(101 days)
The Zebra HealthMammo is a passive notification-only, parallel-workflow software tool used by MQSA-qualified interpreting physicians to prioritize patients with suspicious findings in the medical care environment. HealthMammo utilizes an artificial intelligence algorithm to analyze 2D FFDM screening mammograms and flags those that are suggestive of the presence of at least one suspicious finding at the exam-level. HealthMammo produces an exam-level output to a PACS/Workstation for flagging the suspicious case and allows worklist prioritization.
MQSA-qualified interpreting physicians are responsible for reviewing each exam on a display approved for use in mammography according to the current standard of care. HealthMammo device is limited to the categorization of exams, does not provide any diagnostic information beyond triage and prioritization, does not remove images from the interpreting physician's worklist, and should not be used in lieu of full patient evaluation, or relied upon to make or confirm diagnosis.
The HealthMammo device is intended for use with complete 2D FFDM mammography exams acquired using validated FFDM systems only.
Zebra's HealthMammo solution is a software product that automatically analyzes 2D FFDM screening mammograms and notifies PACS/workstation of the presence of suspicious findings in the scan. This passive-notification allows for worklist prioritization of the specific scan and assists clinicians in viewing prioritized scans before others. The device aim is to aid in prioritization and triage of radiological medical images only. It is a software tool for MQSA interpreting physicians reading mammograms and does not replace complete evaluation according to the standard of care.
The Zebra's HealthMammo device works in parallel to and in conjunction with the standard care of workflow. After a mammogram has been performed, a copy of the study is automatically retrieved and processed by the HealthMammo device. The device performs the analysis of the study and returns a notification about suspected finding to the PACS/workstation which flags it through the worklist interface or alternatively, the Zebra Worklist will notify the user through a desktop application. The clinician is then able to review the study earlier than in standard of care workflow.
The primary benefit of the product is the ability to reduce the time it takes to alert physicians to the presence of a suspicious finding. The software does not recommend treatment or provide a diagnosis. It is meant as a tool to assist in improved workload prioritization of suspicious cases. The final diagnosis is provided by a clinician after reviewing the scan itself.
The following modules compose the HealthMammo software:
Data input and validation: Following retrieval of a study, the validation feature assessed the input data (i.e. age, modality, view) to ensure compatibility for processing by the algorithm.
HealthMammo algorithm: Once a study has been validated, the algorithm analyzes the 2D FFDM screening mammogram for detection of suspected findings.
IMA Integration feature: The study analysis and the results of a successful study analysis is provided to IMA, to then be sent to the PACS/workstation for prioritization.
Error codes feature: In the case of a study failure during data validation or the analysis by the algorithm, an error is provided to the system.
Here's an analysis of the acceptance criteria and study proving the HealthMammo device meets them, based on the provided text:
1. Table of Acceptance Criteria and Reported Device Performance
The FDA document doesn't explicitly present a formal "acceptance criteria" table with distinct thresholds for each metric. However, it implicitly defines performance goals by comparing to a predicate device (CmTriage, K183285) and the Breast Cancer Surveillance Consortium (BCSC) study. The key performance metric highlighted for the algorithm's standalone performance is the Area Under the Receiver Operating Characteristic (ROC) curve (AUC), along with sensitivity and specificity at different operating points.
Here's a table summarizing the performance values reported, with the implicit acceptance criteria being performance comparable to the predicate and BCSC study, and exceeding AUC > 95% for effective triage.
| Metric (Operating Point) | Acceptance Criteria (Implicit) | Reported Device Performance (HealthMammo) |
|---|---|---|
| Area Under ROC Curve (AUC) | > 0.95 (for effective triage, comparable to predicate) | 0.9661 (95% CI: [0.9552, 0.9769]) |
| Sensitivity (Standard Mode) | Comparable to BCSC study/predicate | 89.89% (95% CI: [86.69%; 92.38%]) |
| Specificity (Standard Mode) | Comparable to BCSC study/predicate | 90.75% (95% CI: [87.51%; 93.21%]) |
| Sensitivity (High Sensitivity) | Comparable to BCSC study/predicate | 94.02% (95% CI: [91.39%; 95.89%]) |
| Specificity (High Sensitivity) | Comparable to BCSC study/predicate | 83.50% (95% CI: [79.55%; 86.82%]) |
| Sensitivity (High Specificity) | Comparable to BCSC study/predicate | 84.14% (95% CI: [80.41%; 87.27%]) |
| Specificity (High Specificity) | Comparable to BCSC study/predicate | 94.00% (95% CI: [91.23%; 95.94%]) |
| Average Processing Time | Comparable to predicate | 2.9 minutes |
2. Sample Size Used for the Test Set and Data Provenance
-
Sample Size: 835 anonymized 2D FFDM screening mammograms.
-
Data Provenance: Retrospective cohort from the USA, UK, and Israel.
- 435 cases positive with biopsy confirmed cancers.
- 400 cases negative for breast cancer (BIRADS 1 and BIRADS 2 with a two-year follow-up of a negative diagnosis).
- The test set was constructed to address confounding factors such as Lesion Type, Breast Density, Age, and Histology Type to ensure consistency with the population undergoing breast cancer screening.
3. Number of Experts Used to Establish the Ground Truth for the Test Set and Their Qualifications
The document does not explicitly state the number of experts or their qualifications used to establish the ground truth for the test set. It mentions "biopsy confirmed cancers" for positive cases and "two-year follow-up of a negative diagnosis" for negative cases, implying a medical gold standard rather than consensus reads.
4. Adjudication Method for the Test Set
The document does not describe an adjudication method for the test set, as the ground truth appears to be based on biopsy results and long-term follow-up rather than expert reader consensus that would typically require adjudication.
5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study Was Done, and the Effect Size of Human Readers Improving with AI vs. Without AI Assistance
No, an MRMC comparative effectiveness study involving human readers and AI assistance was not reported or described in this document. The study described is a standalone performance validation of the AI algorithm. The device is intended as a triage tool that operates in parallel to the standard workflow and does not remove cases from the radiologist's worklist.
6. If a Standalone (Algorithm Only Without Human-in-the-Loop Performance) Was Done
Yes, a standalone performance study was done. The document states: "The stand-alone detection and triage accuracy was measured on this cohort versus the ground truth." All the reported performance metrics (AUC, sensitivity, specificity) pertain to the algorithm's performance alone.
7. The Type of Ground Truth Used
The ground truth used was a combination of:
- Pathology/Outcomes Data: "biopsy confirmed cancers" for positive cases.
- Outcomes Data: "BIRADS 1 and 2 normal cases with a two-year follow-up of a negative diagnosis" for negative cases. This represents a clinical outcome used as ground truth.
8. The Sample Size for the Training Set
The document does not specify the sample size for the training set. It only describes the test set and the performance validation on it.
9. How the Ground Truth for the Training Set Was Established
The document does not describe how the ground truth for the training set was established. It focuses solely on the validation test set.
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July 16, 2020
Image /page/0/Picture/1 description: The image shows the logo of the U.S. Food and Drug Administration (FDA). On the left is the Department of Health & Human Services logo. To the right of that is the FDA logo, which is a blue square with the letters "FDA" in white. To the right of the blue square is the text "U.S. FOOD & DRUG ADMINISTRATION" in blue.
Zebra Medical Vision Ltd. % Flair Bar VP Operations Shefayim Commercial Center, P.O. Box 25 Shefayim, 6099000 ISRAEL
Re: K200905
Trade/Device Name: HealthMammo Regulation Number: 21 CFR 892.2080 Regulation Name: Radiological computer aided triage and notification software Regulatory Class: Class II Product Code: QFM Dated: June 17, 2020 Received: June 22, 2020
Dear Flair Bar:
We have reviewed your Section 510(k) premarket notification of intent to market the device referenced above and have determined the device is substantially equivalent (for the indications for use stated in the enclosure) to legally marketed predicate devices marketed in interstate commerce prior to May 28, 1976, the enactment date of the Medical Device Amendments, or to devices that have been reclassified in accordance with the provisions of the Federal Food, Drug, and Cosmetic Act (Act) that do not require approval of a premarket approval application (PMA). You may, therefore, market the device, subject to the general controls provisions of the Act. Although this letter refers to your product as a device, please be aware that some cleared products may instead be combination products. The 510(k) Premarket Notification Database located at https://www.accessdata.fda.gov/scripts/cdrh/cfdocs/cfpmn/pmn.cfm identifies combination product submissions. The general controls provisions of the Act include requirements for annual registration, listing of devices, good manufacturing practice, labeling, and prohibitions against misbranding and adulteration. Please note: CDRH does not evaluate information related to contract liability warranties. We remind you, however, that device labeling must be truthful and not misleading.
If your device is classified (see above) into either class II (Special Controls) or class III (PMA), it may be subject to additional controls. Existing major regulations affecting your device can be found in the Code of Federal Regulations, Title 21, Parts 800 to 898. In addition, FDA may publish further announcements concerning your device in the Federal Register.
Please be advised that FDA's issuance of a substantial equivalence determination does not mean that FDA has made a determination that your device complies with other requirements of the Act or any Federal statutes and regulations administered by other Federal agencies. You must comply with all the Act's requirements, including, but not limited to: registration and listing (21 CFR Part 807); labeling (21 CFR Part
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801); medical device reporting of medical device-related adverse events) (21 CFR 803) for devices or postmarketing safety reporting (21 CFR 4, Subpart B) for combination products (see https://www.fda.gov/combination-products/guidance-regulatory-information/postmarketing-safety-reportingcombination-products); good manufacturing practice requirements as set forth in the quality systems (QS) regulation (21 CFR Part 820) for devices or current good manufacturing practices (21 CFR 4, Subpart A) for combination products; and, if applicable, the electronic product radiation control provisions (Sections 531-542 of the Act); 21 CFR 1000-1050.
Also, please note the regulation entitled, "Misbranding by reference to premarket notification" (21 CFR Part 807.97). For questions regarding the reporting of adverse events under the MDR regulation (21 CFR Part 803), please go to https://www.fda.gov/medical-device-safety/medical-device-reportingmdr-how-report-medical-device-problems.
For comprehensive regulatory information about medical devices and radiation-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) K200905
Device Name HealthMammo
Indications for Use (Describe)
The Zebra HealthMammo is a passive notification-only, parallel-workflow software tool used by MQSA-qualified interpreting physicians to prioritize patients with suspicious findings in the medical care environment. HealthMammo utilizes an artificial intelligence algorithm to analyze 2D FFDM screening mammograms and flags those that are suggestive of the presence of at least one suspicious finding at the exam-level. HealthMammo produces an examlevel output to a PACS/Workstation for flagging the suspicious case and allows worklist prioritization.
MQSA-qualified interpreting physicians are responsible for reviewing each exam on a display approved for use in mammography according to the current standard of care. HealthMammo device is limited to the categorization of exams, does not provide any diagnostic information beyond triage and prioritization, does not remove images from the interpreting physician's worklist, and should not be used in lieu of full patient evaluation, or relied upon to make or confirm diagnosis.
The HealthMammo device is intended for use with complete 2D FFDM mammography exams acquired using validated FFDM systems only.
| 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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5. 510 (k) Summary
510(K) Summary - HealthMammo Zebra Medical Vision Ltd.
510(k) Number – K200905
- I. Applicant's Name: Zebra Medical Vision Ltd. Shefayim Commercial Center PO Box 25 Shefayim, 6099000 ISRAEL Telephone: +972-9-8827795 Fax: +972-9-8827795
Date Prepared: July 13, 2020
II. Device
Trade Name: HealthMammo
Classification Name: QFM - Radiological Computer-Assisted Prioritization Software
Regulation Number: 892.2080
Classification:
Class II, Radiology
III. Predicate Device:
The HealthMammo device is substantially equivalent to the following device:
| Proprietary Name | CmTriage |
|---|---|
| Premarket Notification | K183285 |
| Classification Name | Radiological Computer-Assisted Prioritization Software |
| Regulation Number | 21 CFR 892.2080 |
| Product Code | QFM |
| Regulatory Class | II |
IV. Device Description
Zebra's HealthMammo solution is a software product that automatically analyzes 2D FFDM screening mammograms and notifies PACS/workstation of the presence of suspicious findings in
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the scan. This passive-notification allows for worklist prioritization of the specific scan and assists clinicians in viewing prioritized scans before others. The device aim is to aid in prioritization and triage of radiological medical images only. It is a software tool for MQSA interpreting physicians reading mammograms and does not replace complete evaluation according to the standard of care.
The Zebra's HealthMammo device works in parallel to and in conjunction with the standard care of workflow. After a mammogram has been performed, a copy of the study is automatically retrieved and processed by the HealthMammo device. The device performs the analysis of the study and returns a notification about suspected finding to the PACS/workstation which flags it through the worklist interface or alternatively, the Zebra Worklist will notify the user through a desktop application. The clinician is then able to review the study earlier than in standard of care workflow.
The primary benefit of the product is the ability to reduce the time it takes to alert physicians to the presence of a suspicious finding. The software does not recommend treatment or provide a diagnosis. It is meant as a tool to assist in improved workload prioritization of suspicious cases. The final diagnosis is provided by a clinician after reviewing the scan itself..
The following modules compose the HealthMammo software:
Data input and validation: Following retrieval of a study, the validation feature assessed the input data (i.e. age, modality, view) to ensure compatibility for processing by the algorithm.
HealthMammo algorithm: Once a study has been validated, the algorithm analyzes the 2D FFDM screening mammogram for detection of suspected findings.
IMA Integration feature: The study analysis and the results of a successful study analysis is provided to IMA, to then be sent to the PACS/workstation for prioritization.
Error codes feature: In the case of a study failure during data validation or the analysis by the algorithm, an error is provided to the system.
V. Intended Use/Indication for Use:
The Zebra HealthMammo is a passive notification for prioritization-only, parallel-workflow software tool used by MQSA-qualified interpreting physicians to prioritize patients with suspicious findings in the medical care environment. HealthMammo utilizes an artificial intelligence algorithm to analyze 2D FFDM screening mammograms and flags those that are suggestive of the presence of at least one suspicious finding at the exam-level. HealthMammo produces an exam-level output to a PACS/Workstation for flagging the suspicious case and allows worklist prioritization.
MQSA-qualified interpreting physicians are responsible for reviewing each exam on a display approved for use in mammography according to the current standard of care. HealthMammo device is limited to the categorization of exams, does not provide any diagnostic information beyond triage and prioritization, does not remove images from the interpreting physician's
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worklist, and should not be used in lieu of full patient evaluation, or relied upon to make or confirm diagnosis.
The HealthMammo device is intended for use with complete 2D FFDM mammography exams acquired using validated FFDM systems only.
VI. Technological Characteristics Compared to Predicate Device:
The technological characteristics, e.g., overall design, mechanism of action, mode of operation, performance characteristics, etc., and the intended use of the HealthMammo device are substantially equivalent to the predicate device cited above.
A comparison of the technological characteristics with the predicate is summarized below.
| TechnologicalCharacteristics | Proposed DeviceHealthMammo | Predicate DevicecmTriage (K183285) | Summary |
|---|---|---|---|
| Indication forUse/Intended Use | The Zebra HealthMammo is apassive notification forprioritization-only, parallel-workflow software tool used byMQSA-qualified interpretingphysicians to prioritize patientswith suspicious findings in themedical care environment.HealthMammo utilizes anartificial intelligence algorithmto analyze 2D FFDM screeningmammograms and flags thosethat are suggestive of thepresence of at least onesuspicious finding at the exam-level. HealthMammo producesan exam-level output to aPACS/Workstation for flaggingthe suspicious case and allowsworklist prioritization. MQSA-qualified interpreting physiciansare responsible for reviewingeach exam on a displayapproved for use inmammography according to thecurrent standard of care.HealthMammo device is limitedto the categorization of exams,does not provide any diagnosticinformation beyond triage andprioritization, does not removeimages from the interpretingphysician's worklist, and shouldnot be used in lieu of full patientevaluation or relied upon to | cmTriage is a passive notificationfor prioritization-only, parallel-workflow software tool used byradiologists to prioritize specificpatients within the standard-of-care image worklist for 2D FFDMscreening mammograms.cmTriage uses an artificialintelligence algorithm to analyze2D FFDM screeningmammograms and flags those thatare suggestive of the presence ofat least one suspicious finding atthe exam level. These flags areviewed by the radiologist via theirPicture Archiving andCommunication System (PACS)worklist. The decision to usecmTriage codes and how to usecmTriage codes is ultimately upto the radiologist. cmTriage doesnot send a proactive alert directlyto the radiologist. Radiologists areresponsible for reviewing eachexam on a diagnostic vieweraccording to the current standardof care. cmTriage is limited to thecategorization of exams, does notprovide any diagnosticinformation beyond triage andprioritization, does not removeimages from the radiologist'sworklist, and should not be usedin lieu of full patient evaluation,or relied upon to make or confirm | Same |
| make or confirm diagnosis. TheHealthMammo device isintended for use with complete2D FFDM mammographyexams acquired using validatedFFDM systems only. | diagnosis. cmTriage is forprescription use only. | ||
| Notification-only,parallel workflowtool | Yes | Yes | Same |
| User | Interpreting physician | Radiologist | Same |
| Identify patients withprespecifiedclinical condition | Yes | Yes | Same |
| Alert to finding | Yes; passive notificationflagged for review | Yes; passive notification flaggedfor review | Same |
| Independent ofstandard of careworkflow | Yes; No cases are removed fromworklist | Yes; No cases are removed fromworklist | Same |
| Modality | FFDM screening mammograms | FFDM screening mammograms | Same |
| FFDM manufacturer | Hologic | Vendor agnostic | Different, thesubject deviceprocesses 2DFFDM scansacquired byHologic systems,while the predicatedevice may receivescans acquired fromalternative vendors. |
| Body part | Breast | Breast | Same |
| Artificial Intelligencealgorithm | Yes | Yes | Same |
| Limited to analysis ofimaging data | Yes | Yes | Same |
| Inclusion Criteria | - 2D FFDM screeningmammograms- Biopsy proven cancer studies(soft tissues and micro-calcifications)- BIRADS 1 and 2 normalcases with 2 year follow-up- Studies with 4 standard views(LCC, LMLO, RCC, RMLO) | - 2D FFDM screeningmammograms- Biopsy proven cancer studies(soft tissues and micro-calcifications)- BIRADS 1 and 2 normal caseswith 2 year follow-up- Studies with 4 standard views(LCC, LMLO, RCC, RMLO) | Same |
| Exclusion Criteria | - Studies that do not include all4 views | - Studies that do not include all 4views | Same |
| Digital Breast tomosynthesisstudies- 3D studiesStudies that do not comply withthe inclusion criteria | Digital Breast tomosynthesisstudies- 3D studiesStudies that do not comply withthe inclusion criteria | ||
| Aids promptidentification ofcases with indicatedfindings | Yes | Yes | Same |
| Multiple operatingpoints | Yes; 3 optional operating points | No; single operating point | Different,HealthMammoprovides anadditional 2operating points.Performancecomplies with DEN170073 Specialcontrol 1(iii). |
| Preview Images | Presentation of notification andpreview of the study for initialassessment not meant fordiagnostic purposes. The deviceoperates in parallel with thestandard of care, which remainsthe default option for all cases. | Presentation of notification andpreview of the study for initialassessment not meant fordiagnostic purposes. The deviceoperates in parallel with thestandard of care, which remainsthe default option for all cases. | Same |
| Where results arereceived | PACS / Workstation | PACS / Workstation | Same |
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VII. Performance Data:
Safety and performance of HealthMammo has been evaluated and verified in accordance with software specifications and applicable performance standards through Software Development and Validation & Verification Process to ensure performance according to specifications, User Requirements and Federal Regulations and Guidance documents, "Guidance for the Content of Premarket Submissions for Software Contained in Medical Devices"
The performance of the HealthMammo device has been validated in a performance study for triage of 2D FFDM screening mammogram cases. The data included a retrospective cohort of 835 anonymized 2D FFDM screening mammograms from the USA, UK, and Israel, including 435 cases positive with biopsy confirmed cancers and 400 cases negative for breast cancer (BIRADS1 and BIRADS2 with a two-year follow up of a negative diagnosis), as well as confounding variables.
The test set was constructed to ensure that confounding factors present in the population were addressed in the data and consistent with the population of women undergoing breast cancer screening. Confounding factors that were considered include: 1) Lesion Type; 2) Breast Density
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Image /page/8/Picture/0 description: The image shows the Zebra Medical Vision logo. The logo consists of a yellow horizontal line on the left, followed by a stylized black and white zebra-striped "Z" symbol. To the right of the "Z" symbol is the word "zebra" in black, with the words "MEDICAL.VISION" in smaller black font underneath.
- Age; and 4) Histology Type. The stand-alone detection and triage accuracy was measured on this cohort versus the ground truth.
The HealthMammo device detection accuracy was validated for three different operating points and met the prespecified performance goals for accuracy in terms of the AUC as well as sensitivity, and specificity for all defined operating points. Overall, the HealthMammo was able to demonstrate an area under the ROC curve (AUC) of 0.9661 (95% CI: [0.9552, 0.9769]), which is comparable with the predicate device, and exceeds the required technical method under the QFM product code for effective triage with an AUC >95%.
Sensitivity and Specificity goals were set based on the performance in the Breast Cancer Surveillance Consortium (BCSC) study and compared to the performance of the predicate device, CmTriage (K183285). The "standard mode," "high specificity," and "high sensitivity" operating points correspond to the reported human performance in the BCSC study, and are also established by the predicate device.
Sensitivity and specificity of the HealthMammo was reported for the three operating points, all of which met their performance goals. The first operating point, "standard mode," reported a sensitivity of 89.89% (95% CI: [86.69%;92.38%]) and a specificity of 90.75% (95% CI: [87.51%;93.21%]). The second operating point, "high sensitivity" reported a sensitivity of 94.02% (95% CI: [91.39%;95.89%]) and a specificity of 83.50% (95% CI: [79.55%;86.82%]). The "high specificity" operating point, reported a sensitivity of 84.14% (95% CI: [80.41%;87.27%]) and a specificity of 94.00% (95% CI: [91.23%;95.94%]). These three operating points demonstrated good performance compared with the standard of care reported in the BCSC study, and were found substantially equivalent to the predicate device (K183285).
Furthermore, we assessed the processing time of the HealthMammo which reflects the time it takes for the device to analyze the study and send a notification to the worklist. The average processing time of the HealthMammo was 2.9 minutes, a timing that is substantially equivalent to the predicate.
VIII. Conclusion
The subject HealthMammo device and the CmTriage predicate device (K183285) are both software-only devices intended to aid in triage of radiological images, within the standard of care workflow. The labeling of both devices are limited to the categorization of exams and are not to be used in-lieu of full patient evaluation or relied upon to make or confirm diagnosis.
Both devices operate in parallel to the standard of care workflow in the sense that they do not change the original image, and do not remove cases from the standard of care. The minor differences between the subject device and the predicate raise no new issues of safety or effectiveness. In addition, performance testing demonstrates that the HealthMammo performs as
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intended. The HealthMammo device is therefore substantially equivalent to the CmTriage predicate.
§ 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.