(243 days)
Yes
The device description explicitly states that it uses an "artificial intelligence (AI) algorithm - a convolutional neural network trained using deep-learning techniques" to identify findings.
No.
This device is a software workflow tool designed to aid in the prioritization and triage of radiological medical images by identifying findings suggestive of vertebral compression fracture; it does not provide therapy or treatment.
No.
The device is explicitly stated as a "software workflow tool designed to aid the clinical assessment" and "intended to aid in prioritization and triage of radiological medical images only." It is also stated that "Its results are not intended to be used on a standalone basis for clinical decision making nor is it intended to rule out specific critical findings, or otherwise preclude clinical assessment of X-ray cases." This indicates it does not provide a definitive diagnosis but rather assists in workflow management.
Yes
The device is explicitly described as a "software workflow tool" and its function is to analyze images using an AI algorithm and provide output to existing systems (PACS/RIS). There is no mention of accompanying hardware or hardware components being part of the device itself.
Based on the provided information, this device is not an In Vitro Diagnostic (IVD).
Here's why:
- IVDs analyze biological samples: In Vitro Diagnostics are designed to examine specimens taken from the human body, such as blood, urine, tissue, etc., to provide information about a person's health.
- This device analyzes medical images: The Annalise Enterprise CXR Triage Trauma software analyzes chest X-ray images, which are medical images, not biological samples.
The device falls under the category of medical imaging software or potentially a medical device software (MDSW) with an AI component, but it does not meet the definition of an IVD.
No
The provided text does not contain any explicit statement that the FDA has reviewed, approved, or cleared a Predetermined Change Control Plan (PCCP) for this specific device. The entire section for "Control Plan Authorized (PCCP) and relevant text" indicates "Not Found".
Intended Use / Indications for Use
Annalise Enterprise CXR Triage Trauma is a software workflow tool designed to aid the clinical assessment of adult chest X-ray cases with features suggestive of vertebral compression fracture in the medical care environment.
The device analyzes cases using an artificial intelligence algorithm to identify findings. It makes case-level output available to a PACS or RIS for worklist prioritization or triage intended for clinicians in Bone Health and Fracture Liaison Service programs.
The device is intended to be used by trained clinicians who are qualified to interpret chest X-rays as part of their scope of practice.
The device is not intended to direct attention to specific portions of an image or to anomalies other than vertebral compression fracture.
Its results are not intended to be used on a standalone basis for clinical decision making nor is it intended to rule out specific critical findings, or otherwise preclude clinical assessment of X-ray cases.
Standalone performance evaluation of the device was performed on a dataset that included only erect positioning. Use of this device with supine positioning may result in differences in performance.
Product codes
QFM
Device Description
Annalise Enterprise CXR Triage Trauma is a software workflow tool which uses an artificial intelligence (AI) algorithm to identify suspected findings on chest X-ray (CXR) studies in the medical care environment. The findings identified by the device include vertebral compression fractures.
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 across three continents, including 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 radiologists.
The device interfaces with image and order management systems (such as PACS/RIS) to obtain CXR studies for processing by the AI algorithm. Following processing, if the clinical finding of interest is identified in a CXR 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 clinical 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 CXRs. The device is intended to aid in prioritization and triage of radiological medical images only.
Mentions image processing
Yes
Mentions AI, DNN, or ML
Mentions AI, artificial intelligence, convolutional neural network, deep-learning.
Input Imaging Modality
Chest X-ray (CXR)
Anatomical Site
Chest
Indicated Patient Age Range
Adult
Intended User / Care Setting
Trained clinicians who are qualified to interpret chest X-rays as part of their scope of practice, in the medical care environment, specifically for clinicians in Bone Health and Fracture Liaison Service programs for worklist prioritization or triage.
Description of the training set, sample size, data source, and annotation protocol
Images used to train the algorithm were sourced from datasets across three continents, including a range of equipment manufacturers and models. Test dataset was independent from the training dataset.
Description of the test set, sample size, data source, and annotation protocol
Standalone performance was assessed via a retrospective, anonymized study of adult patient, DICOM-compliant CXR 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 a dataset of 589 CXR cases (positive n=272 and negative n=317), collected consecutively from four US hospital network sites. The cohort included representation across subgroups for patient demographics (gender, age, ethnicity, race), technical parameters (imaging equipment make, model), imaging parameters (positioning, projections) and co-existing findings or abnormalities. To determine the ground truth, each deidentified CXR case was annotated in a blinded fashion by at least two ABR-certified and protocol-trained radiologists (ground truthers), with consensus determined by two ground truthers and a third ground truther in the event of disagreement.
Summary of Performance Studies (study type, sample size, AUC, MRMC, standalone performance, key results)
Performance of the subject device was assessed via software verification and validation testing, and performance testing for standalone performance and triage effectiveness evaluations.
Standalone performance evaluation:
- Study type: Retrospective, anonymized study.
- Sample size: 589 CXR cases (positive n=272 and negative n=317).
- AUC: 0.954 (95% CI: 0.939-0.968) for Vertebral compression fracture.
- Standalone performance: Yes. Results for sensitivity and specificity at two operating points were provided.
- Key results: 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):
- Study type: Assessed as part of the standalone performance study.
- Sample size: Not explicitly stated, but cases were collected from multiple data sources.
- Key results: The results demonstrated an average triage turn-around time of 30.0 seconds, which is substantially equivalent to that published for the predicate device.
Key Metrics (Sensitivity, Specificity, PPV, NPV, etc.)
Sensitivity (Se):
- At Operating Point 0.3849: 89.3 (85.7-93.0)
- At Operating Point 0.4834: 85.3 (80.9-89.3)
Specificity (Sp):
- At Operating Point 0.3849: 89.0 (85.8-92.1)
- At Operating Point 0.4834: 90.9 (87.7-94.0)
Predicate Device(s)
Reference Device(s)
Predetermined Change Control Plan (PCCP) - All Relevant Information
Not Found
§ 892.2080 Radiological computer aided triage and notification software.
(a)
Identification. Radiological computer aided triage and notification software is an image processing prescription device intended to aid in prioritization and triage of radiological medical images. The device notifies a designated list of clinicians of the availability of time sensitive radiological medical images for review based on computer aided image analysis of those images performed by the device. The device does not mark, highlight, or direct users' attention to a specific location in the original image. The device does not remove cases from a reading queue. The device operates in parallel with the standard of care, which remains the default option for all cases.(b)
Classification. Class II (special controls). The special controls for this device are:(1) Design verification and validation must include:
(i) A detailed description of the notification and triage algorithms and all underlying image analysis algorithms including, but not limited to, a detailed description of the algorithm inputs and outputs, each major component or block, how the algorithm affects or relates to clinical practice or patient care, and any algorithm limitations.
(ii) A detailed description of pre-specified performance testing protocols and dataset(s) used to assess whether the device will provide effective triage (
e.g., improved time to review of prioritized images for pre-specified clinicians).(iii) Results from performance testing that demonstrate that the device will provide effective triage. The performance assessment must be based on an appropriate measure to estimate the clinical effectiveness. The test dataset must contain sufficient numbers of cases from important cohorts (
e.g., subsets defined by clinically relevant confounders, effect modifiers, associated diseases, and subsets defined by image acquisition characteristics) such that the performance estimates and confidence intervals for these individual subsets can be characterized with the device for the intended use population and imaging equipment.(iv) Stand-alone performance testing protocols and results of the device.
(v) Appropriate software documentation (
e.g., device hazard analysis; software requirements specification document; software design specification document; traceability analysis; description of verification and validation activities including system level test protocol, pass/fail criteria, and results).(2) Labeling must include the following:
(i) A detailed description of the patient population for which the device is indicated for use;
(ii) A detailed description of the intended user and user training that addresses appropriate use protocols for the device;
(iii) Discussion of warnings, precautions, and limitations must include situations in which the device may fail or may not operate at its expected performance level (
e.g., poor image quality for certain subpopulations), as applicable;(iv) A detailed description of compatible imaging hardware, imaging protocols, and requirements for input images;
(v) Device operating instructions; and
(vi) A detailed summary of the performance testing, including: test methods, dataset characteristics, triage effectiveness (
e.g., improved time to review of prioritized images for pre-specified clinicians), diagnostic accuracy of algorithms informing triage decision, and results with associated statistical uncertainty (e.g., confidence intervals), including a summary of subanalyses on case distributions stratified by relevant confounders, such as lesion and organ characteristics, disease stages, and imaging equipment.
0
March 28, 2023
Image /page/0/Picture/1 description: The image contains the logo of the U.S. Food and Drug Administration (FDA). The FDA logo is composed of the Department of Health & Human Services logo on the left, followed by the FDA acronym in a blue square, and the words "U.S. FOOD & DRUG ADMINISTRATION" in blue text. The logo is simple and professional, and it is easily recognizable.
Annalise-AI Pty Ltd. % Haylee Bosshard Regulatory Affairs Manager Level P, 24 Campbell St. Sydney, New South Wales 2000 AUSTRALIA
Re: K222268
Trade/Device Name: Annalise Enterprise CXR Triage Trauma Regulation Number: 21 CFR 892.2080 Regulation Name: Radiological computer aided triage and notification software Regulatory Class: Class II Product Code: QFM Dated: March 9, 2023 Received: March 9, 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
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
Jessica Lamb, Ph.D. Assistant Director Imaging Software Team DHT 8B: Division of Radiological Imaging Devices and Electronic Products OHT 8: Office of Radiological Health Office of Product Evaluation and Quality Center for Devices and Radiological Health
Enclosure
2
Indications for Use
510(k) Number (if known) K22268
Device Name Annalise Enterprise CXR Triage Trauma
Indications for Use (Describe)
Annalise Enterprise CXR Triage Trauma is a software workflow tool designed to aid the clinical assessment of adult chest X-ray cases with features suggestive of vertebral compression fracture in the medical care environment.
The device analyzes cases using an artificial intelligence algorithm to identify findings. It makes case-level output available to a PACS or RIS for worklist prioritization or triage intended for clinicians in Bone Health and Fracture Liaison Service programs.
The device is intended to be used by trained clinicians who are qualified to interpret chest X-rays as part of their scope of practice.
The device is not intended to direct attention to specific portions of an image or to anomalies other than vertebral compression fracture.
Its results are not intended to be used on a standalone basis for clinical decision making nor is it intended to rule out specific critical findings, or otherwise preclude clinical assessment of X-ray cases.
Standalone performance evaluation of the device was performed on a dataset that included only erect positioning. Use of this device with supine positioning may result in differences in performance.
| 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) |
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 number."
3
Page 1
510(k) Summary
SUBMITTER I.
| Company Name | Annalise-AI Pty Ltd |
|---|---|
| Address | Level P, 24 Campbell St |
| Sydney, NSW 2000 | |
| Australia | |
| Phone Number | +61 1800 958 487 |
| Contact Person | Haylee Bosshard |
| Date Prepared | July 28, 2022 |
II. SUBJECT DEVICE
| Manufacturer Name | Annalise-AI Pty Ltd |
|---|---|
| Device Name | Annalise Enterprise CXR Triage Trauma |
| 510(k) reference | K222268 |
| Classification Name | Radiological computer aided triage and notification software |
| (21CFR892.2080) | |
| Regulatory Class | II |
| Product Code | QFM |
III. PREDICATE DEVICE
| Manufacturer Name | Annalise-AI Pty Ltd |
|---|---|
| Device Name | Annalise Enterprise CXR Triage Pneumothorax |
| 510(k) reference | K213941 |
| Classification Name | Radiological computer aided triage and notification software |
| (21CFR892.2080) | |
| Regulatory Class | II |
| Product Code | QFM |
This predicate has not been subject to a design-related recall.
REFERENCE DEVICE IV.
| Manufacturer Name | Zebra Medical Vision Ltd. |
|---|---|
| Device Name | HealthVCF |
| 510(k) reference | K192901 |
| Classification Name | Radiological computer aided triage and notification software |
| (21CFR892.2080) | |
| Regulatory Class | II |
| Product Code | QFM |
Confidential Proprietary Information, not to be reproduced or made available to third parties without prior consent from annalise.ai and not to be used in any unauthorized way
4
DEVICE DESCRIPTION V.
Annalise Enterprise CXR Triage Trauma is a software workflow tool which uses an artificial intelligence (AI) algorithm to identify suspected findings on chest X-ray (CXR) studies in the medical care environment. The findings identified by the device include vertebral compression fractures.
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 across three continents, including 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 radiologists.
The device interfaces with image and order management systems (such as PACS/RIS) to obtain CXR studies for processing by the AI algorithm. Following processing, if the clinical finding of interest is identified in a CXR 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 clinical 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 CXRs. The device is intended to aid in prioritization and triage of radiological medical images only.
INDICATIONS FOR USE VI.
The Indications for Use statement is as follows:
Annalise Enterprise CXR Triage Trauma is a software workflow tool designed to aid the clinical assessment of adult chest X-ray cases with features suggestive of vertebral compression fracture in the medical care environment.
The device analyzes cases using an artificial intelligence algorithm to identify findings. It makes case-level output available to a PACS or RIS for worklist prioritization or triage intended for clinicians in Bone Health and Fracture Liaison Service programs.
The device is intended to be used by trained clinicians who are qualified to interpret chest X-rays as part of their scope of practice.
The device is not intended to direct attention to specific portions of an image or to anomalies other than vertebral compression fracture.
Page 2
5
Its results are not intended to be used on a standalone basis for clinical decision making nor is it intended to rule out specific critical findings, or otherwise preclude clinical assessment of X-ray cases.
Standalone performance evaluation of the device was performed on a dataset that included only erect positioning. Use of this device with supine positioning may result in differences in performance.
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 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.
VII. COMPARISON OF TECHNOLOGICAL CHARACTERISTICS WITH THE PREDICATE DEVICE
The subject device was evaluated and compared to the predicate device with respect to the following characteristics:
-
- Indications for Use
-
- Target population
-
- Anatomical site and modality
-
- Intended user and clinical use environment
-
- Technical method for notification and prioritization
-
- Device input and radiological image protocol
-
- Device output and means of notification to user
-
- System components
-
- Location where results are received
-
- Prioritization relationship to standard of care workflow
-
- Ability to support effective triage
-
- Set of findings
The difference between the two devices were identified as:
- the set of findings that the subject device is intended to identify, and
- the users that the subject device is intended to notify. ●
To address these differences, standalone performance data was supplied and a reference device was used to support the conclusion that the subject device does not raise different questions of safety and effectiveness and that the subject device is as safe and effective as the predicate device.
6
VIII.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 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, DICOM-compliant CXR 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 a dataset of 589 CXR cases (positive n=272 and negative n=317), collected consecutively from four US hospital network sites. The cohort included representation across subgroups for patient demographics (gender, age, ethnicity, race), technical parameters (imaging equipment make, model), imaging parameters (positioning, projections) and co-existing findings or abnormalities. To determine the ground truth, each deidentified CXR case was annotated in a blinded fashion by at least two ABR-certified and protocol-trained radiologists (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 tables below.
| Finding | Results |
|---|---|
| Vertebral compression fracture | AUC: 0.954 (95% CI: 0.939-0.968) |
| Finding | Operating
Point | Sensitivity (Se)
(95% CI) | Specificity (Sp)
(95% CI) |
|-----------------------------------|--------------------|------------------------------|------------------------------|
| Vertebral compression
fracture | 0.3849 | 89.3 (85.7-93.0) | 89.0 (85.8-92.1) |
| fracture | 0.4834 | 85.3 (80.9-89.3) | 90.9 (87.7-94.0) |
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 as part of the standalone performance study. These cases were collected from multiple data sources spanning a variety of geographical
Page 4
7
locations, patient demographics and technical characteristics. The results demonstrated an average triage turn-around time of 30.0 seconds, which is substantially equivalent to that published for the predicate device.
Therefore, the subject device has been shown to satisfy the performance requirements per 21CFR892.2080, for radiological triage and notification software, by providing clinically effective triage for chest X-ray studies containing features suggestive of clinical findings of interest. This data demonstrates the subject device is safe and effective for its intended use, and thereby supports substantial equivalence.
IX. 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 in adults to trained clinicians. 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 have the same software architecture, use the same deep learning AI principals to identify findings in images and require the same inputs and provide the equivalent outputs.
The minor 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 devices shows that the subject device:
- performs as intended,
- is safe and effective for its intended use, and ●
- is therefore substantially equivalent to the predicate device.