Annalise Enterprise is a device designed to be used in the medical care environment to aid in triage and prioritization of studies with features suggestive of the following finding: mass effect . The device analyzes studies using an artificial intelligence algorithm to identify the finding. It makes study-level output available to an order and imaging management system for worklist prioritization or triage. The device is not intended to direct attention to specific portions of an image and only provides notification for the suspected finding. Its results are not intended: to be used on a standalone basis for clinical decision making ● ● to rule out specific findings, or otherwise preclude clinical assessment of CTB studies

Device Description

Annalise Enterprise CTB Triage Trauma is a software workflow tool which uses an artificial intelligence (AI) algorithm to identify suspected findings on non-contrast brain CT studies in the medical care environment. The findings identified by the device include mass effect. Radiological findings are identified by the device using an AI algorithm - a convolutional neural network trained using deep-learning techniques. Images used to train the algorithm were sourced from datasets that included a range of equipment manufacturers and models. The performance of the device's AI algorithm was validated in a standalone performance evaluation, in which the case-level output from the device was compared with a reference standard ('ground truth'). This was determined by two ground truthers, with a third truther used in the event of disagreement. All truthers were US board-certified neuroradiologists. The device interfaces with image and order management systems (such as PACS/RIS) to obtain noncontrast brain CT studies for processing by the AI algorithm. Following processing, if any of the radiological findings of interest are identified in a non-contrast brain CT study, the device provides a notification to the image and order management system for prioritization of that study in the worklist. This enables users to review the studies containing features suggestive of these radiological findings earlier than in the standard clinical workflow. It is important to note that the device will never decrease a study's existing priority in the worklist. This ensures that worklist items will never have their priorities downgraded based on AI results. The device workflow is performed parallel to and in conjunction with the standard clinical workflow for interpretation of non-contrast brain CTs. The device is intended to aid in prioritization and triage of radiological medical images only.

AI/ML Overview

Here is a summary of the acceptance criteria and study proving device performance, based on the provided text:

Device Name: Annalise Enterprise CTB Triage Trauma
Manufacturer: Annalise-AI Pty Ltd.

1. Table of Acceptance Criteria and Reported Device Performance

The document does not explicitly state "acceptance criteria" for sensitivity and specificity in a separate table. Instead, it provides the results of the standalone performance study in a table, implying these values meet the unstated criteria for demonstrating safety and effectiveness. The comparison section further states that these results are "substantially equivalent to those of the predicate device."

Finding	Slice Thickness Range	Operating Point	Sensitivity % (Se) (95% CI)	Specificity % (Sp) (95% CI)
Mass Effect	≤1.5mm	0.160195	97.0 (95.3, 98.4)	88.7 (83.5, 94.0)
	≤1.5mm	0.221484	96.6 (94.9, 98.2)	89.5 (84.2, 94.0)
Mass Effect	>1.5mm & ≤5.0mm	0.120944	96.8 (95.3, 98.1)	89.3 (84.5, 93.5)
		0.160195	95.3 (93.6, 97.0)	92.9 (88.7, 96.4)

Additional Performance Metric (Triage Effectiveness):

Triage Turn-around Time: 81.6 seconds (95% CI: 80.3 - 82.9)

2. Sample Size Used for the Test Set and Data Provenance

Sample Size:
- For slice thickness ≤1.5mm: 626 cases (493 positive, 133 negative for mass effect)
- For slice thickness >1.5mm & ≤5.0mm: 762 cases (594 positive, 168 negative for mass effect)
Data Provenance: Retrospective, anonymized study. Collected consecutively from five US hospital network sites.

3. Number of Experts Used to Establish the Ground Truth for the Test Set and Qualifications

Number of Experts: At least two neuroradiologists for initial review, with a third neuroradiologist for adjudication in case of disagreement.
Qualifications: All experts were US board-certified neuroradiologists, ABR-certified, and protocol-trained.

4. Adjudication Method for the Test Set

Method: Consensus determined by two ground truthers. If there was a disagreement between the initial two ground truthers, a third ground truther was used to reach consensus (2+1 method).

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study Was Done

An MRMC study was not explicitly described as being performed for AI-assisted human reader performance improvement.
The performance assessment focused on standalone performance of the AI algorithm and a triage effectiveness (turn-around time) study, which was an internal bench study. The document states the AI "enables users to review the studies containing features suggestive of these radiological findings earlier than in the standard clinical workflow," implying an effect on human workflow, but doesn't quantify reader improvement in an MRMC setting.

6. If a Standalone (i.e., algorithm only without human-in-the-loop performance) Was Done

Yes, a standalone performance evaluation was done. The sections under "Performance Testing" describe comparing the device's case-level output with a reference standard (ground truth). The results in the table above (Sensitivity, Specificity) are from this standalone performance evaluation.

7. The Type of Ground Truth Used

Type: Expert consensus (from multiple board-certified neuroradiologists).

8. The Sample Size for the Training Set

The document states, "The test dataset used during the standalone performance evaluation was newly acquired and independent from the training dataset used in model development."
The sample size of the training set is not explicitly provided in the given text. It only mentions that "Images used to train the algorithm were sourced from datasets that included a range of equipment manufacturers and models."

9. How the Ground Truth for the Training Set Was Established

The document does not explicitly describe how the ground truth for the training set was established. It only details the ground truth establishment for the test set used in the standalone performance evaluation.

Summary

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Annalise-AI Pty Ltd. % Haylee Bosshard Regulatory Affairs Manager Level P, 24 Campbell St. Sydney, New South Wales 2000 AUSTRALIA

September 22, 2023

Re: K231384

Trade/Device Name: Annalise Enterprise CTB Triage Trauma Regulation Number: 21 CFR 892.2080 Regulation Name: Radiological computer aided triage and notification software Regulatory Class: Class II Product Code: QAS Dated: August 30, 2023 Received: August 30, 2023

Dear Haylee Bosshard:

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

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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 Team DHT8B: Division of Radiological Imaging Devices and Electronic Products OHT8: Office of Radiological Health Office of Product Evaluation and Quality Center for Devices and Radiological Health

Enclosure

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Indications for Use

510(k) Number (if known)

K231384

Device Name Annalise Enterprise CTB Triage Trauma

Indications for Use (Describe)

Intended context:

Annalise Enterprise is a device designed to be used in the medical care environment to aid in triage and prioritization of studies with features suggestive of the following finding:

• mass effect

The device analyzes studies using an artificial intelligence algorithm to identify the finding. It makes study-level output available to an order and imaging management system for worklist prioritization or triage.

The device is not intended to direct attention to specific portions of an image and only provides notification for the suspected finding.

Its results are not intended:

· to be used on a standalone basis for clinical decision making
· to rule out a specific finding, or otherwise preclude clinical assessment of CTB studies

Intended modality: Annalise Enterprise identifies the suspected finding in non-contrast brain CT studies.

Intended user:

The device is intended to be used by trained clinicians who, as part of their scope of practice, are qualified to interpret brain CT studies.

Intended patient population: The intended population is patients who are 22 years or older.

Type of Use (Select one or both, as applicable)

X Prescription Use (Part 21 CFR 801 Subpart D)

Over-The-Counter Use (21 CFR 801 Subpart C)

CONTINUE ON A SEPARATE PAGE IF NEEDED.

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K231384 510(k) Summary

SUBMITTER I.

Company Name	Annalise-AI Pty Ltd
Address	Level P, 24 Campbell StreetSydney, NSW 2000Australia
Phone Number	+61 1800-958487
Contact Person	Haylee Bosshard
Date Prepared	September 21, 2023

II. SUBJECT DEVICE

Manufacturer Name	Annalise-AI Pty Ltd
Device Name	Annalise Enterprise CTB Triage Trauma
Classification Name	Radiological computer aided triage and notification software(21CFR892.2080)
Regulatory Class	II
Product Code	QAS

PREDICATE DEVICE III.

Manufacturer Name	Nines, Inc.
Device Name	NinesAI
510(k) reference	K193351
Classification Name	Radiological computer aided triage and notification software(21CFR892.2080)
Regulatory Class	II
Product Code	QAS

This predicate has not been subject to a design-related recall. No reference devices were used in this submission.

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DEVICE DESCRIPTION IV.

Radiological findings are identified by the device using an AI algorithm - a convolutional neural network trained using deep-learning techniques. Images used to train the algorithm were sourced from datasets that included a range of equipment manufacturers and models. The performance of the device's AI algorithm was validated in a standalone performance evaluation, in which the case-level output from the device was compared with a reference standard ( ground truth'). This was determined by two ground truthers, with a third truther used in the event of disagreement. All truthers were US board-certified neuroradiologists.

The device interfaces with image and order management systems (such as PACS/RIS) to obtain noncontrast brain CT studies for processing by the AI algorithm. Following processing, if any of the radiological findings of interest are identified in a non-contrast brain CT study, the device provides a notification to the image and order management system for prioritization of that study in the worklist. This enables users to review the studies containing features suggestive of these radiological findings earlier than in the standard clinical workflow. It is important to note that the device will never decrease a study's existing priority in the worklist. This ensures that worklist items will never have their priorities downgraded based on AI results.

The device workflow is performed parallel to and in conjunction with the standard clinical workflow for interpretation of non-contrast brain CTs. The device is intended to aid in prioritization and triage of radiological medical images only.

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V. INDICATIONS FOR USE

The Indications for Use statement is as follows:

Intended context Annalise Enterprise is a device designed to be used in the medical care environment to aid in triage and prioritization of studies with features suggestive of the following finding: mass effect . The device analyzes studies using an artificial intelligence algorithm to identify the finding. It makes study-level output available to an order and imaging management system for worklist prioritization or triage. The device is not intended to direct attention to specific portions of an image and only provides notification for the suspected finding. Its results are not intended: to be used on a standalone basis for clinical decision making ● ● to rule out specific findings, or otherwise preclude clinical assessment of CTB studies Intended modality Annalise Enterprise identifies suspected findings in non-contrast brain CT studies. Intended user The device is intended to be used by trained clinicians who, as part of their scope of practice, are qualified to interpret brain CT studies. Intended patient The intended population is patients who are 22 years or older . population

The Indications for Use statement of the subject device differs to the predicate device only in the clinical conditions of interest, however a standalone performance evaluation was conducted and demonstrated that the device is as safe and effective for its intended use. Both the subject and predicate device are intended for use to assist with worklist triage by providing notifications of suspected findings and their associated priority.

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COMPARISON OF TECHNOLOGICAL CHARACTERISTICS VI. WITH THE PREDICATE DEVICE

The subject device was evaluated and compared to the predicate device with respect to the following characteristics:

1. Indications for Use
1. Anatomical site and modality
1. Intended user and clinical use environment
1. Technical method for notification and prioritization
1. Device input and radiological image protocol
1. System components
1. Location where results are received
1. Prioritization relationship to standard of care workflow

The following characteristics showed a difference between the subject and predicate devices. The different characteristics include:

1. Set of findings and algorithm
1. Device output and means of notification to user

The first difference between the subject and predicate device is the set of findings that the subject device identifies and the underlying artificial intelligence algorithm. This difference does not raise new questions of safety and effectiveness.

The second difference between the subject and predicate device is the output and means of notification. While both devices provide preview images for non-diagnostic viewing, the subject device injects back into a worklist whereas the provides a notification to the workstation. Furthermore, the subject adds a priority, whereas the predicate identifies them for review. However, these differences do not raise new questions of safety and effectiveness.

The performance of the subject device was addressed in standalone performance and triage effectiveness evaluations and showed that the subject device is as safe and effective for its intended use as the predicate device.

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VII. PERFORMANCE DATA

The following performance data have been provided to support evaluation of substantial equivalence.

Software Verification and Validation Testing A.

Software verification and validation testing was conducted, and documentation was provided as recommended by FDA's Guidance for Industry and FDA Staff, "Guidance for the Content of Premarket Submissions for Software Contained in Medical Devices", May 11, 2005.

B. Performance Testing

Performance of the subject device was assessed in four performance studies to satisfy requirements set forth in the special controls per 21CFR892.2080. These included standalone performance and triage effectiveness evaluations.

Standalone performance was assessed via a retrospective, anonymized study of adult patient, DICOMcompliant non-contrast brain CT cases. The test dataset used during the standalone performance evaluation was newly acquired and independent from the training dataset used in model development. The standalone performance study was conducted on an independent cohort which equated to a total dataset of 626 cases for slice thickness ≤1.5mm (positive n=493 and negative n=133) and 762 cases for slice thickness >1.5mm & <5.0mm (positive n=594 and negative n=168), collected consecutively from five US hospital network sites. The cohort included representation across subgroups for patient demographics (gender, age, ethnical parameters (imaging equipment make, model) and co-existing findings or abnormalities.

To determine the ground truth, each deidentified case was annotated in a blinded fashion by at least two ABR-certified and protocol-trained neuroradiologists (ground truthers), with consensus determined by two ground truthers and a third ground truther in the event of disagreement. The key results of the study are summarized in the table below.

Finding	Slice Thickness Range	Operating Point	Sensitivity % (Se)(95% CI)	Specificity % (Sp)(95% CI)
Mass Effect	≤1.5mm	0.160195	97.0 (95.3,98.4)	88.7 (83.5,94.0)
	≤1.5mm	0.221484	96.6 (94.9,98.2)	89.5 (84.2,94.0)
Mass Effect	>1.5mm & ≤5.0mm	0.120944	96.8 (95.3,98.1)	89.3 (84.5,93.5)
Mass Effect		0.160195	95.3 (93.6,97.0)	92.9 (88.7,96.4)

The results demonstrate the subject device establishes effective triage within a clinician's queue based on high sensitivity and specificity. Further, these results are substantially equivalent to those of the predicate device.

Triage effectiveness (turn-around time) was assessed by an internal bench study using a dataset of n=277 cases positive for any of the findings eligible for prioritization. These cases were collected from multiple data sources spanning a variety of geographical locations, patient demographics and technical characteristics. The results demonstrated a triage turn-around time of 81.6 (95%CI: 80.3 - 82.9)

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seconds, which is substantially equivalent to the total performance time published for the predicate device.

Therefore, the subject device has been shown to satisfy the performance requirements per 21CFR892.2080, for 'Radiological computer aided triage and notification software', by providing clinically effective triage for non-contrast brain CT studies containing features suggestive of radiological findings of interest. This data demonstrates the subject device is as safe and effective for its intended use as the predicate device, and thereby supports substantial equivalence.

VIII. CONCLUSIONS

The subject device and the predicate device are both software only packages, devices intended to assist with worklist triage by providing notification of findings. The subject and predicate devices utilize the same principles of operation and work in parallel to the current standard of care workflow.

Both the subject and predicate devices use an artificial intelligence algorithm to identify findings in images and require the same inputs (DICOM image data) and provide the same outputs (prioritization for a medical worklist).

The technological differences between the subject and predicate devices do not raise new questions of safety and effectiveness.

Standalone performance testing and the comparison of technological characteristics with the predicate device shows that the subject device:

performs as intended, ●
is as safe and effective for its intended use as the predicate device, and ●
is therefore substantially equivalent to the predicate device. ●

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.