K211803 · Zebra Medical Vision, Ltd. · QFM · Dec 15, 2021 · Radiology
Device Facts
Record ID
K211803
Device Name
HealthPPT
Applicant
Zebra Medical Vision, Ltd.
Product Code
QFM · Radiology
Decision Date
Dec 15, 2021
Decision
SESE
Submission Type
Traditional
Regulation
21 CFR 892.2080
Device Class
Class 2
Attributes
AI/ML, Software as a Medical Device
AI Performance
Output
Algorithm
Acceptance
Observed
Dev DS
Dev Readers
Test DS
Test Readers
Pneumoperitoneum detection
—
—
AUC: 96.75% (95% CI: [94.28%, 99.21%])
—
—
Retrospective cohort of 216 anonymized Chest X-ray cases.
3 (US Board-Certified Radiologists)
Indications for Use
The Zebra HealthPPT device is a software workflow tool designed to aid the clinical assessment of adult frontal Chest X-Ray cases with features suggestive of pneumoperitoneum in the medical care environment. HealthPPT analyzes cases using an artificial intelligence algorithm to identify suspected findings. It makes case-level output available to a PACS/workstation for worklist prioritization or triage. HealthPPT is not intended to direct attention to anomalies other than pneumoperitoneum. Notifications include compressed preview images that are meant for informational purposes only and not intended for diagnostic use beyond notification. The device does not alter the original medical image and is not intended to be used as a diagnostic device. Its results are not intended to be used on a stand-alone basis for clinical decision-making nor is it intended to rule out pneumoperitoneum or otherwise preclude clinical assessment of X-Ray cases.
Device Story
HealthPPT is a software workflow tool for adult frontal chest X-rays; operates in parallel to standard clinical workflows. Input: DICOM chest X-ray images. Processing: AI algorithm analyzes images for features suggestive of pneumoperitoneum. Output: Case-level notification sent to PACS/workstation for worklist prioritization; includes compressed preview image for informational purposes. Used in clinical settings by radiologists. Does not provide diagnosis or treatment recommendations; does not alter original images. Benefits: Enables earlier review of critical cases by prioritizing suspected pneumoperitoneum findings, potentially improving clinical workflow efficiency.
Clinical Evidence
Retrospective study of 216 anonymized chest X-ray cases (107 positive for pneumoperitoneum, 109 negative). Ground truth established by three US Board-Certified Radiologists. AUC: 96.75% (95% CI: 94.28%-99.21%). Operating point 1 (balanced): sensitivity 92.52%, specificity 92.66%. Operating point 2 (high-specificity): sensitivity 80.37%, specificity 97.25%. Average processing time: 4.78 seconds.
Technological Characteristics
Software-only device; DICOM image input; AI-based algorithm for image analysis; operates on PACS/workstation or standalone desktop application; cloud/networked integration for workflow; no hardware components; software development follows FDA guidance for medical device software.
Indications for Use
Indicated for adult patients undergoing frontal Chest X-Ray in medical care environments to aid clinical assessment by identifying features suggestive of pneumoperitoneum for worklist prioritization.
Regulatory Classification
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.
Special Controls
Radiological computer aided triage and notification software must comply with the following special controls: 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. Standalone 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. 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.
*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.
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Zebra Medical Vision Ltd. % Shlomit Cymbalista Head of Regulatory Affairs Nano AI Ltd./Shefayim Commercial Center PO Box 25 Sefayim, 6099000 ISRAEL
December 15, 2021
Re: K211803
Trade/Device Name: HealthPPT Regulation Number: 21 CFR 892.2080 Regulation Name: Radiological computer aided triage and notification software Regulatory Class: Class II Product Code: QFM Dated: November 7, 2021 Received: November 10, 2021
Dear Shlomit Cymbalista:
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 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) K211803
Device Name HealthPPT
### Indications for Use (Describe)
The HealthPPT device is a software workflow tool designed to aid the clinical assessment of adult frontal Chest X-Ray cases with features suggestive of pneumoperitoneum in the medical care environment. HealthPPT analyzes cases using an artificial intelligence algorithm to identify suspected findings. It makes case-level output available to a PACS/workstation for worklist prioritization or triage. HealthPPT is not intended to direct attention to anomalies other than pneumoperitoneum. Notifications include compressed preview images that are meant for informational purposes only and not intended for diagnostic use beyond notification. The device does not alter the original medical image and is not intended to be used as a diagnostic device. Its results are not intended to be used on a stand-alone basis for clinical decision-making nor is it intended to rule out pneumoperitoneum or otherwise preclude clinical assessment of X-Ray cases.
| Type of Use (Select one or both, as applicable) | |
|---------------------------------------------------------------|--------------------------------------------------------------|
| <div> Prescription Use (Part 21 CFR 801 Subpart D) </div> | <div> Over-The-Counter Use (21 CFR 801 Subpart C) </div> |
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Image /page/3/Picture/0 description: The image contains the logo for Nanox AI. The logo consists of two parts: a stylized graphic on the left and the text "NANOXAI" on the right. The graphic is an abstract shape with a grid-like pattern, colored with a gradient from yellow to blue. The text "NANOXAI" is written in a sans-serif font, with "NANOX" in blue and "AI" in yellow.
# 510(K) Summary - HealthPPT Nanox AI Ltd.
## 510(k) Number – K211803
- I. Applicant's Name: Nanox AI Ltd. Shefayim Commercial Center PO Box 25 Shefayim, 6099000 ISRAEL Telephone: +972-9-8827795 Fax: +972-9-8827795
December 13, 2021 Date Prepared:
#### II. Device
Trade Name: HealthPPT
### Classification Name:
QFM - Radiological Computer-Assisted Prioritization Software
## Regulation Number: 892.2080
### Classification:
Class II, Radiology
#### III. Predicate Device:
The HealthPPT device is substantially equivalent to the following device:
| Proprietary Name | AIMI-Triage CXR PTX |
|------------------------|--------------------------------------------------------|
| Premarket Notification | K193300 |
| Classification Name | Radiological Computer-Assisted Prioritization Software |
| Regulation Number | 21 CFR 892.2080 |
| Product Code | QFM |
| Regulatory Class | II |
#### Device Description IV.
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Image /page/4/Picture/0 description: The image contains the logo for NanoXAI. The logo consists of two parts: a stylized graphic on the left and the company name on the right. The graphic is an abstract shape with curved lines, colored in a gradient from yellow at the top to blue at the bottom. The company name "NANOXAI" is written in a sans-serif font, with "NANOX" in blue and "AI" in yellow.
The HealthPPT solution is a software product that automatically identifies suspected findings on chest x-rays (e.g. pneumoperitoneum) and notifies PACS/workstation of the presence of this critical finding in the scan. This notification allows for prioritization of the identified scan and assists clinicians in viewing the prioritized scan before others. The device aim is to aid in prioritization and triage of radiological medical images only.
The software is automatic and is capable of analyzing PA or AP chest x-rays. If a suspected finding is found in a scan, the alert is automatically sent to the PACS/workstation used by the radiologist or to a standalone desktop application in parallel with the ongoing standard of care. The PACS/workstation prioritizes and displays the study through its worklist interface. The ZebrAInsight standalone application includes a compressed preview image meant for informational purposes only and is not intended for diagnostic use.
The HealthPPT device works in parallel to and in conjunction with the standard care of workflow. After a chest x-ray has been performed, a copy of the study is automatically retrieved and processed by the HealthPPT device performs the analysis of the study and returns a notification about the relevant pathology to the PACS/workstation for prioritization. The clinician is then able to review the study earlier than in standard of care workflow.
The software does not recommend treatment or provide a diagnosis. It is meant as a tool to assist in improved workload prioritization of critical cases. The final diagnosis is provided by a radiologist after reviewing the scan itself.
The following modules compose the HealthPPT software for Pneumoperitoneum:
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.
Pneumoperitoneum algorithm: Once a study has been validated, the algorithm analyzes the frontal chest x-ray for detection of suspected finding suggestive of pneumoperitoneum.
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 HealthPPT device is a software workflow tool designed to aid the clinical assessment of adult frontal Chest X-Ray cases with features suggestive of pneumoperitoneum in the medical care environment. HealthPPT analyzes cases using an artificial intelligence algorithm to identify suspected findings. It makes case-level output available to a PACS/workstation for worklist
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Image /page/5/Picture/0 description: The image contains the logo for NanoXAI. The logo consists of two parts: a stylized graphic on the left and the company name on the right. The graphic is an abstract shape with curved lines in shades of blue and yellow, resembling a stylized flower or butterfly. To the right of the graphic is the company name "NANOXAI" in blue, with the "AI" portion in yellow.
prioritization or triage. HealthPPT is not intended to direct attention to anomalies other than pneumoperitoneum. Notifications include compressed preview images that are meant for informational purposes only and not intended for diagnostic use beyond notification. The device does not alter the original medical image and is not intended to be used as a diagnostic device. Its results are not intended to be used on a stand-alone basis for clinical decision-making nor is it intended to rule out pneumoperitoneum or otherwise preclude clinical assessment of X-Ray cases.
#### 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 HealthPPT device are substantially equivalent to the predicate device cited above.
| Technological<br>Characteristics | Proposed Device<br>HealthPPT | Predicate Device<br>AIMI-Triage CXR PTX<br>(K193300) | Summary |
|----------------------------------------------------------------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| Indication for<br>Use/Intended Use | The Zebra HealthPPT device<br>is a software workflow tool<br>designed to aid the clinical<br>assessment of adult frontal<br>Chest X-Ray cases with<br>features suggestive of<br>pneumoperitoneum in the<br>medical care environment.<br>HealthPPT analyzes cases<br>using an artificial intelligence<br>algorithm to identify<br>suspected findings. It makes<br>case-level output available to<br>a PACS/workstation for<br>worklist prioritization or | The AIMI-Triage CXR<br>PTX Application is a<br>notification-only triage<br>workflow tool for use by<br>hospital networks and<br>clinics to identify and<br>help prioritize chest<br>X-rays acquired in the<br>acute setting for review<br>by hospital radiologists.<br>The device operates in<br>parallel to and<br>independent of standard<br>of care image<br>interpretation workflow. | Similar expect for<br>lesion type |
| | triage. HealthPPT is not<br>intended to direct attention to<br>anomalies other than<br>pneumoperitoneum.<br>Notifications include<br>compressed preview images<br>that are meant for<br>informational purposes only<br>and not intended for<br>diagnostic use beyond<br>notification. The device does<br>not alter the original medical<br>image and is not intended to<br>be used as a diagnostic<br>device. Its results are not | Specifically, the device<br>uses an artificial<br>intelligence algorithm to<br>analyze images for<br>features suggestive of<br>moderate to large sized<br>pneumothorax; it makes<br>caselevel output available<br>to a PACS/workstation<br>for worklist prioritization<br>or triage. Identification of<br>suspected cases of<br>moderate to large sized<br>pneumothorax is not for<br>diagnostic use beyond | |
| | intended to be used on a<br>stand-alone basis for clinical<br>decision-making nor is it<br>intended to rule out<br>pneumoperitoneum or<br>otherwise preclude clinical<br>assessment of X-Ray cases. | notification. The<br>AIMI-Triage CXR PTX<br>Application is limited to<br>analysis of imaging data<br>as a guide to possible<br>urgency of adult chest<br>X-ray image review, and<br>should not be used in lieu<br>of full patient evaluation<br>or relied upon to make or<br>confirm diagnoses.<br>Notified radiologists are<br>responsible for engaging<br>in appropriate patient<br>evaluation as per local<br>hospital procedure before<br>making care-related<br>decisions or requests. The<br>device does not replace<br>review and diagnosis of<br>the X-rays by<br>radiologists. The device is<br>not intended to be used<br>with plain film X-rays. | |
| Notification-only,<br>parallel workflow<br>tool | Yes | Yes | Same |
| User | Radiologist | Radiologist | Same |
| Radiological<br>images format | DICOM | DICOM | Same |
| Identify patients<br>with prespecified<br>clinical condition | Yes | Yes | Same |
| Clinical condition | Pneumoperitoneum | Pneumothorax | Different but as<br>per the product<br>classification<br>definition, both<br>identify "time<br>sensitive imaging." |
| Alert to finding | Yes; notification flagged for<br>review on hospital worklist or<br>Zebra application | Yes; notification flagged<br>for review | Similar,<br>HealthPPT can be<br>directly integrated<br>for notification on<br>the hospital<br>worklist or on the<br>Zebra application.<br>Both notifications<br>operate in parallel |
| | | | with the standard<br>of care. |
| Independent of<br>standard of care<br>workflow | Yes; No cases are removed<br>from worklist | Yes; No cases are<br>removed from worklist | Same |
| Modality | Chest X-Ray | Chest X-Ray | Same |
| Artificial<br>Intelligence<br>algorithm | Yes | Yes | Same |
| Limited to analysis<br>of imaging data | Yes | Yes | Same |
| Aids prompt<br>identification of<br>cases with<br>indicated findings | Yes | Yes | Same |
| Preview Image | Presentation of a compressed<br>preview image for initial<br>assessment, not meant for<br>diagnostic purposes.<br>The device operated in<br>parallel with the standard of<br>care, which remains the<br>default option for all cases. | Presentation of<br>notification for initial<br>assessment not meant for<br>diagnostic purposes. The<br>device operates in parallel<br>with the standard of care,<br>which remains the default<br>option for all cases. | Similar,<br>HealthPPT<br>provides an<br>additional<br>compressed image<br>as a preview only,<br>not for diagnostic<br>use. |
| Multiple operating<br>points | Yes; 2 optional operating<br>points | No; single operating point | Different, but all<br>operating points<br>comply with DEN<br>170073 Special<br>control 1(iii). |
| Where results are<br>received | PACS / Workstation | PACS / Workstation | Same |
A comparison of the technological characteristics with the predicate is summarized below.
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Image /page/6/Picture/0 description: The image contains the logo for NanoX AI. The logo consists of two parts: a stylized graphic on the left and the company name on the right. The graphic is an abstract shape with a grid-like pattern, colored in shades of blue and yellow. The text "NANOXAI" is written in blue, except for the "AI" which is in yellow.
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Image /page/7/Picture/0 description: The image contains the logo for Nanox AI. The logo consists of two parts: a stylized graphic on the left and the text "NANOXAI" on the right. The graphic is a symmetrical design with curved lines, colored in shades of blue and yellow. The text "NANOXAI" is written in a sans-serif font, with "NANOX" in blue and "AI" in yellow.
#### VII. Performance Data:
Safety and performance of HealthPPT 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 HealthPPT device has been validated in a performance study for triage of time sensitive chest X-Ray cases. The data included a retrospective cohort of 216 anonymized Chest X-ray cases from the USA and OUS, including 107 cases positive for Pneumoperitoneum and 109 cases negative for Pneumoperitoneum, as well as confounding imaging factors. The validation data set was truthed (ground truth) by three US Board-Certified Radiologists
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Image /page/8/Picture/0 description: The image contains the logo for NanoX AI. The logo consists of a geometric shape on the left and the text "NANOX AI" on the right. The geometric shape is a stylized representation of a sphere or globe, with a grid-like pattern. The left side of the text is blue, while the right side is yellow.
(truthers). The stand-alone detection accuracy was measured on this cohort respective to the ground truth.
The HealthPPT device detection accuracy met the accuracy performance goals for AUC, and the sensitivity, and specificity for two defined operating points. Overall, the HealthPPT was able to demonstrate an area under the curve (AUC) of 96.75% (95% CI: [94.28%, 99.21%]), which is both comparable to the predicate device, and exceeds the required technical method under the QFM product code. The sensitivity and specificity of the HealthPPT was reported for two operating points. The first "balanced sensitivity and specificity" (default) operating point demonstrated a sensitivity of 92.52% (95% CI: [85.94%;96.16%]) and a specificity of 92.66% (95% CI: [86.18%;96.23%]). The second "high-specificity" operating point reported a sensitivity of 80.37% (95% CL: [71.85%;86.79%]) and a specificity of 97.25% (95% CI: [92.22%;99.06%]). Both operating points reached their performance goal.
In addition, we assessed the time it takes for the HealthPPT device to analyze the study and send a result. The average performance time of the HealthPPT was 4.78 seconds, which is significantly lower than the time reported by the predicate device (20.3 seconds).
## VIII. Conclusion
The subject HealthPPT device and the AIMI-Triage CXR PTX (K193300) predicate device are both software-only devices intended to aid in triage of radiological images, independent of and in parallel to the standard of care workflow. Both devices incorporate an artificial intelligence algorithm. 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, do not provide any marking, 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 HealthPPT performs as intended. The HealthPPT device is therefore substantially equivalent to the AIMI-Triage CXR PTX predicate.
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Scroll right in the search results table. The intended use column is extracted for you; no need to dig into the 510(k) summary PDF.
What does the AI Performance sub-table show, and why is it useful?
Output name, acceptance criteria, observed values, development dataset description, and test dataset description. It's the same format we use for regulatory strategy output and Fast 510(k) input, and the fastest high-level fingerprint of an AI device. AI-generated but reliable in practice.
3. Judging fuzzy relevance
Fuzzy results trail off in relevance as you scroll. Use three signals to decide how far down to go: the fuzzy badge explanations, the intended use column, and whether your target output (e.g., Cobb angle) still appears in the AI Performance sub-table. Once it stops appearing, you're past the relevant zone. A top hit with a low score (~0.4) and a stretched explanation is a hint the closest predicates are far away — the project may be headed for De Novo. Note the fuzzy search is a pattern match: it doesn't handle negation ("not") well, and hardware devices can appear — filter by SaMD/AI ML to cut them.
How do you judge how far down fuzzy search results to go?
Use the relevancy signals: the fuzzy badge explanations, the intended use column, and whether the target output (e.g., Cobb angle) still appears in the AI Performance sub-table. Once it stops appearing, results are trailing off in relevancy.
4. Device detail page: chat and citations
Click a device name to open its detail page: device facts on the left, a chat window on the right. Ask something like "Describe the training data". The answer carries little citation bubbles — click one to jump to the highlighted passage in the source PDF, so you can verify every AI answer against the document. There's also a Download PDF button for sharing.
How do you verify an AI chat answer on the device detail page?
Click the citation bubbles to jump to the relevant highlight in the source document.
Reading rule for every project: how many summaries do you read in full?
At least the three most relevant 510(k) or De Novo summaries, in full. After that, use targeted chat questions to confirm your memory quickly. The tool supports this professional habit — it doesn't replace it.
5. Side-by-side comparison
Select multiple rows in the results table (aim for under ~10), then open the PDF Viewer tab. Ask one question — it goes to all selected devices in parallel, each with citations. This is the fastest way to compare and contrast devices: training data, PCCP scope, how they handled adding new scanners, and so on.
What does the side-by-side PDF viewer mode do?
Select multiple devices, open the PDF viewer tab, and ask one question (e.g., "Describe the training data"). It queries all selected devices simultaneously with citations, so you can compare and contrast quickly.
6. Collections
With rows selected, go to the Collections tab and create a labeled collection (e.g., "Cobb Angle Project"). Reload that selection any time — before a client call, pull up the collection and ask questions across all of its devices at once.
How do you save a set of selected devices for later use?
Select the rows, go to the Collections tab, and create a labeled collection (e.g., "Cobb Angle Project"). You can reload the selection anytime and carry it into the PDF viewer and other tabs that support selections.
7. Product codes and the regulations tree
Click a product code in the results to jump to it in the regulations tree — identification text, sibling product codes, and devices you can open in a PDF viewer on the right. Click a regulation number to see its identification, special controls, and related product codes. You can also search by product code or regulation number at the top of the tree. Always read the special controls if any exist for your device — it broadens your search and sharpens pre-kickoff research.
What can you do from the regulations tree view?
Browse product codes and regulation numbers, read the identification text and special controls, browse sibling product codes, open device PDFs on the right, and search by product code or regulation number at the top of the tree.
8. Chart view
Click Show Chart and segment by regulation number (or product code) to see which regulations dominate your result set. Clicking a regulation takes you into the regulations tree. Great for spotting that most matches are, say, hardware laparoscopic devices — a cue to go back and filter.
How do you see which regulations dominate a search result set?
Click "Show Chart" and segment by Regulation Number. Clicking a regulation takes you to the regulations tree.
9. The predicate graph
Open the Predicates tab for a family-tree view of predicate relationships. Click a node to trace its parents and children; selections from search carry over pre-selected. Commonly predicated devices are worth reading — a lot of people predicated them for a reason. The visual lineage is also handy on client calls, e.g. to show how a predicate family evolved and justify why your predicate still holds.
In the predicate graph, why are commonly predicated devices worth reading?
A lot of people predicated them for a reason. Clicking a node traces parents and children, and selections from search carry over pre-selected.
10. Embeddings: the galaxy map
The Embeddings tab plots every matching document in a 2-D "galaxy map" where semantically similar devices cluster together. Hover or click clusters to explore, and let AI label the clusters for you. Embeddings beat product codes for grouping: two devices can carry different product codes (LLZ vs. QIH) yet do the same thing — the embedding captures the meaning of the intended use and device story. This is also exactly how retrieval-augmented generation (RAG) works under the hood, and it makes a great visual on client calls.
Try it yourself
Head to the search page and work through a few of these AI/ML fuzzy searches to build intuition: perivascular fat on CT · aortic valve calcification opportunistic screening on noncontrast CT · breast cancer prediction on digital pathology slides · autism detection · gestational age prediction · a hearing aid that can also detect a pulse · foundation model based analysis of ECG · large language models · penetration test. Watch how the relevance scores, intended use, and AI Performance tables tell you when results stop being meaningful.
