BriefCase is a radiological computer aided triage and notification software indicated for use in the analysis of chest CTs (with or without contrast) 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 communicating suspect cases of three or more acute Rib fracture (RibFx) pathologies.

BriefCase uses an artificial intelligence algorithm to analyze images and highlight cases with detected findings in parallel to the ongoing standard of care image interpretation. The user is presented with notifications for cases with suspected RibFx findings. 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 is based on an algorithm programmed component and is intended to run on a linux-based server in a cloud environment.

The BriefCase receives filtered DICOM Images, and processes them chronologically by running the algorithms on each series to detect suspected cases. Following the Al processing, the output of the algorithm analysis is transferred to an image review software (desktop application). When a suspected case is detected, the user receives a pop-up notification and is presented with a compressed, lowguality, grayscale image that is captioned "not for diagnostic use, for prioritization only" which is displayed as a preview function. This 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 efficient triaqe 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 study details for the Aidoc BriefCase device, based on the provided text:

Acceptance Criteria and Device Performance

1. A table of acceptance criteria and the reported device performance

Performance Metric	Acceptance Criteria (Pre-specified Performance Goal)	Reported Device Performance (95% Confidence Interval)
Area Under the Curve (AUC)	> 0.95	0.98 (0.966, 0.994)
Sensitivity	> 80%	98.08% (93.23%, 99.77%)
Specificity	> 80%	93.14% (88.75%, 96.20%)
Time-to-Notification (mean)	Comparable to predicate device (K202992)	70.1 seconds (64.9-75.4)

Note: The text also mentions two additional operating points (AOP1 and AOP2) that met specific sensitivity and specificity criteria.

Study Details

2. Sample size used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective)

Test Set Sample Size: 308 cases
Data Provenance: Retrospective, multicenter study from 5 US-based clinical sites. The cases were distinct in time or center from the training data.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts (e.g. radiologist with 10 years of experience)

Number of Experts: Three (3) senior board-certified radiologists.
Qualifications: "Senior board-certified radiologists"

4. Adjudication method (e.g. 2+1, 3+1, none) for the test set

Adjudication Method: Majority voting. (Implies a 3-reader consensus where at least 2 out of 3 agreed).

5. If a multi reader multi case (MRMC) comparative effectiveness study was done, If so, what was the effect size of how much human readers improve with AI vs without AI assistance

The provided text does not describe an MRMC comparative effectiveness study evaluating the improvement of human readers with AI assistance. The study described is a standalone performance validation of the AI algorithm and a comparison of its Time-to-Notification with a predicate device.

6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done

Yes, a standalone performance study was done. The primary endpoint explicitly describes the performance of the algorithm (AUC, Sensitivity, Specificity) compared to the ground truth.

7. The type of ground truth used (expert consensus, pathology, outcomes data, etc)

Type of Ground Truth: Expert consensus, determined by three senior board-certified radiologists using majority voting.

8. The sample size for the training set

The exact sample size for the training set is not explicitly stated. However, the document mentions that the subject device was trained on "a larger data set" compared to the predicate device, and that the pivotal dataset cases were "distinct in time or center from the cases used to train the algorithm."

9. How the ground truth for the training set was established

How the ground truth for the training set was established is not explicitly detailed in the provided text. It only states that the algorithm was "trained on a larger data set." This would typically involve annotated data, but the method of annotation (e.g., expert review, semi-automated) is not described.

Summary

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Aidoc Medical, Ltd. % John J. Smith Partner Hogan Lovells U.S. LLP 555 Thirteenth Street NW WASHINGTON DC 20004

February 1, 2023

Re: K230020

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: January 3, 2023 Received: January 3, 2023

Dear John J. 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

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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,

Jessica Lamb

Jessica Lamb, Ph.D. Assistant Director Imaging Software 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

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DEPARTMENT OF HEALTH AND HUMAN SERVICES Food and Druq Administration

Indications for Use

510(k) Number (if known)

K230020

Device Name

BriefCase

Indications for Use (Describe)

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

K230020

Submitter:

Aidoc Medical, Ltd.92 Yigal Alon St.Tel-Aviv, IsraelPhone:	+972-73-7946870
Contact Person:	Amalia Schreier, LL.M
Date Prepared:	January 3, 2023
Name of Device:	BriefCase
Classification Name:	Radiological computer-assisted triage and notification softwaredevice
Requlatory Class:	Class II
Product Code:	QFM (21 C.F.R. 892.2080)
Primary Predicate Device:	BriefCase (RibFx triage, K202992)

Device Description

BriefCase is a radiological computer-assisted triage and notification software is based on an algorithm programmed component and is intended to run on a linux-based server in a cloud environment.

Intended Use / Indications for Use

BriefCase is a radiological computer aided triage and notification software indicated for use in the analysis of chest CTs (with or without contrast) 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 communicating suspect cases of three or more acute Rib fracture (RibFx) pathologies.

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BriefCase uses an artificial intelligence algorithm to analyze images and highlight cases with detected findings in parallel to the ongoing standard of care image interpretation. The user is presented with notifications for cases with suspected RibFx findings. 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 triaqe/prioritization of medical images. Notified clinicians are responsible for viewing full images per the standard of care.

Comparison of Technological Characteristics

The subject BriefCase for RibFx triage and the algorithm analysis module for the primary predicate BriefCase for RibFx triage (K202992) are identical in most aspects and differ with respect to their algorithm performance, due to training the subject device on a larger data set. Additional operating points have been added to the pivotal study endpoints. In addition, the SW architecture was changed to separate the image communication platform from the BriefCase SW. The new device consists of only the algorithm analysis module which can be integrated with image communication platforms that meet the BriefCase input and output requirements.

Both the primary predicate and subject devices are radiological computer-aided triage and notification software programs. Both devices are artificial intelligence, deep-learning algorithms incorporated in software packages for use with DICOM compliant CT scanners, PACS, and radiology workstations.

Both devices are intended to aid in triage and prioritization of radiological images and utilize the same design of deep learning algorithm trained on medical images. Both devices are intended to provide the specialists with notifications and unannotated, compressed, low-quality, and grayscale preview images of suspect studies for the purpose of preemptive triage.

The subject and predicate BriefCase devices raise the same types of safety and effectiveness questions, namely, accurate detection of findings within the processed study. It is important to note that, like the predicate, the subject device neither removes cases from the standard of care reading queue nor deprioritized cases. Both devices operate in parallel with the standard of care, which remains the default option for all cases. A table comparing the key features of the subject and the primary predicate devices is provided below.

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	Predicate DeviceAidoc Briefcase for RibFx triage(K202992)	Subject DeviceAidoc Briefcase for RibFx triage
Intended Use /Indications forUse	BriefCase is a radiological computeraided triage and notification softwareindicated for use in the analysis of chestCTs (with or without contrast). Thedevice is intended to assist hospitalnetworks and appropriately trainedmedical specialists in workflow triageby flagging and communication ofsuspect cases of three or more acuteRib fracture (RibFx) pathologies.BriefCase uses an artificial intelligencealgorithm to analyze images and flagsuspect cases on a standalone desktopapplication in parallel to the ongoingstandard of care image interpretation.The user is presented with notificationsfor suspect cases. Notifications includecompressed preview images that aremeant for informational purposes onlyand not intended for diagnostic usebeyond notification. The device doesnot alter the original medical image andis not intended to be used as adiagnostic device.The results of BriefCase are intended tobe used in conjunction with otherpatient information and based on theirprofessional judgment, to assist withtriage/prioritization of medical images.Notified clinicians are responsible forviewing full images per the standard ofcare.	BriefCase is a radiological computeraided triage and notification softwareindicated for use in the analysis of chestCTs (with or without contrast) images inadults or transitional adolescents aged18 and older. The device is intended toassist hospital networks andappropriately trained medicalspecialists in workflow triage byflagging and communicating suspectcases of three or more acute Ribfracture (RibFx) pathologies.BriefCase uses an artificial intelligencealgorithm to analyze images andhighlight cases with detected findings inparallel to the ongoing standard of careimage interpretation. The user ispresented with notifications for caseswith suspected RibFx findings.Notifications include compressedpreview images that are meant forinformational purposes only and notintended for diagnostic use beyondnotification. The device does not alterthe original medical image and is notintended to be used as a diagnosticdevice.The results of BriefCase are intended tobe used in conjunction with otherpatient information and based on theirprofessional judgment, to assist withtriage/prioritization of medical images.Notified clinicians are responsible forviewing full images per the standard ofcare.
User population	Hospital networks and appropriatelytrained medical specialists	Hospital networks and appropriatelytrained medical specialists
	Predicate DeviceAidoc Briefcase for RibFx triage(K202992)	Subject DeviceAidoc Briefcase for RibFx triage
Anatomical region of interest	Chest	Chest
Data acquisition protocol	Chest CTs (with or without contrast)	Chest CTs (with or without contrast)
Notification-only(/notification alerts), parallel workflow tool	Yes	Yes
Images format	DICOM	DICOM
Interference with standard workflow	No. No cases are removed from Worklist or deprioritized.	No. No cases are removed from desktop app or deprioritized
Inclusion/ Exclusion criteria for clinical performance testing	Inclusion criteria- Chest CTs (with or without contrast).- Single energy exams.- Scans performed with a 64 or greater number of detectors.- Scans performed on adults/transitional adults ≥ 18 years of age.- Slice thickness; 0.5 - 5.0 mm axial.Exclusion Criteria- All studies that are technically inadequate, including studies with motion artifacts, severe metal artifacts, or inadequate field of view.	Inclusion criteria- chest CT with or without contrast- Single energy exams.- Performed on CT scanners with 64 or greater number of detectors- Scans performed on adults/transitional adults ≥ 18 years of age- Slice thickness; 0.5 mm to 5.0 mm axial slicesExclusion Criteria- All studies that are technically inadequate, including studies with motion artifacts, severe metal artifacts, or inadequate field of view.
Algorithm	Artificial intelligence algorithm with database of images.	Artificial intelligence algorithm with database of images.
Structure	- AHS module (image acquisition);- ACS module (image processing));- Aidoc Worklist application for workflow integration (worklist and non-diagnostic Image Viewer).	- Integrated with image routing module via image communication platform (ICP) (image acquisition).- Algorithm module (image processing)- Integrated with desktop application for workflow integration (feed and non-diagnostic Image Viewer)

Table 1. Key Feature Comparison

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Performance Data

Pivotal Study Summary

Aidoc conducted a retrospective, blinded, multicenter, study with the BriefCase software to evaluate the software's performance in identifying Chest CTs (with or without contrast) images containing three or more acute Rib fractures (RibFx) in 308 cases from 5 US-based clinical sites. The study compared the software's performance to the ground truth, as determined by three senior board-certified radiologists, using majority voting. The cases collected for the pivotal dataset were all distinct in time or center from the cases used to train the algorithm.

Primary endpoints were pre-specified standalone performance goal (PG) of area under the curve (AUC) > 0.95 for the study level receiver operating characteristic (ROC) curve. Secondary endpoints were BriefCase time-to-notification compared to the predicate device. Positive Value (PPV), Negative Predictive Value (NPV), Positive Likelihood Ratio (PLR), and Negative Likelihood Ratio (NLR) were also assessed.

Primary Endpoint

AUC was 0.98 (95% CI: 0.966, 0.994), Sensitivity was 98.08% (95% CI: 93.23%, 99.77%) and Specificity was 93.14% (95% Cl: 88.75%, 96.20%). 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, demonstrating that the pre-specified performance goals were met.

Secondary Endpoint

In addition, the time-to-notification metric observed for the BriefCase software, when integrated with a compatible image communication platform, was compared to the equivalent metric of the predicate devices. The BriefCase time-to-notification includes the time to get the DICOM exam, de-identify it, upload it to the cloud, analyze and send a notification on a positive suspect case back to the desktop application.

The BriefCase time-to-notification was measured for all True Positive cases (i.e., identified as positive both by the reviewers as well as the BriefCase device) and is given in Table 2 below. The Table also displays the same metric reported for the predicate BriefCase RibFx.

The time-to-notification results obtained for the subject BriefCase device show comparability with the primary predicate with regard to time savings to the standard of care review. The BriefCase mean time-to-notification for the subject RibFx triage was 70.1 seconds (95% CI: 64.9-75.4). The time-tonotification for the predicate RibFx triage was 252 seconds (95% Cl: 234-270).

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Table 2. Time-to- notification comparison for BriefCase devices (Seconds)
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Time -to-notification	MeanEstimate(seconds)	N	95% LowerCL	95% UpperCL	Median	IQR
Predicate K202992Processing Time	252	67	234	270	252	108
BriefCase + ImageCommunicationPlatform Time-To-Notification	70.1	104	64.9	75.4	66	59

NPV was 99.8% (95% Cl: 99.1% - 99.9%) and PPV was 61.4% (95% Cl: 48.9% - 72.5%).

PLR was 14.2912 (95% Cl: 8.614 - 23.710) and NLR was 0.0206 (95% Cl: 0.005 - 0.082).

Thus, the reported similar time-to-notification data demonstrates that when using the subject BriefCase for RibFx triage the clinician may have the same benefit in time saving as with the predicate BriefCase for RibFx triage.

As can be seen in Table 3 the mean age of patients whose scans were reviewed for RibFx was 65.1 years, with a standard deviation of 15.5 years. Gender distribution was 51.3% male, and 45.5% female (Table 4). Scanner distribution can also be found in Table 5 below.

	Table 3. Descriptive Statistics for Age
--	-----------------------------------------	--	--

	Mean	Std	Min	Median	Max	N
Age(Years)	65.1	15.5	20	66	90	308

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	Gender
Ground Truth Results	Female	Male	All
	N	%	N	%	N	%
Positive	36	12.1%	61	20.5%	97	32.6%
Negative	104	34.9%	97	32.6%	201	67.4%
All	140	47.0%	158	53.0%	298	100.0%

Table 4. Frequency Distribution of Gender *

10 cases were unknown for gender (3 negative and 7 positive).

Table 5. Frequency Distribution of Manufacturer

Manufacturer	N	%
GE MEDICALSYSTEMS	150	48.7%
Philips	64	20.8%
SIEMENS	53	17.2%
TOSHIBA	41	13.3%
Total	308	100%

Clinical Subgroups and Confounders:

Pathologies present in negative; Neoplastic; Heart & vascular; Chronic diseases; Inflammatory; Trauma; None of the above.

Additional Operating Points:

In addition to the default operating point that was selected to maximize both sensitivity and specificity, two additional operating points (AOP) were selected to maximize specificity while maintaining a lower bound 95% confidence interval of 80% for sensitivity and spectively:

AOP1: Sensitivity was 88.46% (95% C1: 80.71%, 93.89%) and specificity was 95.1% (95% C1: 91.17%, 97.62%).

AOP2: Sensitivity was 99.04% (95% Cl: 94.76%, 99.98%) and specificity was 90.20% (95% Cl: 85.27%, 93.91%).

In summary, performance goals were achieved for the default and two additional operating points.

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Combined with the comparison results of time-to-notification metric with the predicate device, these data establish the achievement by the subject BriefCase of preemptive triage in the range of several minutes.

Conclusions

The subject BriefCase for RibFx triage and the predicate BriefCase for RibFx triage are intended to aid in prioritization and triage of radiological images for the indications for suspected positive findings of Rib Fracture (RibFx) pathologies. Both devices are software packages consisting of deep learning Al algorithms that process images and produce analysis results, which are displayed to the user by a prioritization alert and a compressed, low-quality, grayscale, unannotated preview image. In both devices, the labeling clearly states that the devices are not for diagnostic use and instructs the user to further evaluate and diagnose based only on the original images in the local PACS.

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, do not remove images from the standard of care FIFO queue and do not de-prioritize cases, thus not disturbing standard interpretation of the images. Both devices notify the radiologist of time-sensitive critical cases within the range of several minutes, and thus contribute similarly to the standard of care workflow turnaround time reduction through preemptive triage.

The subject BriefCase device for RibFx triage is thus substantially equivalent to the primary predicate BriefCase for RibFx.

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.