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.

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.

AI/ML Overview

Here's a breakdown of the acceptance criteria and the study proving the device meets them, based on the provided FDA 510(k) summary for Aidoc Medical, Ltd.'s BriefCase:

1. Table of Acceptance Criteria and Reported Device Performance

Metric	Acceptance Criteria (Pre-specified Performance Goals)	Reported Device Performance (BriefCase)
AUC	Exceeded 0.95	0.969 (95% CI: 0.954, 0.985)
Sensitivity	Exceeded 80%	94.2% (95% CI: 89.9%, 97.8%)
Specificity	Exceeded 80%	90.8% (95% CI: 88.1%, 93.1%)
Time-to-Notification (TP cases)	Substantially similar to predicate (13.8 seconds)	13.1 seconds (95% CI: 10.6 - 15.7; Median 11.8)

2. Sample Size and Data Provenance

Test Set Sample Size: 619 Chest X-ray cases.
Data Provenance: Retrospective, blinded, multicenter, multinational study.
- Country of Origin: 5 clinical study sites (4 US-based, 1 OUS). 89% of cases were collected from US sites.
- Retrospective/Prospective: Retrospective.

3. Number of Experts and Qualifications for Ground Truth

Number of Experts: Two radiologists initially, with an additional third radiologist to resolve inconsistencies.
Qualifications of Experts: Not explicitly stated beyond "radiologists." It implies they are trained medical professionals in radiology.

4. Adjudication Method for the Test Set

Adjudication Method: 2+1 (Two radiologists performed initial ground truthing, and a third radiologist resolved inconsistencies).

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

MRMC Study: No, an MRMC comparative effectiveness study was not performed to assess how human readers improve with AI vs without AI assistance.
Effect Size: Not applicable as no MRMC study was conducted. The study, instead, compares the device's standalone performance to pre-specified metrics and its time-to-notification to that of a predicate device.

6. Standalone (Algorithm Only) Performance

Standalone Performance: Yes, standalone performance (algorithm only without human-in-the-loop) was evaluated against the primary endpoints of AUC, Sensitivity, and Specificity. The results reported (AUC, Sensitivity, Specificity) are for the BriefCase algorithm's performance in identifying Pneumothorax.

7. Type of Ground Truth Used

Type of Ground Truth: Expert consensus of radiologists, adjudicated by a third radiologist.

8. Sample Size for the Training Set

Training Set Sample Size: Not explicitly stated. The document mentions "No patient data were reused between the training and the clinical validation datasets," indicating a separate training set, but its size is not provided in this summary.

9. How Ground Truth for the Training Set was Established

Training Set Ground Truth: Not explicitly detailed in this summary. However, given the methodology for the test set, it is highly probable that similar expert review and consensus methods would have been employed to establish ground truth for the training data to ensure consistency and quality. The document states the algorithm was trained on a "database of images," implying these images were labeled, presumably by experts.

Summary

{0}------------------------------------------------

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

{2}------------------------------------------------

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.

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)

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 numbe

{3}------------------------------------------------

510(k) Summary Aidoc Medical, Ltd.'s BriefCase K214043

Submitter:

	Aidoc Medical, Ltd.92 Yigal Alon St.Tel-Aviv. IsraelPhone: +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

{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.

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 forUse	Behold.ai red dot™ device (K191556)The red dot™ software platform is a softwareworkflow tool designed to aid the clinicalassessment of adult Chest XRay cases withfeatures suggestive of Pneumothorax in themedical care environment. red dot™ analyzescasesusing an artificial intelligence algorithm toidentify suspected findings. It makes case-leveloutput available to a PACS/ workstation forworklist prioritization or triage. red dot™ is notintended to direct attention to specific portionsof an image or to anomalies other thanPneumothorax. Its results are not intended to beused on a stand-alone basis for clinicaldecision-making nor is it intended to rule outPneumothorax or otherwise preclude clinicalassessment of X-Ray cases.	Aidoc Briefcase (K214043)BriefCase is a radiological computer aided triageand notification software indicated for use in theanalysis of Chest X-Ray cases in adults ortransitional adolescents aged 18 and older. Thedevice is intended to assist hospital networksand appropriately trained medical specialists inworkflow triage by flagging and communicationof suspect positive cases with Pneumothorax(Ptx) findings.BriefCase uses an artificial intelligencealgorithm to analyze images and flag suspectcases on a standalone desktop application inparallel to the ongoing standard of care imageinterpretation. The user is notified for suspectcases. Notifications include compressed previewimages that are meant for informational purposesonly and not intended for diagnostic use beyondnotification. The device does not alter theoriginal medical image and is not intended to beused as a diagnostic device.The results of BriefCase are intended to be usedin conjunction with other patient informationand based on their professional judgment, toassist with triage/prioritization of medicalimages. Notified clinicians are responsible forviewing full images per the standard of care.
User population	Trained clinicians	Appropriately trained medical specialists
Anatomicalregion	Chest	Chest
Dataacquisitionprotocol	Chest X-ray	Chest X-ray
Notification-only, parallelworkflow tool	Yes (passive)	Yes
Imagesformat	DICOM	DICOM
Interferencewith standardworkflow	No. No cases are removed fromWorklist or de-prioritized.	No. No cases are removed fromworklist or de-prioritized
Algorithm	Artificial intelligence algorithm with databaseof images	Artificial intelligence algorithm with database ofimages
Structure	- Image input, validation and anonymization- Image Analysis Algorithm- PACS Integration Feature	- AHS module (image acquisition).- ACS module (image processing).- Aidoc Worklist application for workflowintegration (worklist and non-diagnosticbasic Image Viewer).

Table 1. Key Feature Comparison

{6}------------------------------------------------

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%
FUJIFILMCorporation	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%
DiaphragmaticPathologies	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	Meanestimate	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™.

Regulation Number and Section

§ 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.