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.

Device Description

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.

AI/ML Overview

Here's a breakdown of the acceptance criteria and study details for the HealthPPT device, based on the provided document:

1. Table of Acceptance Criteria and Reported Device Performance

Metric	Acceptance Criterion (implicitly met if "reached performance goal")	Reported Device Performance
Overall Accuracy	Comparable to predicate device & exceeds technical method	AUC: 96.75% (95% CI: [94.28%, 99.21%])
Operating Point 1 (Balanced Sensitivity & Specificity)	Reached performance goal	Sensitivity: 92.52% (95% CI: [85.94%;96.16%]), Specificity: 92.66% (95% CI: [86.18%;96.23%])
Operating Point 2 (High Specificity)	Reached performance goal	Sensitivity: 80.37% (95% CL: [71.85%;86.79%]), Specificity: 97.25% (95% CI: [92.22%;99.06%])
Processing Time	Lower than predicate device	Average performance time: 4.78 seconds

2. Sample Size and Data Provenance for Test Set

Sample Size: 216 anonymized Chest X-ray cases.
Data Provenance: Retrospective cohort from the USA and OUS (Outside the US).
- 107 cases positive for Pneumoperitoneum.
- 109 cases negative for Pneumoperitoneum, including confounding imaging factors.

3. Number of Experts and Qualifications for Ground Truth

Number of Experts: Three.
Qualifications: US Board-Certified Radiologists.

4. Adjudication Method

The document states the validation data set was "trued (ground truth) by three US Board-Certified Radiologists." It does not explicitly mention an adjudication method like 2+1 or 3+1, but implies consensus among the three radiologists to establish the ground truth.

5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study

No, a Multi-Reader Multi-Case (MRMC) comparative effectiveness study was not explicitly mentioned or presented in the document. The study focused on the standalone performance of the HealthPPT device.

6. Standalone Performance Study

Yes, a standalone (algorithm only without human-in-the-loop performance) study was done. The document explicitly states: "The stand-alone detection accuracy was measured on this cohort respective to the ground truth."

7. Type of Ground Truth Used

Expert consensus. The ground truth was established by "three US Board-Certified Radiologists."

8. Sample Size for the Training Set

The document does not specify the sample size used for the training set. It only details the test/validation set.

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

The document does not specify how the ground truth for the training set was established. It only details the establishment of ground truth for the validation data set.

Summary

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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)

Type of Use (Select one or both, as applicable)
Prescription Use (Part 21 CFR 801 Subpart D)	Over-The-Counter Use (21 CFR 801 Subpart C)

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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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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.

TechnologicalCharacteristics	Proposed DeviceHealthPPT	Predicate DeviceAIMI-Triage CXR PTX(K193300)	Summary
Indication forUse/Intended Use	The Zebra HealthPPT deviceis a software workflow tooldesigned to aid the clinicalassessment of adult frontalChest X-Ray cases withfeatures suggestive ofpneumoperitoneum in themedical care environment.HealthPPT analyzes casesusing an artificial intelligencealgorithm to identifysuspected findings. It makescase-level output available toa PACS/workstation forworklist prioritization or	The AIMI-Triage CXRPTX Application is anotification-only triageworkflow tool for use byhospital networks andclinics to identify andhelp prioritize chestX-rays acquired in theacute setting for reviewby hospital radiologists.The device operates inparallel to andindependent of standardof care imageinterpretation workflow.	Similar expect forlesion type
	triage. HealthPPT is notintended to direct attention toanomalies other thanpneumoperitoneum.Notifications includecompressed preview imagesthat are meant forinformational purposes onlyand not intended fordiagnostic use beyondnotification. The device doesnot alter the original medicalimage and is not intended tobe used as a diagnosticdevice. Its results are not	Specifically, the deviceuses an artificialintelligence algorithm toanalyze images forfeatures suggestive ofmoderate to large sizedpneumothorax; it makescaselevel output availableto a PACS/workstationfor worklist prioritizationor triage. Identification ofsuspected cases ofmoderate to large sizedpneumothorax is not fordiagnostic use beyond
	intended to be used on astand-alone basis for clinicaldecision-making nor is itintended to rule outpneumoperitoneum orotherwise preclude clinicalassessment of X-Ray cases.	notification. TheAIMI-Triage CXR PTXApplication is limited toanalysis of imaging dataas a guide to possibleurgency of adult chestX-ray image review, andshould not be used in lieuof full patient evaluationor relied upon to make orconfirm diagnoses.Notified radiologists areresponsible for engagingin appropriate patientevaluation as per localhospital procedure beforemaking care-relateddecisions or requests. Thedevice does not replacereview and diagnosis ofthe X-rays byradiologists. The device isnot intended to be usedwith plain film X-rays.
Notification-only,parallel workflowtool	Yes	Yes	Same
User	Radiologist	Radiologist	Same
Radiologicalimages format	DICOM	DICOM	Same
Identify patientswith prespecifiedclinical condition	Yes	Yes	Same
Clinical condition	Pneumoperitoneum	Pneumothorax	Different but asper the productclassificationdefinition, bothidentify "timesensitive imaging."
Alert to finding	Yes; notification flagged forreview on hospital worklist orZebra application	Yes; notification flaggedfor review	Similar,HealthPPT can bedirectly integratedfor notification onthe hospitalworklist or on theZebra application.Both notificationsoperate in parallel
			with the standardof care.
Independent ofstandard of careworkflow	Yes; No cases are removedfrom worklist	Yes; No cases areremoved from worklist	Same
Modality	Chest X-Ray	Chest X-Ray	Same
ArtificialIntelligencealgorithm	Yes	Yes	Same
Limited to analysisof imaging data	Yes	Yes	Same
Aids promptidentification ofcases withindicated findings	Yes	Yes	Same
Preview Image	Presentation of a compressedpreview image for initialassessment, not meant fordiagnostic purposes.The device operated inparallel with the standard ofcare, which remains thedefault option for all cases.	Presentation ofnotification for initialassessment not meant fordiagnostic purposes. Thedevice operates in parallelwith the standard of care,which remains the defaultoption for all cases.	Similar,HealthPPTprovides anadditionalcompressed imageas a preview only,not for diagnosticuse.
Multiple operatingpoints	Yes; 2 optional operatingpoints	No; single operating point	Different, but alloperating pointscomply with DEN170073 Specialcontrol 1(iii).
Where results arereceived	PACS / Workstation	PACS / Workstation	Same

A comparison of the technological characteristics with the predicate is summarized below.

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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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(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.

Regulation Number and Section

§ 892.2080 Radiological computer aided triage and notification software.

(a)
Identification. Radiological computer aided triage and notification software is an image processing prescription device intended to aid in prioritization and triage of radiological medical images. The device notifies a designated list of clinicians of the availability of time sensitive radiological medical images for review based on computer aided image analysis of those images performed by the device. The device does not mark, highlight, or direct users' attention to a specific location in the original image. The device does not remove cases from a reading queue. The device operates in parallel with the standard of care, which remains the default option for all cases.(b)
Classification. Class II (special controls). The special controls for this device are:(1) Design verification and validation must include:
(i) A detailed description of the notification and triage algorithms and all underlying image analysis algorithms including, but not limited to, a detailed description of the algorithm inputs and outputs, each major component or block, how the algorithm affects or relates to clinical practice or patient care, and any algorithm limitations.
(ii) A detailed description of pre-specified performance testing protocols and dataset(s) used to assess whether the device will provide effective triage (
e.g., improved time to review of prioritized images for pre-specified clinicians).(iii) Results from performance testing that demonstrate that the device will provide effective triage. The performance assessment must be based on an appropriate measure to estimate the clinical effectiveness. The test dataset must contain sufficient numbers of cases from important cohorts (
e.g., subsets defined by clinically relevant confounders, effect modifiers, associated diseases, and subsets defined by image acquisition characteristics) such that the performance estimates and confidence intervals for these individual subsets can be characterized with the device for the intended use population and imaging equipment.(iv) Stand-alone performance testing protocols and results of the device.
(v) Appropriate software documentation (
e.g., device hazard analysis; software requirements specification document; software design specification document; traceability analysis; description of verification and validation activities including system level test protocol, pass/fail criteria, and results).(2) Labeling must include the following:
(i) A detailed description of the patient population for which the device is indicated for use;
(ii) A detailed description of the intended user and user training that addresses appropriate use protocols for the device;
(iii) Discussion of warnings, precautions, and limitations must include situations in which the device may fail or may not operate at its expected performance level (
e.g., poor image quality for certain subpopulations), as applicable;(iv) A detailed description of compatible imaging hardware, imaging protocols, and requirements for input images;
(v) Device operating instructions; and
(vi) A detailed summary of the performance testing, including: test methods, dataset characteristics, triage effectiveness (
e.g., improved time to review of prioritized images for pre-specified clinicians), diagnostic accuracy of algorithms informing triage decision, and results with associated statistical uncertainty (e.g., confidence intervals), including a summary of subanalyses on case distributions stratified by relevant confounders, such as lesion and organ characteristics, disease stages, and imaging equipment.