(132 days)
No reference devices were used in this submission.
Yes
The device description explicitly mentions an "AI algorithm" as a core component and the document includes sections describing the training and test sets for an "AI model".
No.
This device is an image processing software that analyzes medical images and provides anatomical descriptors. It does not directly treat or prevent a disease, nor does it restore, modify, or correct the body structure or function.
No
The device is intended to analyze image pixel data to create anatomical descriptors, which are supplemental metadata used to categorize anatomy. It does not provide a medical diagnosis or interpret the images for disease, but rather organizes information for physicians and healthcare systems.
Yes
The device description explicitly states it is a "standalone image processing software application" and details its components as API endpoints, an AI algorithm, a study results aggregator, and a data store, all of which are software elements. It processes existing image data and exports derived information, without mentioning any associated hardware that is 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: The core function of an IVD is to examine biological specimens (like blood, urine, tissue, etc.) to provide information about a patient's health status, diagnose diseases, or monitor treatment.
- This device analyzes medical images: The Change Healthcare Anatomical AI analyzes pixel data from CT and MR images. It does not interact with or analyze any biological samples from the patient.
- Its purpose is image processing and annotation: The device's intended use is to create anatomical descriptors from images to help organize and identify studies. This is a form of image processing and metadata generation, not a diagnostic test performed on a biological sample.
Therefore, while it is a medical device used in healthcare, it falls under the category of medical image processing software rather than an In Vitro Diagnostic.
No
The letter does not state that the FDA has reviewed and approved or cleared a Predetermined Change Control Plan (PCCP) for this specific device.
Intended Use / Indications for Use
Change Healthcare Anatomical AI is intended to analyze pixel data from CT or MR images to create comprehensive anatomic descriptors for export to integrated healthcare systems.
This supplements traditional methods used for the selection, presentation or analysis of image based medical data. The application is intended to enable physicians, or other healthcare providers as well as integrated healthcare systems to rapidly identify images, series, and / or studies of interest.
Change Healthcare Anatomical AI is not indicated for patients under the age of 18 years old.
Product codes
QIH
Device Description
Change Healthcare Anatomical AI is a standalone image processing software application that analyzes CT and MR DICOM images to associate anatomic regions with images and exports the derived information for use in integrated healthcare systems. These anatomic descriptors can be applied by integrated applications to categorize anatomy from a patient's CT or MR image, series, or study.
The device communicates via Application Programmable Interfaces (APIs) which allow for receiving DICOM images and returning inference results. The algorithm produces a JSON file which contains results of the analysis for each image and study with the corresponding identified body regions.
Change Healthcare Anatomical AI works in parallel to and in conjunction with the standard of care workflow. The device does not alter the original medical image in any way. The anatomic descriptors are used as supplemental metadata for a patient's imaging study.
Change Healthcare Anatomical AI contains the following core components:
API endpoints
The device uses API endpoints which allow for receiving DICOM images and returning results.
Following receipt of an image, the device performs data validation to ensure appropriateness and compatibility for the algorithm. If the validation fails and the image cannot be processed, an error is returned with the corresponding code and description.
AI algorithm
After validation, the algorithm analyzes the CT or MR image pixel data and generates the anatomic descriptors.
Study results aggregator
The results of the analysis for each image in a study are aggregated and returned to the integrated system.
Data store
The results of the inference for each analyzed image are maintained in a persistent data store. The results are stored by the algorithm inference model and retrieved by the study results aggregation component.
Mentions image processing
Yes
Mentions AI, DNN, or ML
Yes - "AI algorithm", "Machine learning based algorithm (non-adaptive)"
Input Imaging Modality
CT, MR
Anatomical Site
Abdomen, breast (MR only), calf, chest, elbow, foot, forearm, hand, head, arm, knee, neck, pelvis, shoulder, spine cervical, spine thoracic, spine lumbar, and thigh
Indicated Patient Age Range
not indicated for patients under the age of 18 years old.
Intended User / Care Setting
physicians, or other healthcare providers as well as integrated healthcare systems
Description of the training set, sample size, data source, and annotation protocol
Not Found
Description of the test set, sample size, data source, and annotation protocol
A retrospective study was designed to assess the subject device accuracy. For each modality, three databases were built for the AI model training, validation, and testing, with a balanced distribution of studies per body region. By design, no repeat or temporal studies were allowed in the validation and test databases.
The test databases originated from a different healthcare system. The de-identified datasets were gathered from multiple centers to reduce bias due to demographics, scanner manufacturer and acquisition protocols. 27 institutions from primary care hospitals, community hospitals and imaging centers contributed to the test datasets. The data included cases of all genders in equal proportions and subjects aging from 18 years old and above.
This all-comers study was designed with the intent to be as inclusive as possible and clinically relevant. All randomly selected patients from 18 years old and above were included in the study irrespective of their demographic or comorbidities. The same inclusive approach was followed for clinical protocols.
Summary of Performance Studies (study type, sample size, AUC, MRMC, standalone performance, key results)
Software verification and validation testing were 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." The software for this device is considered as a Moderate Level of Concern software medical device, since a failure or latent design flaw could indirectly result in minor injury to the patient through incorrect or delayed information through the action of a care provider.
Verification and Validation Plans and Protocols have been executed to ensure adequate testing of all defined product design requirements and specifications and the device performs as intended. Software verification testing assessed the performance of the software's anatomical structure detection function, performance characteristics of the algorithm including image-level accuracy, risk management, and overall functional performance.
A retrospective study was designed to assess the subject device accuracy. For each modality, three databases were built for the AI model training, validation, and testing, with a balanced distribution of studies per body region. By design, no repeat or temporal studies were allowed in the validation and test databases.
The test databases originated from a different healthcare system. The de-identified datasets were gathered from multiple centers to reduce bias due to demographics, scanner manufacturer and acquisition protocols. 27 institutions from primary care hospitals, community hospitals and imaging centers contributed to the test datasets. The data included cases of all genders in equal proportions and subjects aging from 18 years old and above.
This all-comers study was designed with the intent to be as inclusive as possible and clinically relevant. All randomly selected patients from 18 years old and above were included in the study irrespective of their demographic or comorbidities. The same inclusive approach was followed for clinical protocols.
The accuracy results were evaluated according to patient demographics, healthcare institution, and other confounding imaging factors such as scanner manufacturer, presence of contrast, slice thickness, MR sequence, and CT kernel.
Test Summary Reports have been created to evaluate the acceptability of test results and all applicable verification and validation activities and records have been completed to ensure safety and effectiveness of the device.
Key Metrics (Sensitivity, Specificity, PPV, NPV, etc.)
Not Found
Predicate Device(s)
Reference Device(s)
No reference devices were used in this submission.
Predetermined Change Control Plan (PCCP) - All Relevant Information
Not Found
§ 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).
0
July 20, 2021
Image /page/0/Picture/1 description: The image shows the logo of the U.S. Food and Drug Administration (FDA). The logo consists of two parts: the Department of Health & Human Services logo on the left and the FDA acronym and name on the right. The FDA part of the logo is in blue, with the acronym in a square and the full name, "U.S. Food & Drug Administration," written out next to it.
Change Healthcare Canada Company % Chester Mccoy VP, QA/RA & Chief Quality Officer 10711 Cambie Road Richmond, BC V6X 3GS CANADA
Re: K210719
Trade/Device Name: Change Healthcare Anatomical AI Regulation Number: 21 CFR 892.2050 Regulation Name: Medical image management and processing system Regulatory Class: Class II Product Code: QIH Dated: June 23, 2021 Received: June 24, 2021
Dear Chester Mccoy:
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.
For
Thalia T. Mills, Ph.D. Director Division of Radiological Health OHT7: Office of In Vitro Diagnostics and Radiological Health Office of Product Evaluation and Quality Center for Devices and Radiological Health
Enclosure
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Indications for Use
510(k) Number (if known) K210719
Device Name Change Healthcare Anatomical AI
Indications for Use (Describe)
Change Healthcare Anatomical AI is intended to analyze pixel data from CT or MR images to create comprehensive anatomic descriptors for export to integrated healthcare systems.
This supplements traditional methods used for the selection, presentation or analysis of image based medical data. The application is intended to enable physicians, or other healthcare providers as well as integrated healthcare systems to rapidly identify images, series, and/ or studies of interest.
Change Healthcare Anatomical AI is not indicated for patients under the age of 18 years old.
| 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) |
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Image /page/3/Picture/1 description: The image shows the logo for Change Healthcare. The word "CHANGE" is written in a dark blue font, with the "A" replaced by a red triangle. Below the word "CHANGE", the word "HEALTHCARE" is written in a red font.
Section 5: 510 (k) Summary
The Company's 510(k) Summary of Change Healthcare Anatomical AI (K210719), prepared in accordance with 21 CFR 807.92(C), is provided on the following page.
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Image /page/4/Picture/1 description: The image shows the logo for Change Healthcare. The word "CHANGE" is written in dark blue, with the "A" replaced by a red triangle pointing upwards. Below "CHANGE", the word "HEALTHCARE" is written in red.
510(k) SUMMARY
Change Healthcare Anatomical AI
1. Introduction and Administrative Information
This 510(k) Summary provides a high-level summary of the contents of the Change Healthcare Anatomical AI Premarket Notification (510(k)), including a comparison of Change Healthcare Anatomical AI to existing legally marketed medical device.
10711 Cambie Road
This Premarket Notification (510(k)) Summary contains no confidential or trade secret information. For additional information, please contact the Establishment's contact listed below.
This summary was prepared on March 9, 2021.
1.1. Submitter
Submitter Submitter Address
Submitter Phone Submitter Website Establishment Number Establishment Contact Contact Title Contact Phone Contact Email
1.2. Device Identification
Proprietary Name(s): Common/ Usual Name:
1.3. Device Classification
Device classification: Regulation Number: Classification Product Code: Classification Name: Regulation Description:
Richmond, B.C. Canada, V6X 3G5 (604) 279-5422 www.changehealthcare.com 8022257 Chester McCoy VP, QA/RA & Chief Quality Officer (404) 338-2088 chester.mccoy(@changehealthcare.com
Change Healthcare Canada Company
Change Healthcare Anatomical AI Automated Radiological Image Processing Software
Class II 21 CFR 892.2050 OIH Automated Radiological Image Processing Software Medical image management and processing system
1.4. Predicate Device Identification
| Proprietary Name(s): | AquariusAPS Server |
|---|---|
| 510(k) Number: | K061214 |
| Applicant: | TeraRecon, Inc. |
No reference devices were used in this submission.
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Image /page/5/Picture/1 description: The image shows the logo for Change Healthcare. The word "CHANGE" is in large, bold, dark blue letters, with the "A" replaced by a red triangle pointing upwards. Below "CHANGE" in smaller, red letters is the word "HEALTHCARE".
2. Indications for Use and Device Description
2.1. Indications for Use
Change Healthcare Anatomical AI is intended to analyze pixel data from CT or MR images to create comprehensive anatomic descriptors for export to integrated healthcare systems.
This supplements traditional methods used for the selection, presentation or analysis of image based medical data. The application is intended to enable physicians, or other healthcare providers as well as integrated healthcare systems to rapidly identify images, series, and / or studies of interest.
Change Healthcare Anatomical AI is not indicated for patients under the age of 18 years old.
2.2. Device Description
Change Healthcare Anatomical AI is a standalone image processing software application that analyzes CT and MR DICOM images to associate anatomic regions with images and exports the derived information for use in integrated healthcare systems. These anatomic descriptors can be applied by integrated applications to categorize anatomy from a patient's CT or MR image, series, or study.
The device communicates via Application Programmable Interfaces (APIs) which allow for receiving DICOM images and returning inference results. The algorithm produces a JSON file which contains results of the analysis for each image and study with the corresponding identified body regions.
Change Healthcare Anatomical AI works in parallel to and in conjunction with the standard of care workflow. The device does not alter the original medical image in any way. The anatomic descriptors are used as supplemental metadata for a patient's imaging study.
Change Healthcare Anatomical AI contains the following core components:
API endpoints
The device uses API endpoints which allow for receiving DICOM images and returning results.
Following receipt of an image, the device performs data validation to ensure appropriateness and compatibility for the algorithm. If the validation fails and the image cannot be processed, an error is returned with the corresponding code and description.
AI algorithm
After validation, the algorithm analyzes the CT or MR image pixel data and generates the anatomic descriptors.
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Image /page/6/Picture/1 description: The image shows the logo for Change Healthcare. The word "CHANGE" is written in a bold, sans-serif font, with the letters in dark blue except for the "A", which is a red triangle. Below the word "CHANGE" is the word "HEALTHCARE" in a smaller, red, sans-serif font. The logo is simple and modern, and the use of color helps to draw the eye.
Study results aggregator
The results of the analysis for each image in a study are aggregated and returned to the integrated system.
Data store
The results of the inference for each analyzed image are maintained in a persistent data store. The results are stored by the algorithm inference model and retrieved by the study results aggregation component.
3. Comparison of Technological Characteristics with the Predicate Device
With respect to technological characteristics, Change Healthcare Anatomical AI is substantially equivalent to the predicate device. A comparison of the proposed device to the currently marketed predicate device is provided in the table below:
| Description | Subject Device | Predicate Device |
|---|---|---|
| Device proprietary name | Change Healthcare Anatomical AI | AquariusAPS Server |
| Manufacturer | Change Healthcare Canada Company | TeraRecon, Inc. |
| 510(k) Number | K210719 | K061214 |
| Classification | Class II | Class II |
| Product Code | QIH | LLZ |
| Regulatory Number | 892.2050 | 892.2050 |
| Platform | Change Healthcare Enterprise Imaging Network (EIN) cloud platform | TeraRecon |
| Intended for use in primary diagnostic workflow | Yes | Yes |
| Process DICOM images | Yes | Yes |
| Identify locations of anatomical structures | Abdomen, breast (MR only), calf, chest, elbow, foot, forearm, hand, head, arm, knee, neck, pelvis, shoulder, spine cervical, spine thoracic, spine lumbar, and thigh | Brain, Heart, Heart Vasculature, Liver, Lung |
| Modalities supported for identification of anatomical structures | CT and MR | CT |
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Image /page/7/Picture/1 description: The image shows the logo for Change Healthcare. The word "CHANGE" is in large, bold, dark blue letters, with the "A" replaced by a red triangle pointing upwards. Below "CHANGE" is the word "HEALTHCARE" in smaller, red letters.
| Description | Subject Device | Predicate Device |
|---|---|---|
| Algorithm | Machine learning based algorithm (non-adaptive) | Image processing based algorithm |
| Manual review of identified anatomical structures | Yes | Yes |
| Can be used to navigate to a study based on an identified anatomical structure | Yes | Yes |
| Performing actions based on DICOM and other data identified from the DICOM image set | Yes | Yes |
4. Performance Data
4.1. Software Verification and Validation Testing
Software verification and validation testing were 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." The software for this device is considered as a Moderate Level of Concern software medical device, since a failure or latent design flaw could indirectly result in minor injury to the patient through incorrect or delayed information through the action of a care provider.
Verification and Validation Plans and Protocols have been executed to ensure adequate testing of all defined product design requirements and specifications and the device performs as intended. Software verification testing assessed the performance of the software's anatomical structure detection function, performance characteristics of the algorithm including image-level accuracy, risk management, and overall functional performance.
A retrospective study was designed to assess the subject device accuracy. For each modality, three databases were built for the AI model training, validation, and testing, with a balanced distribution of studies per body region. By design, no repeat or temporal studies were allowed in the validation and test databases.
The test databases originated from a different healthcare system. The de-identified datasets were gathered from multiple centers to reduce bias due to demographics, scanner manufacturer and
8
Image /page/8/Picture/1 description: The image shows the logo for Change Healthcare. The word "CHANGE" is written in a bold, sans-serif font, with the letters in navy blue except for the "A", which is a red triangle. Below "CHANGE" is the word "HEALTHCARE" in red, sans-serif font.
acquisition protocols. 27 institutions from primary care hospitals, community hospitals and imaging centers contributed to the test datasets. The data included cases of all genders in equal proportions and subjects aging from 18 years old and above.
This all-comers study was designed with the intent to be as inclusive as possible and clinically relevant. All randomly selected patients from 18 years old and above were included in the study irrespective of their demographic or comorbidities. The same inclusive approach was followed for clinical protocols.
The accuracy results were evaluated according to patient demographics, healthcare institution, and other confounding imaging factors such as scanner manufacturer, presence of contrast, slice thickness, MR sequence, and CT kernel.
Test Summary Reports have been created to evaluate the acceptability of test results and all applicable verification and validation activities and records have been completed to ensure safety and effectiveness of the device.
Change Healthcare Anatomical AI adheres to the cybersecurity requirements as defined by the FDA Guidance "Content of Premarket Submissions for Management for Cybersecurity in Medical Devices," by implementing cybersecurity controls to protect data in use, in transit or at rest for the components in the product.
4.2. Conformance Standards
There are no applicable FDA required performance standards for this device. However, voluntary standards have been utilized in the design and development of the software. The following FDA recognized consensus standards were used:
- ISO 14971:2007 Medical devices Application of risk management to medical devices 트
- ISO 15223-1:2016 - Medical devices - Symbols to be used with medical devices labels, labeling, and information to be supplied - Part 1: General requirements
- l IEC 62304:2015 - Medical device software - Software life cycle processes
- . IEC 62366-1:2015 Medical devices - Part 1: Application of usability engineering to medical devices
- I NEMA PS 3.1-3.20 (2016) - Digital Imaging and Communications in Medicine (DICOM) set
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Image /page/9/Picture/1 description: The image shows the logo for Change Healthcare. The word "CHANGE" is in large, bold, dark blue letters, with the "A" replaced by a red triangle pointing upwards. Below "CHANGE" in smaller, red letters is the word "HEALTHCARE".
5. Conclusion
Change Healthcare Anatomical AI is substantially equivalent to the identified predicate device AquariusAPS Server (K061214). Specifically, Change Healthcare Anatomical Al has the similar indications for use and technological characteristics compared to the previously cleared predicate device.
The minor differences in the technological characteristics of Change Healthcare Anatomical AI and AquariusAPS Server, its predicate device, including the platform where the device is installed, identification of additional anatomical structures, additional modalities supported and algorithm type, these minor differences do not raise new issues of safety or effectiveness. The performance tests have been completed and successfully support the device performance. Therefore, Change Healthcare Anatomical AI is substantially equivalent to the predicate device.