(265 days)
Not Found
Yes
The document explicitly states the device uses a "deep learning algorithm" and "modern deep learning and computer vision techniques," and lists "Supervised Deep Learning" under "Machine Learning Methodology."
No
The device is a computer-assisted detection (CADe) software intended as a reading aid to help physicians identify lung hyperinflation. It is not intended for clinical diagnosis of any disease and does not provide therapy.
No.
The device is specifically stated as "not intended for clinical diagnosis of any disease" and "does not replace the role of the physician or of other diagnostic testing in the standard of care and does not provide a diagnosis for any disease." It functions as a "concurrent reading aid for physicians," helping to "more effectively identify lung hyperinflation" rather than making a diagnosis itself.
Yes
The device is described as "computer-assisted detection (CADe) software" and its output is a "DICOM Presentation State file (output overlay)". There is no mention of any accompanying hardware or hardware components being part of the device itself.
Based on the provided information, Lung-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 for diagnosis, monitoring, or screening.
- Lung-CAD's Function: Lung-CAD analyzes images (chest radiographs) of the human body, not specimens taken from the body. It processes visual data, not biological samples.
- 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.
- No Specimen Analysis: There is no mention of analyzing any biological specimens.
Therefore, Lung-CAD falls under the category of medical imaging software or a computer-assisted detection (CADe) device, not an In Vitro Diagnostic device.
No
The provided document does not contain any explicit statement that the FDA has reviewed, approved, or cleared a PCCP for this specific device.
Intended Use / Indications for Use
Lung-CAD is a computer-assisted detection (CADe) software device that analyzes chest radiograph studies for lung hyperinflation. The device uses a deep learning algorithm to identify regions of interest (ROIs) with lung hyperinflation and produces boxes around the ROIs.
Lung-CAD is intended for use as a concurrent reading aid for physicians interpreting chest X-rays. The device is not intended for clinical diagnosis of any disease. It does not replace the role of other diagnostic testing in the standard of care for lung parenchymal findings. Lung-CAD is indicated for adults only.
Product codes
MYN
Device Description
Lung-CAD is computer-assisted detection (CADe) software designed to increase the accurate detection of lung hyperinflation. Lung-CAD's output is available for physicians interpreting chest radiographs as a concurrent reading aid. The device helps physicians more effectively identify lung hyperinflation. Lung-CAD does not replace the role of the physician or of other diagnostic testing in the standard of care and does not provide a diagnosis for any disease. Lung-CAD uses modern deep learning and computer vision techniques to analyze chest radiographs.
For each image within a study, Lung-CAD generates a DICOM Presentation State file (output overlay). If any region of interest (ROI) is detected by Lung-CAD in the study, the output overlay for each image includes "Lung hyperinflation". 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: "Lung hyperinflation". If no ROI is detected by Lung-CAD in the study, the output overlay for each image will include the text "No Lung-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 Lung-CAD and a customer configurable message containing a link or instructions, for users, to access labeling documents. The Lung-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
Not Found
Mentions AI, DNN, or ML
Lung-CAD is a computer-assisted detection (CADe) software device that analyzes chest radiograph studies for lung hyperinflation. The device uses a deep learning algorithm to identify regions of interest (ROIs) with lung hyperinflation and produces boxes around the ROIs.
Lung-CAD uses modern deep learning and computer vision techniques to analyze chest radiographs.
Machine Learning Methodology: Supervised Deep Learning
Input Imaging Modality
X-ray
Digital X-ray
Anatomical Site
Chest
Indicated Patient Age Range
adults only
Intended User / Care Setting
physicians interpreting chest X-rays
concurrent reading aid
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
Standalone Performance Assessment:
- Study Type: Standalone performance assessment
- Sample Size: 5,000 chest radiograph cases
- Key Results:
- Sensitivity: 0.898 (95% Wilson's Confidence Interval: 0.856, 0.929)
- Specificity: 0.894 (95% Wilson's Confidence Interval: 0.885, 0.902)
- AUC: 0.964 (95% Bootstrap Confidence Interval: 0.956, 0.972)
- FROC curve also estimated, showing box-level sensitivity versus false positives per image.
Clinical Data (MRMC Retrospective Reader Study):
- Study Type: 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 Lung-CAD than when unaided by Lung-CAD as calculated by the Dorfman, Berbaum, and Metz (DBM) modeling approach.
- Reader AUC estimates significantly improved (p-value
§ 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.
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October 3, 2023
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Imagen Technologies, Inc % Rebecca Jones Director, Clinical Research 594 Broadway, Suite 701 NEW YORK, NY 10012
Re: K230085
Trade/Device Name: Lung-CAD Regulation Number: 21 CFR 892.2070 Regulation Name: Medical image analyzer Regulatory Class: Class II Product Code: MYN Dated: September 4, 2023 Received: September 5, 2023
Dear Rebecca Jones:
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 (the 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 available 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.
Additional information about changes that may require a new premarket notification are provided in the FDA guidance documents entitled "Deciding When to Submit a 510(k) for a Change to an Existing Device" (https://www.fda.gov/media/99812/download) and "Deciding When to Submit a 510(k) for a Software Change to an Existing Device" (https://www.fda.gov/media/99785/download).
Your device is also subject to, among other requirements, the Quality System (QS) regulation (21 CFR Part 820), which includes, but is not limited to, 21 CFR 820.30, Design controls; 21 CFR 820.90, Nonconforming product; and 21 CFR 820.100, Corrective and preventive action. Please note that regardless of whether a
1
change requires premarket review, the QS regulation requires device manufacturers to review and approve changes to device design and production (21 CFR 820.30 and 21 CFR 820.70) and document changes and approvals in the device master record (21 CFR 820.181).
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 801); medical device reporting of medical device-related adverse events) (21 CFR Part 803) for devices or postmarketing safety reporting (21 CFR Part 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 Part 4. Subpart A) for combination products; and, if applicable, the electronic product radiation control provisions (Sections 531-542 of the Act); 21 CFR Parts 1000-1050.
Also, please note the regulation entitled, "Misbranding by reference to premarket notification" (21 CFR 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,
Lu Jiang
Lu Jiang, 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
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Indications for Use
510(k) Number (if known) K230085
Device Name Lung-CAD
Indications for Use (Describe)
Lung-CAD is a computer-assisted detection (CADe) software device that analyzes chest radiograph studies for lung hyperinflation. The device uses a deep learning algorithm to identify regions of interest (ROIs) with lung hyperinflation and produces boxes around the ROIs.
Lung-CAD is intended for use as a concurrent reading aid for physicians interpreting chest X-rays. The device is not intended for clinical diagnosis of any disease. It does not replace the role of other diagnostic testing in the standard of care for lung parenchymal findings. Lung-CAD is indicated for adults only.
| Type of Use (Select one or both, as applicable) | |
|---|---|
| Prescription Use (Part 21 CFR 801 Subpart D) | Over-The-Counter Use (21 CFR 801 Subpart C) |
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K230085
510(k) Summary
In accordance with 21 CFR 807.87(h) (and 21 CFR 807.92) the 510(k) Summary for Lung-CAD is provided below.
SUBMITTER 1.
| Applicant: | Imagen Technologies, Inc.
224 W 35th St Ste 500
New York, NY 10001 |
|---------------------------------------|---------------------------------------------------------------------------------------------------------------------|
| Contact and Primary
Correspondent: | Rebecca Jones, Ph.D.
VP, Clinical Research
Head of Regulatory |
| | Imagen Technologies, Inc.
224 W 35th St Ste 500
New York, NY 10001
917-565-9319
rebecca.jones@imagen.ai |
| Secondary Correspondent: | Alex J. Cadotte, Ph.D.
Senior Director, Digital Health & Imaging |
| | MCRA
803 7th Street, NW, 3rd Floor
Washington, DC 20001
202-742-3828
acadotte@mcra.com |
| Date Prepared: | September 22, 2023. |
2. DEVICE
| Device Trade Name: | Lung-CAD |
|---|---|
| Device Common Name or | |
| Classification Name: | Medical Image Analyzer |
| Regulation: | 21 CFR 892.2070 |
| Regulatory Class: | II |
| Product Code: | MYN |
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PREDICATE DEVICE 3.
Imagen Technologies' Chest-CAD has been identified as the predicate device for Lung-CAD.
DEVICE DESCRIPTION 4.
Lung-CAD is computer-assisted detection (CADe) software designed to increase the accurate detection of lung hyperinflation. Lung-CAD's output is available for physicians interpreting chest radiographs as a concurrent reading aid. The device helps physicians more effectively identify lung hyperinflation. Lung-CAD does not replace the role of the physician or of other diagnostic testing in the standard of care and does not provide a diagnosis for any disease. Lung-CAD uses modern deep learning and computer vision techniques to analyze chest radiographs.
For each image within a study, Lung-CAD generates a DICOM Presentation State file (output overlay). If any region of interest (ROI) is detected by Lung-CAD in the study, the output overlay for each image includes "Lung hyperinflation". 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: "Lung hyperinflation". If no ROI is detected by Lung-CAD in the study, the output overlay for each image will include the text "No Lung-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 Lung-CAD and a customer configurable message containing a link or instructions, for users, to access labeling documents. The Lung-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 5.
Lung-CAD is a computer-assisted detection (CADe) software device that analyzes chest radiograph studies for lung hyperinflation. The device uses a deep learning algorithm to identify regions of interest (ROIs) with lung hyperinflation and produces boxes around the ROIs.
Lung-CAD is intended for use as a concurrent reading aid for physicians interpreting chest X-rays. The device is not intended for clinical diagnosis of any disease. It does not replace the role of the physician or of other diagnostic testing in the standard of care for lung parenchymal findings. Lung-CAD is indicated for adults only.
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SUBSTANTIAL EQUIVALENCE 6.
Comparison of Indications
The predicate device for Lung-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 Lung-CAD both analyze chest radiographs and both detect ROIs in the chest. Both devices identify and categorize ROIs. Chest-CAD and Lung-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 Lung-CAD to the predicate Chest-CAD.
| Proposed Device | Predicate | |
|---|---|---|
| Number | K230085 | K210666 |
| Applicant | Imagen Technologies, Inc. | Imagen Technologies, Inc. |
| Device Name | Lung-CAD | Chest-CAD |
| Classification Regulation | 892.2070 | 892.2070 |
| Product Code | MYN | MYN |
| Image Modality | X-ray | X-ray |
| Study Type | Chest | Chest |
Table 1: Technological Comparison
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| Proposed Device | Predicate | |
|---|---|---|
| Clinical Output | Identify and mark regions of interest (ROIs) on chest radiographs and label the box around the ROI as lung hyperinflation | 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 |
| 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, Lung-CAD's technological characteristics are similar to those of Chest-CAD. Lung-CAD differs from Chest-CAD in that Lung-CAD simultaneously identifies and categorizes ROIs as one category 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.
7. PERFORMANCE DATA
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.
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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 Lung-CAD is Moderate since a malfunction of, or a latent design flaw in, the 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 Lung-CAD detects ROIs with high sensitivity (0.898; 95% Wilson's Confidence Interval: 0.856. 0.929), high specificity (0.894; 95% Wilson's Confidence Interval: 0.885. 0.902), and high Area Under the Curve (AUC) of the Receiver Operating Characteristic (ROC) curve (0.964, 95% Bootstrap Confidence Interval: 0.956. 0.972) as shown in Figure 1, Table 2, and Table 3.
The Free-Response ROC (FROC) curve was also estimated and Figure 2 shows the box-level sensitivity versus the false positives per image. The FROC curve terminates at the device's box-level sensitivity due to the cascaded nature of the Lung-CAD predictions.
Figure 1: Standalone Results - Lung-CAD ROC Curve
Image /page/7/Figure/8 description: The image is a plot of sensitivity versus 1-specificity, which is a receiver operating characteristic (ROC) curve. The x-axis is labeled "1- Specificity" and ranges from 0.0 to 1.0. The y-axis is labeled "Sensitivity" and ranges from 0.0 to 1.0. The ROC curve for lung hyperinflation is shown as a solid line that starts at (0,0) and rises sharply to near 1.0 before gradually approaching (1,1). A dashed line represents the line of no discrimination, running from (0,0) to (1,1).
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| Category | Ground Truth
Positive n (%) | AUC | 95% Bootstrap CI |
|---------------------|--------------------------------|-------|------------------|
| Lung hyperinflation | 266 (5.3) | 0.964 | 0.956, 0.972 |
Table 2: AUC of the ROC Curve for Lung-CAD Predictions
Abbreviations: AUC = Area Under the Curve; CI = Confidence Interval; ROC = Receiver Operating Characteristic.
Sensitivity, Specificity, Positive Predictive Value, and Negative Predictive for Table 3: Lung-CAD Predictions
| Category | Sensitivity | Specificity | Positive
Predictive Value | Negative
Predictive Value |
|---------------------|------------------------------------------------------------------------------------------------------------------------------------------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|------------------------------------------------------------------------------------------------------------------------------------------------|------------------------------|
| | તે જેન્જી જેવી સવલતો પ્રાપ્ય થયેલી છે. આ ગામના લોકોનો મુખ્ય વ્યવસાય ખેતી, ખેતમજૂરી તેમ જ પશુપાલન છે. આ ગામનાં મુખ્યત્વે ખેત-ઉપય
Wilson's CI | તે જેન્જી જેવી સવલતો પ્રાપ્ય થયેલી છે. આ ગામના લોકોનો મુખ્ય વ્યવસાય ખેતી, ખેતમજૂરી તેમ જ પશુપાલન છે. આ ગામના લોકોનો મુખ્ય વ્યવસાય ખેતી, ખેતમજૂરી તેમ જ પશુપાલન છે. આ ગામના લો
Wilson's CI | તે જેન્જી જેવી સવલતો પ્રાપ્ય થયેલી છે. આ ગામના લોકોનો મુખ્ય વ્યવસાય ખેતી, ખેતમજૂરી તેમ જ પશુપાલન છે. આ ગામનાં મુખ્યત્વે ખેત-ઉપય
Wilson's CI | 95%
Wilson's CI |
| Lung hyperinflation | 0.898
(0.856, 0.929) | 0.894
(0.885, 0.902) | 0.322
(0.289, 0.357) | 0.994
(0.991, 0.996) |
Abbreviations: CI = Confidence Interval.
Figure 2: Standalone Results - Free-Response ROC (FROC) Curve
Image /page/8/Figure/9 description: The image is a plot of box-level sensitivity versus false positives per image for lung hyperinflation. The x-axis represents the false positives per image, ranging from 0.0 to 0.7. The y-axis represents the box-level sensitivity, ranging from 0.0 to 1.0. The plot shows that the sensitivity increases rapidly as the number of false positives increases from 0.0 to 0.1, and then plateaus as the number of false positives increases further.
Animal Testing
Not applicable. Animal studies are not necessary to establish the substantial equivalence of this device.
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Clinical Data
Imagen conducted a fully-crossed multiple reader, multiple case (MRMC) retrospective reader study to determine the impact of Lung-CAD on reader performance in detecting lung hyperinflation in chest radiograph cases. The primary objective of this study was to determine whether the accuracy of readers aided by Lung-CAD ("Aided") was superior to the accuracy of readers when unaided by Lung-CAD ("Unaided") 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 Lung-CAD's Indications for Use under both Aided and Unaided conditions. 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.
The accuracy of readers in the intended use population was superior when aided by Lung-CAD than when unaided by Lung-CAD as calculated by the Dorfman, Berbaum, and Metz (DBM) modeling approach. The results of the clinical study are shown in Figure 3.
Figure 3: Reader Study Results - Aided and Unaided ROC Curves for Lung Hyperinflation
Image /page/9/Figure/7 description: The 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 and the "Unaided" curve is dotted. The "Aided" curve is generally above the "Unaided" curve, indicating that the "Aided" modality has higher sensitivity for a given level of specificity.
In particular, the clinical study results demonstrated improvements when Aided versus Unaided:
- Reader AUC estimates significantly improved (p-value