(81 days)
Not Found
Yes
The document explicitly states that the device "uses an artificial intelligence algorithm to analyze images" and mentions "deep learning AI algorithms".
No.
The device is a radiological computer-aided triage and notification software intended to assist with workflow triage by flagging and communicating suspect positive cases. It is not intended for diagnostic use beyond notification and does not directly treat or diagnose a medical condition.
No
The "Intended Use / Indications for Use" section explicitly states: "The device does not alter the original medical image and is not intended to be used as a diagnostic device." It is described as a triage and notification software to assist with workflow prioritization, not for primary diagnosis.
Yes
The device is described as a "radiological computer-assisted triage and notification software device" and its components are listed as software modules running on off-the-shelf operating systems and applications. There is no mention of proprietary hardware being part of the device itself.
Based on the provided information, this device is not an IVD (In Vitro Diagnostic).
Here's why:
- IVD Definition: In Vitro Diagnostics are tests performed on samples taken from the human body, such as blood, urine, or tissue, to detect diseases, conditions, or infections.
- BriefCase's Function: BriefCase analyzes radiological images (Chest X-Rays), which are external to the body and are not biological samples. Its purpose is to assist in the triage and notification of potential findings within these images, not to perform a diagnostic test on a biological specimen.
- Intended Use: The intended use clearly states it's for analyzing Chest X-Ray cases and assisting in workflow triage by flagging suspect cases. It explicitly states it is not intended to be used as a diagnostic device.
- Device Description: The description details software modules for image acquisition, processing, and workflow integration, all focused on handling and analyzing medical images.
Therefore, BriefCase falls under the category of medical imaging software or a computer-assisted detection/triage device, not an In Vitro Diagnostic.
No
The letter explicitly states "Control Plan Authorized (PCCP) and relevant text: Not Found", indicating no PCCP was approved or cleared.
Intended Use / Indications for Use
BriefCase is a radiological computer aided triage and notification software in the analysis of Chest X-Ray images in adults or transitional adolescents aged 18 and older. The device is intended to assist hospital networks and appropriately trained medical specialists in workflow triage by flagging and communication of suspect positive cases with Pneumothorax (Ptx) findings.
BriefCase uses an artificial intelligence algorithm to analyze images and flag suspect cases on a standalone desktop application in parallel to the ongoing standard of care image interpretation. The user is notified for suspect cases. Notifications include compressed preview images that are meant for informational purposes only and not intended for diagnostic use beyond notification. The device does not alter the original medical image and is not intended to be used as a diagnostic device.
The results of BriefCase are intended to be used in conjunction with other patient information and based on their professional judgment, to assist with triage/prioritization of medical images. Notified clinicians are responsible for viewing full images per the standard of care.
Product codes
QFM
Device Description
BriefCase is a radiological computer-assisted triage and notification software device. The software system is based on an algorithm programmed component and consists of a standard off-the-shelf operating system, the Microsoft Windows server 2012 64bit, and additional applications, which include PostgreSQL, DICOM module and the BriefCase Image Processing Application.
The device consists of the following three modules: (1) Aidoc Hospital Server (AHS) for image acquisition; (2) Aidoc Cloud Server (ACS) for image processing; and (3) Aidoc Worklist Application for workflow integration.
DICOM images are received, saved, filtered and de-identified before processing. Filtration matches metadata fields with keywords. Series are processed chronologically by running the algorithms on each series to detect suspected cases. The software then flags suspect cases by sending passive notifications to the Worklist desktop application, thereby facilitating triage and prioritization by the user. As the BriefCase software platform harbors several triage algorithms, the user may opt to filter out notifications by pathology, e.g., a chest radiologist may choose to filter out notifications on LVO cases, and a neuro-radiologist would opt to divert PE notifications. Where several medical centers are linked to a shared PACS, a user may read cases for a certain center but not for another, and thus may opt to filter out notification by center. Activating the filter does not impact the order in which notifications are presented in the Aidoc worklist application.
The Worklist application displays all incoming suspect cases, each notified case in a line. Hovering over a line in the worklist pops up a compressed, low-quality, grayscale, unannotated image that is captioned "hot for diagnostic use" and is displayed as a preview function. This compressed preview is meant for informational purposes only, does not contain any marking of the findings, and is not intended for primary diagnosis beyond notification.
Presenting the users with worklist prioritization facilitates earlier triage by prompting the user to assess the relevant original images in the PACS. Thus, the suspect case receives attention earlier than would have been the case in the standard of care practice alone.
Mentions image processing
Yes
Mentions AI, DNN, or ML
Mentions artificial intelligence algorithm, deep-learning algorithms, deep learning AI algorithms.
Input Imaging Modality
Chest X-Ray
Anatomical Site
Chest
Indicated Patient Age Range
adults or transitional adolescents aged 18 and older
Intended User / Care Setting
hospital networks and appropriately trained medical specialists
Description of the training set, sample size, data source, and annotation protocol
No patient data were reused between the training and the clinical validation datasets.
Ground truthing was performed by two radiologists with an additional third radiologist to resolve inconsistencies.
Description of the test set, sample size, data source, and annotation protocol
Performance data were collected on an entirely new data set of Pneumothorax images in a retrospective, blinded, multicenter, multinational study. Cases and reports were selected that have not been previously reviewed using BriefCase.
The primary endpoint was to evaluate the software's performance in identifying Pneumothorax in chest Xray exams, in 619 cases from 5 clinical study sites (4 US-based study sites, 1 OUS, 89% of the cases were collected from the US sites). There were 139 positive reports and 480 negative reports (reports on images with Ptx versus without Ptx) included in the analysis.
The mean age of patients whose scans were reviewed in the study was 59.3 years, with standard deviation of 19.8 years. Gender distribution was 51.3% male, and 48.7% female. Ethnicity distribution within the study data patient population was unavailable.
Summary of Performance Studies
Pivotal Study Summary
Study Type: retrospective, blinded, multicenter, multinational study.
Sample Size: 619 cases (139 positive reports, 480 negative reports)
AUC: 0.969 (95% CI: 0.954, 0.985)
Standalone Performance:
Sensitivity: 94.2% (95% CI: 89.9%, 97.8%)
Specificity: 90.8% (95% CI: 88.1%, 93.1%)
NPV: 99.3% (95% CI: 98.6%-99.6%)
PPV: 53.3 % (95% CI: 46.2%-60.3%)
PLR: 10.28% (95% CI: 7.73%-13.67%)
NLR: 0.06 % (95% CI: 0.03%-0.12%)
Key results: As the AUC exceeded 0.95% and sensitivity and specificity both exceeded 80%, the study's primary endpoints were met. Lower confidence limits for AUC, sensitivity and specificity were all above the pre-specified performance goals for all chosen operating points, demonstrating that the pre-specified performance goals were met.
Secondary Endpoint
Study Type: Comparison to predicate device for time-to-notification.
Key results: The BriefCase time-to-notification for Pneumothorax was 13.1 seconds (95% CI: 10.6 -15.7 ; Median 11.8, IQR 15.8), shown to be substantially similar to the predicate red dot™ time-to-notification which was reported to be 13.8 seconds (95% CI: 10.9 - - 13.0 - ; Median 14.5, IQR 8.56). Performance validation data suggest that when using the subject BriefCase, the users may have the same benefit in time saving as with using the red dot.
Key Metrics
AUC: 0.969 (95% CI: 0.954, 0.985)
Sensitivity: 94.2% (95% CI: 89.9%, 97.8%)
Specificity: 90.8% (95% CI: 88.1%, 93.1%)
NPV: 99.3% (95% CI: 98.6%-99.6%)
PPV: 53.3 % (95% CI: 46.2%-60.3%)
PLR: 10.28% (95% CI: 7.73%-13.67%)
NLR: 0.06 % (95% CI: 0.03%-0.12%)
Time-to-notification (Mean estimate): BriefCase 13.1 seconds, Red dotTM 13.8 seconds.
Predicate Device(s)
K191556 Behold.ai red dot™ device
Reference Device(s)
Not Found
Predetermined Change Control Plan (PCCP) - All Relevant Information
Not Found
§ 892.2080 Radiological computer aided triage and notification software.
(a)
Identification. Radiological computer aided triage and notification software is an image processing prescription device intended to aid in prioritization and triage of radiological medical images. The device notifies a designated list of clinicians of the availability of time sensitive radiological medical images for review based on computer aided image analysis of those images performed by the device. The device does not mark, highlight, or direct users' attention to a specific location in the original image. The device does not remove cases from a reading queue. The device operates in parallel with the standard of care, which remains the default option for all cases.(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 notification and triage algorithms and all underlying image analysis algorithms including, but not limited to, a detailed description of the algorithm inputs and outputs, each major component or block, how the algorithm affects or relates to clinical practice or patient care, and any algorithm limitations.
(ii) A detailed description of pre-specified performance testing protocols and dataset(s) used to assess whether the device will provide effective triage (
e.g., improved time to review of prioritized images for pre-specified clinicians).(iii) Results from performance testing that demonstrate that the device will provide effective triage. The performance assessment must be based on an appropriate measure to estimate the clinical effectiveness. The test dataset must contain sufficient numbers of cases from important cohorts (
e.g., subsets defined by clinically relevant confounders, effect modifiers, associated diseases, and subsets defined by image acquisition characteristics) such that the performance estimates and confidence intervals for these individual subsets can be characterized with the device for the intended use population and imaging equipment.(iv) Stand-alone performance testing protocols and results of the device.
(v) 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).(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 user and user training that addresses appropriate use protocols for the device;
(iii) 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 for certain subpopulations), as applicable;(iv) A detailed description of compatible imaging hardware, imaging protocols, and requirements for input images;
(v) Device operating instructions; and
(vi) A detailed summary of the performance testing, including: test methods, dataset characteristics, triage effectiveness (
e.g., improved time to review of prioritized images for pre-specified clinicians), diagnostic accuracy of algorithms informing triage decision, and results with associated statistical uncertainty (e.g., confidence intervals), including a summary of subanalyses on case distributions stratified by relevant confounders, such as lesion and organ characteristics, disease stages, and imaging equipment.
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Image /page/0/Picture/0 description: The image contains the logo of the U.S. Food and Drug Administration (FDA). On the left is the Department of Health & Human Services logo. To the right of that is the FDA logo, which consists of the letters "FDA" in a blue square, followed by the words "U.S. FOOD & DRUG ADMINISTRATION" in blue text.
Aidoc Medical, Ltd. % John J. Smith, M.D., J.D. Partner Hogan Lovells US LLP 555 Thirteenth St. WASHINGTON DC 20004
Re: K214043
March 14, 2022
Trade/Device Name: BriefCase Regulation Number: 21 CFR 892.2080 Regulation Name: Radiological computer aided triage and notification software Regulatory Class: Class II Product Code: QFM Dated: February 28, 2022 Received: February 28, 2022
Dear John Smith:
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 801); medical device reporting of medical device-related adverse events) (21 CFR 803) for
1
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.
Jessica Lamb Assistant Diretor Division of Radiological Health OHT7: Office of In Vitro Diagnostics and Radiological Health Office of Product Evaluation and Quality Center for Devices and Radiological Health
Enclosure
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DEPARTMENT OF HEALTH AND HUMAN SERVICES Food and Drug Administration
Indications for Use
510(k) Number (if known) K214043
Device Name
BriefCase
Indications for Use (Describe)
BriefCase is a radiological computer aided triage and notification software in the analysis of Chest X-Ray images in adults or transitional adolescents aged 18 and older. The device is intended to assist hospital networks and appropriately trained medical specialists in workflow triage by flagging and communication of suspect positive cases with Pneumothorax (Ptx) findings.
BriefCase uses an artificial intelligence algorithm to analyze images and flag suspect cases on a standalone desktop application in parallel to the ongoing standard of care image interpretation. The user is notified for suspect cases. Notifications include compressed preview images that are meant for informational purposes only and not intended for diagnostic use beyond notification. The device does not alter the original medical image and is not intended to be used as a diagnostic device.
The results of BriefCase are intended to be used in conjunction with other patient information and based on their professional judgment, to assist with triage/prioritization of medical images. Notified clinicians are responsible for viewing full images per the standard of care.
Type of Use (Select one or both, as applicable)
区 Prescription Use (Part 21 CFR 801 Subpart D)
□ Over-The-Counter Use (21 CFR 801 Subpart C)
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510(k) Summary Aidoc Medical, Ltd.'s BriefCase K214043
Submitter:
| | Aidoc Medical, Ltd.
92 Yigal Alon St.
Tel-Aviv. Israel
Phone: +972-73-7946870 |
|----------------------|----------------------------------------------------------------------------------------|
| | Contact Person: N. Epstein, Ph.D. |
| Date Prepared: | March 2, 2022 |
| Name of Device: | BriefCase |
| Classification Name: | Radiological computer-assisted triage and notification software device |
| Regulatory Class: | Class II |
| Product Code: | QFM (21.CFR 892.2080) |
| Predicate Device: | K191556 Behold.ai red dot™ device Behold.AI Technologies Limited |
Device Description
BriefCase is a radiological computer-assisted triage and notification software device. The software system is based on an algorithm programmed component and consists of a standard off-the-shelf operating system, the Microsoft Windows server 2012 64bit, and additional applications, which include PostgreSQL, DICOM module and the BriefCase Image Processing Application.
The device consists of the following three modules: (1) Aidoc Hospital Server (AHS) for image acquisition; (2) Aidoc Cloud Server (ACS) for image processing; and (3) Aidoc Worklist Application for workflow integration.
DICOM images are received, saved, filtered and de-identified before processing. Filtration matches metadata fields with keywords. Series are processed chronologically by running the algorithms on each series to detect suspected cases. The software then flags suspect cases by sending passive notifications to the Worklist desktop application, thereby facilitating triage and prioritization by the user. As the BriefCase software platform harbors several triage algorithms, the user may opt to filter out notifications by pathology, e.g., a chest radiologist may choose to filter out notifications on LVO cases, and a neuro-radiologist would opt to divert PE notifications. Where several medical centers are linked to a shared PACS, a user may read cases for a certain center but not for another, and thus may opt to filter out notification by center. Activating the filter does not impact the order in which notifications are presented in the Aidoc worklist application.
The Worklist application displays all incoming suspect cases, each notified case in a line. Hovering over a line in the worklist pops up a compressed, low-quality, grayscale, unannotated image that is captioned "hot for diagnostic use" and is displayed as a preview function. This compressed preview is meant for informational purposes only, does not contain any marking of the findings, and is not intended for primary diagnosis beyond notification.
Presenting the users with worklist prioritization facilitates earlier triage by prompting the user to assess the relevant original images in the PACS. Thus, the suspect case receives attention earlier than would have been the case in the standard of care practice alone.
4
Intended Use / Indications for Use
Brief Case is a radiological computer aided triage and notification software indicated for use in the analysis of Chest X-Ray cases in adults or transitional adolescents aged 18 and older. The device is intended to assist hospital networks and appropriately trained medical specialists in workflow triage by flagging and communication of suspect positive cases with Pneumothorax (Ptx) findings.
BriefCase uses an artificial intelligence algorithm to analyze images and flag suspect cases on a standalone desktop application in parallel to the ongoing standard of care image interpretation. The user is notified for suspect cases. Notifications include compressed preview images that are meant for informational purposes only and not intended for diagnostic use beyond notification. The device does not alter the original medical image and is not intended to be used as a diagnostic device.
The results of BriefCase are intended to be used in conjunction with other patient information and based on their professional judgment, to assist with triage/prioritization of medical images. Notified clinicians are responsible for viewing full images per the standard of care.
Comparison of Technological Characteristics
The subject BriefCase and the predicate red dot™ device (K191556) are nearly identical, as explained below.
Both devices are radiological computer-aided triage and notification software programs. Both devices are artificial intelligence deep-learning algorithms incorporating software packages for use with DICOM 3.0 compliant CT scanners, PACS, and radiology workstations. Both devices are intended to aid in triage and prioritization of radiological images. Both devices operate in parallel to the standard of care workflow in the sense that they do not change the original image, and do not remove cases from the standard of care queue or de-prioritize them. In addition, performance testing demonstrates that both devices have similar performance.
Thus, the subject and predicate devices raise the same types of safety and effectiveness questions, namely, accurate detection of findings within the processed study. It is important to note again that, like the predicate, the subject device neither removes cases from the standard of care reading queue, nor de-prioritize them and does not modify the image. Both devices operate in parallel to the standard of care, which remains the default option for all cases.
A table comparing the key features of the subject and predicate devices is provided below.
5
| Predicate Device | Subject Device | |
|---|---|---|
| Intended Use / | ||
| Indications for | ||
| Use | Behold.ai red dot™ device (K191556) | |
| The red dot™ software platform is a software | ||
| workflow tool designed to aid the clinical | ||
| assessment of adult Chest XRay cases with | ||
| features suggestive of Pneumothorax in the | ||
| medical care environment. red dot™ analyzes | ||
| cases | ||
| using an artificial intelligence algorithm to | ||
| identify suspected findings. It makes case-level | ||
| output available to a PACS/ workstation for | ||
| worklist prioritization or triage. red dot™ is not | ||
| intended to direct attention to specific portions | ||
| of an image or to anomalies other than | ||
| Pneumothorax. Its results are not intended to be | ||
| used on a stand-alone basis for clinical | ||
| decision-making nor is it intended to rule out | ||
| Pneumothorax or otherwise preclude clinical | ||
| assessment of X-Ray cases. | Aidoc Briefcase (K214043) | |
| BriefCase is a radiological computer aided triage | ||
| and notification software indicated for use in the | ||
| analysis of Chest X-Ray cases in adults or | ||
| transitional adolescents aged 18 and older. The | ||
| device is intended to assist hospital networks | ||
| and appropriately trained medical specialists in | ||
| workflow triage by flagging and communication | ||
| of suspect positive cases with Pneumothorax | ||
| (Ptx) findings. | ||
| BriefCase uses an artificial intelligence | ||
| algorithm to analyze images and flag suspect | ||
| cases on a standalone desktop application in | ||
| parallel to the ongoing standard of care image | ||
| interpretation. The user is notified for suspect | ||
| cases. Notifications include compressed preview | ||
| images that are meant for informational purposes | ||
| only and not intended for diagnostic use beyond | ||
| notification. The device does not alter the | ||
| original medical image and is not intended to be | ||
| used as a diagnostic device. | ||
| The results of BriefCase are intended to be used | ||
| in conjunction with other patient information | ||
| and based on their professional judgment, to | ||
| assist with triage/prioritization of medical | ||
| images. Notified clinicians are responsible for | ||
| viewing full images per the standard of care. | ||
| User population | Trained clinicians | Appropriately trained medical specialists |
| Anatomical | ||
| region | Chest | Chest |
| Data | ||
| acquisition | ||
| protocol | Chest X-ray | Chest X-ray |
| Notification- | ||
| only, parallel | ||
| workflow tool | Yes (passive) | Yes |
| Images | ||
| format | DICOM | DICOM |
| Interference | ||
| with standard | ||
| workflow | No. No cases are removed from | |
| Worklist or de-prioritized. | No. No cases are removed from | |
| worklist or de-prioritized | ||
| Algorithm | Artificial intelligence algorithm with database | |
| of images | Artificial intelligence algorithm with database of | |
| images | ||
| Structure | - Image input, validation and anonymization |
- Image Analysis Algorithm
- PACS Integration Feature | - AHS module (image acquisition).
- ACS module (image processing).
- Aidoc Worklist application for workflow
integration (worklist and non-diagnostic
basic Image Viewer). |
Table 1. Key Feature Comparison
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Performance Data
Pivotal Study Summary
Performance data were collected on an entirely new data set of Pneumothorax images in a retrospective, blinded, multicenter, multinational study. Cases and reports were selected that have not been previously reviewed using BriefCase. No patient data were reused between the training and the clinical validation datasets. Ground truthing was performed by two radiologists with an additional third radiologist to resolve inconsistencies.
Primary Endpoint
The primary endpoint was to evaluate the software's performance in identifying Pneumothorax in chest Xray exams, in 619 cases from 5 clinical study sites (4 US-based study sites, 1 OUS, 89% of the cases were collected from the US sites). There were 139 positive reports and 480 negative reports (reports on images with Ptx versus without Ptx) included in the analysis.
The mean age of patients whose scans were reviewed in the study was 59.3 years, with standard deviation of 19.8 years. Gender distribution was 51.3% male, and 48.7% female. Ethnicity distribution within the study data patient population was unavailable. Further, additional characteristics of the data set can be found below:
| Manufacturer | N | % |
|---|---|---|
| Philips | 45 | 7.3% |
| FUJIFILM | ||
| Corporation | 94 | 15.2% |
| Canon Inc. | 97 | 15.7% |
| GE Healthcare | 33 | 5.3% |
| Carestream | 285 | 46.0% |
| Siemens | 65 | 10.5% |
| Total | 619 | 100.0% |
Table 2. Frequency Distribution of Manufacturer
In addition, to make sure that the Pneumothorax negative cases are representative of the intended-use population, for every Report-negative case ("suspected negative" as defined by the case selector, during the case selection phase), the report was reviewed to identify the existence of other pathologies within the scan.
7
| Pathology | N | % |
|---|---|---|
| Fully Negative | 270 | 60.8% |
| Lung Pathologies | 117 | 26.4% |
| Cardiac Pathologies | 15 | 3.4% |
| Vascular | 14 | 3.2% |
| Inflammatory | ||
| (Pneumonia) | 9 | 2.0% |
| Other | 8 | 1.8% |
| Diaphragmatic | ||
| Pathologies | 4 | 0.9% |
| Post-Op | 3 | 0.7% |
| Trauma | 2 | 0.5% |
| Mechanical | 1 | 0.2% |
| Neoplastic | 1 | 0.2% |
Table 3. Distribution of Pathologies in Report-Negative Cases
8
AUC was 0.969 (95% CI: 0.954, 0.985), Sensitivity was 94.2% (95% CI: 89.9%, 97.8%) and Specificity was 90.8% (95% CI: 88.1%, 93.1%). As the AUC exceeded 0.95% and sensitivity and specificity both exceeded 80%, the study's primary endpoints were met.
Three operating points for the BriefCase algorithm were evaluated. Lower confidence limits for AUC, sensitivity and specificity were all above the pre-specified performance goals for all chosen operating points, demonstrating that the pre-specified performance goals were met.
Secondary Endpoint
Briefcase's potential clinical benefit of worklist prioritization for true positive Pneumothorax cases was evaluated by comparing the software's time-to-notification metric to that of the predicate.
The time-to-notification (TTN) metric includes the time to obtain the DICOM exam, de-identify it, upload it to the cloud, analyze and send a notification on a positive suspect case back to the worklist application. The TTN metric was measured for True Positive cases (i.e., identified as positive both by the reviewers as well as the BriefCase device), and the results compared to the predicate are reported in the Table 4 below.
The BriefCase time-to-notification for Pneumothorax was 13.1 seconds (95% CI: 10.6 -15.7 ; Median 11.8, IQR 15.8), thus shown to be substantially similar to the predicate red dot™ time-to-notification which was reported to be 13.8 seconds (95% CI: 10.9 - - 13.0 - ; Median 14.5, IQR 8.56). Furthermore, the predicate demonstrated the benefit of triage compared to the standard of care for pneumothorax.
| Time-to-
notification | Mean
estimate | 95%
Lower CL | 95%
Upper CL | Median | IQR |
|--------------------------|------------------|-----------------|-----------------|--------|------|
| BriefCase | 13.1 | 10.6 | 15.7 | 11.8 | 15.8 |
| Red dotTM | 13.8 | 10.9 | 13.0 | 14.5 | 8.56 |
Table 4 . TTN Comparison Subject & Predicate
NPV was 99.3% (95% CI: 98.6%-99.6%) and PPV was 53.3 % (95% CI: 46.2%-60.3%). PLR was 10.28% (95% CI: 7.73%-13.67%) and NLR was 0.06 % (95% CI: 0.03%-0.12%).
Thus, the reported TTN metric data demonstrates that BriefCase users may have the opportunity to be involved in the clinical workflow of true positive Pneumothorax cases on the same timeframe as the predicate. Performance validation data suggest that when using the subject BriefCase, the users may have the same benefit in time saving as with using the red dot.
Conclusion
The subject BriefCase and the predicate red dot™ devices are both intended to aid in prioritization and triage of radiological images for the indications of Pneumothorax. Both devices are software packages with similar technological characteristics and principles of operation, both incorporating deep learning AI algorithms that process images, to triage of radiological images, independent of standard of care workflow.
Both devices operate in parallel to the standard of care workflow in the sense that they do not change the original image, do not provide any marking on the output preview, and do not remove images from the standard of care FIFO queue, thus not disturbing standard interpretation of the images by the users. In addition, performance testing demonstrates that Briefcase has similar performance compared to the predicate device.
The BriefCase device is thus substantially equivalent to the predicate red dot™.