte@mcra.com |
| Date Prepared: | September 13, 2022 |
2. DEVICE
| Device Trade Name: | Aorta-CAD |
|---|---|
| Device Common Name or | |
| Classification Name: | Medical Image Analyzer |
| Regulation: | 21 CFR 892.2070 |
| Regulatory Class: | II |
| Product Code: | MYN |
4
PREDICATE DEVICE 3.
Imagen Technologies' Chest-CAD has been identified as the predicate device for Aorta-CAD.
DEVICE DESCRIPTION 4.
Aorta-CAD is computer-assisted detection (CADe) software designed for physicians to increase the accurate detection of findings on chest radiographs that are suggestive of chronic conditions in the aorta. The ROIs are labeled with one of the following radiographic findings: Aortic calcification or Dilated aorta. Aorta-CAD is intended for use as a concurrent reading aid for physicians looking for suspicious ROIs with radiographic findings suggestive of Aortic Atherosclerosis or Aortic Ectasia. Aorta-CAD's output is available for physicians as a concurrent reading aid and does not replace the role of the physician or of other diagnostic testing in the standard of care for the distinct conditions. Aorta-CAD uses modern deep learning and computer vision techniques to analyze chest radiographs.
For each image within a study, Aorta-CAD generates a DICOM Presentation State file (output overlay). If any ROI is detected by Aorta-CAD in the study, the output overlay for each image includes which radiographic finding(s) were identified and what chronic condition in the aorta is suggested by these findings, such as "Aortic calcification suggestive of Aortic Atherosclerosis." In addition, if ROI(s) are detected in an image, bounding boxes surrounding each detected ROI are included in the output overlay for that image and are labeled with the radiographic findings, such as "Aortic calcification". If no ROI is detected by Aorta-CAD in the study, the output overlay for each image will include the text "No Aorta-CAD ROI(s)" and no bounding boxes will be included. Regardless of whether an ROI is detected, the overlay includes text identifying the X-ray study as analyzed by Aorta-CAD and a customer configurable message containing a link to our instructions for users to access labeling documents. The Aorta-CAD overlay can be toggled on or off by the physician within their Picture Archiving and Communication System (PACS) viewer, allowing for concurrent review of the X-ray study.
INTENDED USE/INDICATIONS FOR USE ട.
Aorta-CAD is a computer-assisted detection (CADe) software device that analyzes chest radiograph studies for suspicious regions of interest (ROIs). The device uses a deep learning algorithm to identify ROIs and produce boxes around the ROIs. The boxes are labeled with one of the following radiographic findings: Aortic calcification or Dilated aorta.
Aorta-CAD is intended for use as a concurrent reading aid for physicians looking for ROIs with radiographic findings suggestive of Aortic Atherosclerosis or Aortic Ectasia. It does not replace the role of the physician or of other diagnostic testing in the standard of care. Aorta-CAD is indicated for adults only.
5
SUBSTANTIAL EQUIVALENCE 6.
Comparison of Indications
The predicate device for Aorta-CAD is Chest-CAD (K210666). Chest-CAD has the following FDA-cleared Indications for Use:
Chest-CAD is a computer-assisted detection (CADe) software device that analyzes chest radiograph studies using machine learning techniques to identify, categorize, and highlight suspicious regions of interest (ROI). Any suspicious ROI identified by Chest-CAD is assigned to one of the following categories: Cardiac, Mediastinum/Hila, Lungs, Pleura, Bones, Soft Tissues, Hardware, or Other. The device is intended for use as a concurrent reading aid for physicians. Chest-CAD is indicated for adults only.
Chest-CAD and Aorta-CAD both analyze chest radiographs and both detect ROIs in the chest. Both devices identify and categorize ROIs. Chest-CAD and Aorta-CAD are indicated for use as a concurrent reading aid. Both devices are intended as an aid to the physician and not intended to replace the role of the physician or of other diagnostic testing in the standard of care. The differences in Indications for Use do not constitute a new intended use, as both devices are intended to assist physicians by identifying and marking ROIs in chest radiographs.
Technological Comparisons
Table 1 provides a comparison of the Technological Characteristics of Aorta-CAD to the predicate Chest-CAD.
| Proposed Device | Predicate | |
|---|---|---|
| Number | TBD | K210666 |
| Applicant | Imagen Technologies, Inc. | Imagen Technologies, Inc. |
| Device Name | Aorta-CAD | Chest-CAD |
| Classification Regulation | 892.2070 | 892.2070 |
| Product Code | MYN | MYN |
| Image Modality | X-ray | X-ray |
| Study Type | Chest | Chest |
| Clinical Output | Identify and mark regions of interest (ROIs) on chest radiographs and label the box around the ROI as one of the following: Aortic calcification or Dilated aorta. | Identify and mark regions of interest (ROIs) on chest radiographs and label the box around the ROI as one of the following: Cardiac, Mediastinum/Hila, Lungs, Pleura, Bones, Soft Tissues, Hardware, or Other. |
Table 1: Technological Comparison
6
| Proposed Device | Predicate | |
|---|---|---|
| Intended Users | Physicians | Physicians |
| Intended User Workflow | Device intended for use as a concurrent reading aid for physicians interpreting chest radiographs. | Device intended for use as a concurrent reading aid for physicians interpreting chest radiographs. |
| Patient Population | Adults with Chest Radiographs | Adults with Chest Radiographs |
| Machine Learning Methodology | Supervised Deep Learning | Supervised Deep Learning |
| Platform | Secure cloud-based processing and delivery of chest radiographs | Secure cloud-based processing and delivery of chest radiographs |
| Image Source | Digital X-ray | Digital X-ray |
| Image Viewing | Image displayed on PACS system | Image displayed on PACS system |
| Privacy | HIPAA Compliant | HIPAA Compliant |
As outlined in the table above, Aorta-CAD's technological characteristics are similar to those of Chest-CAD. Aorta-CAD differs from Chest-CAD in that Aorta-CAD simultaneously identifies and categorizes ROIs as one of two categories compared to Chest-CAD which simultaneously identifies and categorizes ROIs as one of eight categories. The fundamental purpose of both devices is to identify ROIs on chest X-rays for further consideration by the physicians, and the differences in technological characteristics do not raise different concerns of safety and effectiveness.
PERFORMANCE DATA 7.
Biocompatibility Testing
There are no direct or indirect patient-contacting components of the subject device. Therefore, patient contact information is not needed for this device.
Electrical Safety and Electromagnetic Compatibility (EMC)
The subject device is a software-only device. Therefore, electrical safety and EMC testing was not necessary to establish the substantial equivalence of this device.
Software Verification and Validation Testing
Software verification and validation testing were conducted, and documentation was provided as recommended by FDA's Guidance for Industry and FDA Staff, "Guidance for the Content of Premarket Submissions for Software Contained in Medical Devices." The software level of concern for Aorta-CAD is Moderate since a malfunction of, or a latent design flaw in, the
7
software device may lead to an erroneous diagnosis or a delay in delivery of appropriate medical care that would likely lead to Minor Injury.
Bench Testing
Imagen conducted a standalone performance assessment on 5,000 chest radiograph cases representative of the intended use population. The results of the standalone testing demonstrated that Aorta-CAD detects ROIs with high sensitivity (0.910; 95% Wilson's Confidence Interval: 0.896. 0.922), high specificity (0.896; 95% Wilson's Confidence Interval: 0.889, 0.902), and high Area Under the Curve (AUC) of the Receiver Operating Characteristic (ROC) curve (0.974, 95% Bootstrap Confidence Interval: 0.971, 0.977).
The AUC of the ROC curve was also estimated for each category and Figure 1 shows AUCs remain high across the two categories (further details in Table 2). The AUC of the ROC curve was 0.972 for Aortic calcification suggestive of Aortic Atherosclerosis and was 0.948 for Dilated aorta suggestive of Aortic Ectasia. Sensitivity and specificity were calculated for each category. As shown in Table 3, sensitivity was 0.922 for Aortic calcification suggestive of Aortic Atherosclerosis and sensitivity was 0.830 for Dilated aorta suggestive of Aortic Ectasia. Specificity was 0.897 for Dilated aorta suggestive of Aortic Ectasia and was 0.894 for Aortic calcification suggestive of Aortic Atherosclerosis. The Free-Response ROC (FROC) curve was also estimated for each Aorta-CAD category and Figure 2 shows the box-level sensitivity versus the false positives per image. The FROC curves terminate at the device's box-level sensitivity for each category due to the cascaded nature of the Aorta-CAD predictions.
8
Image /page/8/Figure/2 description: This image is a plot that shows sensitivity on the y-axis and 1-specificity on the x-axis. There are two curves on the plot, one for 'Aortic calcification suggestive of Aortic Atherosclerosis' and one for 'Dilated aorta suggestive of Aortic Ectasia'. The 'Aortic calcification suggestive of Aortic Atherosclerosis' curve is blue, and the 'Dilated aorta suggestive of Aortic Ectasia' curve is red. The plot also includes a dashed line that represents the line of no discrimination.
Figure 1: Standalone Results - Aorta-CAD ROC Curve by Category
Table 2: AUC of the ROC Curve for Aorta-CAD Predictions by Category
| Category | Ground Truth
Positive n (%) | AUC | 95% Bootstrap CI |
|-----------------------------------------------------------------|--------------------------------|-------|------------------|
| Aortic calcification
suggestive of Aortic
Atherosclerosis | 1662 (33.2) | 0.972 | 0.967, 0.976 |
| Dilated aorta suggestive
of Aortic Ectasia | 247 (4.9) | 0.948 | 0.939, 0.957 |
Abbreviations: AUC = Area Under the Curve; CI = Confidence Interval; ROC = Receiver Operating Characteristic.
9
| Category | Sensitivity | Specificity | Positive
Predictive Value | Negative
Predictive Value |
|-----------------------------------------------------------------|-------------------------|-------------------------|------------------------------|------------------------------|
| | 95%
Wilson's CI | 95%
Wilson's CI | 95%
Wilson's CI | 95%
Wilson's CI |
| Aortic calcification
suggestive of Aortic
Atherosclerosis | 0.922
(0.908, 0.934) | 0.894
(0.883, 0.904) | 0.812
(0.794, 0.829) | 0.958
(0.951, 0.965) |
| Dilated aorta
suggestive of Aortic
Ectasia | 0.830
(0.778, 0.872) | 0.897
(0.888, 0.906) | 0.296
(0.263, 0.331) | 0.990
(0.987, 0.993) |
Sensitivity, Specificity, Positive Predictive Value, and Negative Predictive for Aorta-Table 3: CAD Predictions by Category
Abbreviations: CI = Confidence Interval.
Standalone Results - Free-Response ROC (FROC) Curve by Aorta-CAD Category Figure 2:
Image /page/9/Figure/6 description: The image is a plot comparing the box-level sensitivity versus false positives per image for two different conditions. The blue line represents "Aortic calcification suggestive of Aortic Atherosclerosis", while the red line represents "Dilated aorta suggestive of Aortic Ectasia". The plot shows that the blue line has a higher box-level sensitivity than the red line for lower values of false positives per image. The box-level sensitivity ranges from 0.0 to 1.0, and the false positives per image range from 0.0 to 0.7.
10
Animal Testing
Not applicable. Animal studies are not necessary to establish the substantial equivalence of this device.
Clinical Data
Imagen conducted a fully-crossed multiple reader, multiple case (MRMC) retrospective reader study to determine the impact of Aorta-CAD on reader performance in detecting Aortic calcification suggestive of Aortic Atherosclerosis and Dilated aorta suggestive of Aortic Ectasia in chest radiograph cases. The primary objective of this study was to determine whether the accuracy of readers aided by Aorta-CAD ("Aided") was superior to the accuracy of readers when unaided by Aorta-CAD ("Unaided") per category as determined by the case-level Area Under the Curve (AUC) of the Receiver Operating Characteristic (ROC) curve.
Clinical readers each evaluated 244 cases in Aorta-CAD's Indications for Use under both Aided and Unaided conditions. Each case was previously evaluated by a panel of U.S. board-certified radiologists who assigned a ground truth binary label indicating the presence of Aortic calcification suggestive of Aortic Atherosclerosis and Dilated aorta suggestive of Aortic Ectasia. The MRMC study consisted of two independent reading sessions separated by a washout period of at least 28 days in order to avoid memory bias. For each case, each reader was required to provide a binary determination of the presence of an ROI for each category and to provide a confidence score representing their certainty.
The accuracy of readers in the intended use population was superior when aided by Aorta-CAD than when unaided by Aorta-CAD for each category as calculated by the Dorfman, Berbaum, and Metz (DBM) modeling approach. The results of the clinical study are shown in Figure 3 and Figure 4.
11
Figure 3: Reader Study Results - Aided and Unaided ROC Curves for Aortic calcification suggestive of Aortic Atherosclerosis
Image /page/11/Figure/3 description: This image is a plot comparing the sensitivity and specificity of two modalities, "Aided" and "Unaided". The x-axis represents "1 - Specificity", while the y-axis represents "Sensitivity". The "Aided" modality is represented by a solid blue line, and the "Unaided" modality is represented by a dotted blue line. The plot shows that the "Aided" modality has higher sensitivity and specificity than the "Unaided" modality.
Reader Study Results - Aided and Unaided ROC Curves for Dilated aorta Figure 4: suggestive of Aortic Ectasia
Image /page/11/Figure/5 description: This image is a plot of sensitivity vs 1-specificity. There are two curves on the plot, one for 'Aided' and one for 'Unaided'. The 'Aided' curve is solid, while the 'Unaided' curve is dotted. The 'Aided' curve is generally higher than the 'Unaided' curve, indicating that the 'Aided' modality has better performance.
12
In particular, the clinical study results demonstrated improvements when Aided versus Unaided:
- Reader AUC estimates significantly improved for both categories (p-values