BriefCase-Quantification of Midline Shift (MLS) is a radiological image management and processing system software intended for automatic measurement of brain midline shift in non-contrast head CT (NCCT) images, in adults or transitional adolescents aged 18 years and older.

The device is intended to assist appropriately trained medical specialists by providing the user with an automated current manual process of measuring midline shift.

The device provides midline shift measurement from NCCT images acquired at a single time point, and can additionally provide an output with comparative analysis of two or more images that were acquired in the same individual at multiple time points.

The device does not alter the original medical image and is not intended to be used as a diagnostic device. The BriefCase-Quantification results are not intended to be used on a stand-alone basis for clinical decision-making or otherwise preclude clinical assessment of cases. Clinicians are responsible for viewing full images per the standard of care.

Device Description

BriefCase-Quantification is a radiological image management and processing device. The software consists of a single module based on an algorithm programmed component and is intended to run on a linux-based server in a cloud environment.

The BriefCase-Quantification receives filtered DICOM Images, and processes them chronologically by running the algorithm on relevant series to quantify the extent of midline shift. Following the Al processing, the output of the algorithm analysis is transferred to an image review software (the PACS or a desktop application).

The device generates a summary report that includes a preview image of the slice with the largest midline shift. The preview image includes the measured shift, the annotation of the midline, and the annotation of the largest perpendicular distance between the midline and septum pellucidum. Also, the summary report includes a table and a graph showing the measured midline shift over time for patients with multiple scans.

AI/ML Overview

Here's a detailed breakdown of the acceptance criteria and study information for the BriefCase-Quantification device, based on the provided document:

Acceptance Criteria and Device Performance

Criteria	Acceptance Criteria	Reported Device Performance
Primary Endpoint: Mean Absolute Error (MAE)	Mean absolute error estimate must be lower than prespecified performance goal.	0.94 mm (95% CI: 0.74 mm, 1.14 mm) (Lower than prespecified goal)
Secondary Endpoint: Bias (Bland-Altman plot)	Little to no bias between ground truth and algorithm output.	Mean difference of -0.15 mm (Little to no bias)
Secondary Endpoint: MAE for multiple time points (First Case)	Mean absolute error estimate must be lower than prespecified performance goal.	1.16 mm (95% CI: 0.61 mm, 1.71 mm) (Lower than prespecified goal)
Secondary Endpoint: MAE for multiple time points (Follow-up Cases)	Mean absolute error estimate must be lower than prespecified performance goal.	1.28 mm (95% CI: 0.68 mm, 1.88 mm) (Lower than prespecified goal)

Study Details

Sample size for the test set and data provenance:
- Sample Size: 284 cases from 228 unique patients.
- Data Provenance: Retrospective, multi-center study from 6 US-based clinical sites (both academic and community centers). The cases were distinct in time or center from the cases used to train the algorithm, indicating independent test data.
Number of experts used to establish the ground truth for the test set and the qualifications of those experts:
- Number of Experts: Three neuroradiologists.
- Qualifications: The document states "appropriately trained medical specialists" and specifically "three neuroradiologists," implying they are qualified experts in the field. Specific experience (e.g., "10 years of experience") is not provided.
Adjudication method for the test set:
- Method: The reference standard (ground truth) was created as the mean of all three independent measurements by the neuroradiologists. This implies a "consensus by average" approach rather than a specific 2+1 or 3+1 voting method.
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:
- MRMC Study: No, an MRMC comparative effectiveness study was not explicitly stated as performed with human readers and AI assistance. The study described focuses on the standalone performance of the AI algorithm against a neuroradiologist-established ground truth.
- Effect Size of Human Improvement with AI: This information is not provided because an MRMC study comparing human readers with and without AI assistance was not detailed.
If a standalone (i.e., algorithm only without human-in-the-loop performance) was done:
- Standalone Performance: Yes, a standalone performance study was done. The reported performance metrics (MAE, Bland-Altman) directly compare the algorithm's output to the ground truth established by experts, without human intervention in the device's measurement process. The device is intended to assist specialists by providing an automated process, but its performance evaluation here is purely algorithmic.
The type of ground truth used:
- Ground Truth Type: Expert consensus. Specifically, the "mean of all three [neuroradiologist] measurements."
The sample size for the training set:
- Training Set Sample Size: Not explicitly stated. The document only mentions that the "cases collected for the pivotal dataset were all distinct in time or center from the cases used to train the algorithm."
How the ground truth for the training set was established:
- Training Set Ground Truth: Not explicitly stated. Given the nature of a supervised learning algorithm, it is implied that the training data also had established ground truth measurements, likely derived through expert review, but the specific method or number of experts for the training data is not detailed in this summary.

Summary

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Aidoc Medical, Ltd. % John Smith Partner Hogan & Lovells U.S. LPP 555 Thirteenth Street NW Washington, DC 20004

Re: K232083

November 13, 2023

Trade/Device Name: BriefCase-Quantification Regulation Number: 21 CFR 892.2050 Regulation Name: Medical Image Management And Processing System Regulatory Class: Class II Product Code: QIH Dated: October 17, 2023 Received: October 17, 2023

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 (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).

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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 change requires premarket review. the OS 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,

Jessica Lamb Assistant Director DHT8B: Division of Radiologic 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 Drug Administration

Indications for Use

510(k) Number (if known)

K232083

Device Name

BriefCase-Quantification

Indications for Use (Describe)

The device is intended to assist appropriately trained medical specialists by providing the user with an automated current manual process of measuring midline shift.

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) Summarv Aidoc Medical, Ltd.'s BriefCase-Quantification K232083

Submitter:

Aidoc Medical, Ltd.3 Aminadav St.Tel-Aviv, IsraelPhone:	+972-73-7946870
Contact Person:	Amalia Schreier, LL.M.
Date Prepared:	November 10, 2023
Name of Device:	BriefCase-Quantification
Classification Name:	Medical image management and processing system
Regulatory Class:	Class II
Product Code:	QIH (21 CFR 892.2050)
Primary Predicate Device:	qER-Quant (K211222)

Device Description

Intended Use / Indications for Use

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The device is intended to assist appropriately trained medical specialists by providing the user with an automated current manual process of measuring midline shift.

Comparison of Technological Characteristics

The subject BriefCase-Quantification of Midline Shift (MLS) is substantially equivalent to the predicate qER-Quant (K211222), as explained below.

The subject device and the predicate device are both radiological image management and processing system software. Both devices are artificial intelligence, deep-learning algorithms incorporating software packages for use with compliant scanners, PACS, and radiology workstations. The predicate qER-Quant evaluates images from CT scanners as does the proposed device for BriefCase-Quantification of Midline Shift (MLS). The predicate and subject devices differ in the fact that the predicate device analyzes additional two brain structures, which are Intracranial Hyperdensities and Lateral Ventricles.

The proposed device for BriefCase-Quantification of Midline Shift (MLS) has similar technology and design as the primary predicate device, and similar indications for use as both devices are intended to aid in automation of the current manual process of measuring midline shift. The subject and predicate qER-Quant devices raise the same types of safety and effectiveness questions. A table comparing the key features of the subject device and the predicate device is provided below.

	Predicate DeviceqER-Quant (K211222)	BriefCase-Quantification of MidlineShift (MLS)
Intended Use / Indicationsfor Use	The qER-Quant device is intended forautomatic labeling, visualization andquantification of segmentable brainstructures from a set of Non-Contrasthead CT (NCCT) images. The software isintended to automate the current manualprocess of identifying, labeling andquantifying the volume of segmentablebrain structures identified on NCCTimages.	BriefCase-Quantification of Midline Shift(MLS) is a radiological imagemanagement and processing systemsoftware intended for automaticmeasurement of brain midline shift innon-contrast head CT (NCCT) images,in adults or transitional adolescentsaged 18 years and older.
	Predicate DeviceqER-Quant (K211222)	BriefCase-Quantification of MidlineShift (MLS)
	qER-Quant provides volumes from NCCTimages acquired at a single time point andprovides a table with comparativeanalysis for two or more images that wereacquired on the same scanner with thesame image acquisition protocol for thesame individual at multiple time points.	The device is intended to assistappropriately trained medical specialistsby providing the user with an automatedcurrent manual process of measuringmidline shift.
	The qER-Quant software is indicated foruse in the analysis of the followingstructures: Intracranial Hyperdensities,Lateral Ventricles and Midline Shift.	The device provides midline shiftmeasurement from NCCT imagesacquired at a single time point, and canadditionally provide an output withcomparative analysis of two or moreimages that were acquired in the sameindividual at multiple time points.
		The device does not alter the originalmedical image and is not intended to beused as a diagnostic device. TheBriefCase-Quantification results are notintended to be used on a stand-alonebasis for clinical decision-making orotherwise preclude clinical assessmentof cases. Clinicians are responsible forviewing full images per the standard ofcare.
Anatomical region of interest	Brain	Brain
Target structures analyzedon NCCT scans	Midline shift, Intracranial hyperdensities,and lateral ventricles	Midline shift
Data acquisition protocol	Non-contrast head CT (NCCT) images	Non-contrast head CT (NCCT) images
Midline Shift Measurement	Yes	Yes
Interference with standardworkflow	No	No
Time point	Single or multiple time points	Single or multiple time points
Output	Multiple electronic reports with volumetricinformation of brain structures and midlineshift and Annotated DICOM Images	A Summary report with measurementinformation of midlineshiftandannotated images
	Predicate DeviceqER-Quant (K211222)	BriefCase-Quantification of MidlineShift (MLS)

Algorithm	Artificial intelligence algorithm with database of images.	Artificial intelligence algorithm with database of images.
Structure	- The qER-Quant software interacts with the user's picture archiving and communication system (PACS) to receive scans and returns the results to the same destination.- The core processing component is coupled with a pre-processing module to prepare input digital imaging and communications in medicine (DICOMS) for processing by the CNNs and a post-processing module to convert the output into visual and tabular output for users.	- BriefCase-Quantification is hosted on a cloud server and analyzes applicable CT images that are acquired on CT scanner that are forwarded to BriefCase-Quantification- The results of the analysis are exported and presented to medical specialists for review , to assist in the measurement of MLS.

Table 1: Key Feature Comparison

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Performance Data

Pivotal Study Summary

Aidoc conducted a retrospective, blinded, multicenter, study with the BriefCase-Quantification software to evaluate the software's performance in providing adequate measurements of the midline shift in non-contrast head CT images in 284 cases from 228 unique patients from 6 US-based clinical sites, both academic and community centers, compared to the ground truth, as determined by three neuroradiologists, who independently measured the midline shift, the reference standard was created as the mean of all three measurements. The cases collected for the pivotal dataset were all distinct in time or center from the cases used to train the algorithm.

Primary Endpoints

The algorithm performance showed that the mean absolute error between the ground truth measurement and algorithm was 0.94 mm (95% Cl: 0.74 mm, 1.14 mm) mm. Because the mean absolute error estimate is lower than the prespecified performance goal, the study's primary endpoint was achieved.

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Secondary Endpoints

A Bland-Altman plot demonstrated an agreement between the ground truth compared to the algorithm output. The mean difference between the two measurements was -0.15 mm, indicating that there is little to no bias between the two measurements, demonstrating the study's secondary endpoint was achieved.

The mean absolute error in midline shift between the reference for multiple time point testing for the same patient was 1.16 mm (95% Cl: 0.61 mm, 1.71 mm) for the first case and 1.28 mm (95% Cl: 0.68 mm, 1.88 mm) for the follow-up cases. Because the mean absolute error estimate is lower than the prespecified performance goal, the study's secondary endpoint was achieved.

Thus, the reported similar mean absolute error [the subject device: 0.94 (1.54) mm 0.51 (0.09 - 1.75) mm; the predicate device: 1.37 (1.23) mm 1.15 (0.23 - 2.59) mm] demonstrates that when using the subject BriefCase-Quantification of Midline Shift (MLS) the radiologists may have the same benefits with the qER-Quant.

As can be seen in Table 2 the mean age of patients whose scans were reviewed for BriefCase-Quantification of Midline Shift (MLS) was 64.4 years, with a standard deviation of 20.1 years. Gender distribution was 48.3% male, and 48.8% female (Table 3). Scanner distribution can also be found in Table 4 below.

Table 2: Descriptive Statistics for Age

	Mean	Std	Min	Median	Max	N
Age(Years)	64.4	20.2	18	68	90	228

Table 3: Frequency Distribution of Gender

Gender	N*	%
Male	110	48.3%
Female	111	48.8%

7 cases had unknown gender defined in the DICOM metadata header and were excluded from the gender distribution

Table 4: Frequency Distribution of Manufacturer

Manufacturer	N	%
Siemens	47	20.6%
GE	58	25.4%

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Manufacturer	N	%
Philips	65	28.5%
Toshiba	58	25.4%
Total	228	100%

Clinical Subgroups and Confounders: Heart and Vascular, Chronic diseases, Neoplasm, Trauma, Inflammatory, None of the above and Fully Negative.

In summary, performance validation data, combined with a comparison of overall agreement metric with the predicate device demonstrated equivalent performance.

Conclusions

The subject BriefCase-Quantification of Midline Shift (MLS) and the predicate qER-Quant are intended to aid in medical image management and processing of radiological images of the brain. The subject and predicate devices are both software devices with similar technological characteristics and principles of operation, incorporating deep learning AI algorithms that process images. The predicate and subject devices both provide an automated current manual process of measuring midline shift. Both devices provide a summary report and quantitative measurements from NCCT images acquired at a single time point and provide a table with comparative analysis for two or more images that were acquired for the same individual at multiple time points. The BriefCase-Quantification of Midline Shift (MLS) device is thus substantially equivalent to the qER-Quant device (K211222).

Regulation Number and Section

§ 892.2050 Medical image management and processing system.

(a)
Identification. A medical image management and processing system is a device that provides one or more capabilities relating to the review and digital processing of medical images for the purposes of interpretation by a trained practitioner of disease detection, diagnosis, or patient management. The software components may provide advanced or complex image processing functions for image manipulation, enhancement, or quantification that are intended for use in the interpretation and analysis of medical images. Advanced image manipulation functions may include image segmentation, multimodality image registration, or 3D visualization. Complex quantitative functions may include semi-automated measurements or time-series measurements.(b)
Classification. Class II (special controls; voluntary standards—Digital Imaging and Communications in Medicine (DICOM) Std., Joint Photographic Experts Group (JPEG) Std., Society of Motion Picture and Television Engineers (SMPTE) Test Pattern).