Accipiolx is a software workflow tool designed to aid in prioritizing the clinical assessment of adult non-contrast head CT cases with features suggestive of acute intracranial hemorrhage in the acute care environment. Accipiolx 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.

Accipiolx is not intended to direct attention to specific portions of an image or to anomalies other than acute intracranial hemorrhage. Its results are not intended to be used on a stand-alone basis for clinical decision-making nor is it intended to rule out hemorrhage or otherwise preclude clinical assessment of CT cases.

Device Description

Accipiolx is a software device designed to be installed within healthcare facility radiology networks to identify and prioritize non-contrast head CT (NCCT) scans based on algorithmically-identified findings of acute intracranial hemorrhage (alCH). The device, which utilizes deep learning technologies, facilitates prioritization of CT scans containing findings of alCH. Accipiolx receives CT scans identified by the Accipio Agent or other compatible Medical Image Communications Device (MICD), processes them using algorithmic methods involving execution of multiple computational steps to identify suspected presence of alCH, and generates a results file to be transferred by the Accipio Agent or a similar MICD device for output to a PACS system or workstation for worklist prioritization.

Accipiolx works in parallel to and in conjunction with the standard care of workflow. After a CT scan has been performed, a copy of the study is automatically retrieved and processed by Accipiolx. The device performs identification and classification of objects consistent with alCH, and provides a case-level indicator which facilitates prioritization of cases with potential acute hemorrhagic findings for urgent review.

AI/ML Overview

Here's a breakdown of the acceptance criteria and study details for the MaxQ AI Accipiolx device, based on the provided text:

1. Table of Acceptance Criteria & Reported Device Performance

Performance Metric	Acceptance Criteria (Predefined Goals)	Reported Device Performance (Accipiolx K201310)	Predicate Device Performance (Accipiolx K182177)
Sensitivity	Not explicitly stated as a number for the acceptance criteria but implied to be high, and the reported performance is compared favorably to the predicate.	97% (95% CI: 92.8% - 98.8%)	92% (95% CI: 87.29 - 95.68%)
Specificity	Not explicitly stated as a number for the acceptance criteria but implied to be high, and the reported performance is compared favorably to the predicate.	93% (95% CI: 88.6% - 96.6%)	86% (95% CI: 80.18 - 90.81%)
Processing Time	Not explicitly stated as a numerical acceptance criterion, but the stated goal is "improved benefit in time saving compared to the predicate device."	1.17 minutes (95% CI: 1.16 - 1.18 minutes)	4.1 minutes (95% CI: 3.8 - 4.3 minutes)
Negative Predictive Value (NPV)	Not explicitly stated as a number, but high NPV is implied for a triage device.	99.8% (95% CI: 99.7% - 100%)	Not explicitly stated in the predicate's performance table, but stated it has "very low probability of false positive results."
Positive Predictive Value (PPV)	Not explicitly stated as a number.	43.3% (95% CI: 43.3% - 53%)	Not explicitly stated in the predicate's performance table.
Sensitivity for Intra-Axial Hemorrhages	Not explicitly stated as an independent acceptance criterion.	100% (95% CI: 96.6% - 100%)	Not explicitly stated for the predicate.
Sensitivity for Extra-Axial Hemorrhages	Not explicitly stated as an independent acceptance criterion.	92% (95% CI: 82.7% - 96.9%)	Not explicitly stated for the predicate.

Note: While specific numerical acceptance criteria (e.g., "Sensitivity must be >= 95%") are not explicitly listed in the document for each metric, the text states that "These results exceeded the predefined performance goals." This implies that the reported performance values were at or above the company's internal targets.

2. Sample Size Used for the Test Set and Data Provenance

Sample Size for Test Set: 360 newly tested cases.
Data Provenance: Retrospective study. Cases were collected from multiple sites across 17 US states.

3. Number of Experts Used to Establish Ground Truth for the Test Set and Qualifications

The document does not specify the number of experts or their qualifications used to establish the ground truth for the test set. It only mentions that performance was validated by comparing results to "predefined performance goals."

4. Adjudication Method for the Test Set

The document does not describe an explicit adjudication method (e.g., 2+1, 3+1, none) for the test set's ground truth.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study Was Done

No, the document does not mention an MRMC comparative effectiveness study where human readers improve with AI vs. without AI assistance. The study focuses on the standalone performance of the AI algorithm.

6. If a Standalone (Algorithm Only) Performance Study Was Done

Yes, a standalone (algorithm only) performance study was done. The reported metrics (Sensitivity, Specificity, Processing Time, NPV, PPV) are for the Accipiolx device's performance in identifying acute intracranial hemorrhage directly from the CT scans.

7. The Type of Ground Truth Used

The document implicitly suggests the ground truth was established by clinical assessment, as the device's output "prioritizes cases with potential acute hemorrhagic findings for urgent review." While not explicitly stated as "expert consensus," the context of a "workflow tool" aiding "clinical assessment" implies a human expert review of the cases to establish the presence or absence of ICH for comparison against the algorithm's output. The comparison of device performance to "predefined performance goals" further supports this.

8. The Sample Size for the Training Set

The document does not specify the exact sample size for the training set. It only states that the device was "developed using training CT cases collected from multiple institutions and CT manufacturers."

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

The document does not explicitly describe how the ground truth for the training set was established. It mentions a "training process" that "included pilot development, optimization of object and feature identification, algorithmic training and selection/optimization of thresholds." This strongly implies that the training data was meticulously labeled for the presence of acute intracranial hemorrhage, likely by medical experts, to enable the deep learning algorithm to learn patterns associated with ICH.

Summary

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MaxQ Al Ltd. % Joshua Schulman, Ph.D. Vice President, Clinical, Regulatory and Quality Affairs 96 Yigal Alon Street, 1st Floor Tel Aviv, 6789140 ISRAEL

August 7, 2020

Re: K201310

Trade/Device Name: Accipiolx Regulation Number: 21 CFR 892.2080 Regulation Name: Radiological computer aided triage and notification software Regulatory Class: Class II Product Code: QAS Dated: May 15, 2020 Received: May 15, 2020

Dear Dr. Schulman:

We have reviewed your Section 510(k) premarket notification of intent to market the device referenced above and have determined the device is substantially equivalent (for the indications for use stated in the enclosure) to legally marketed predicate devices marketed in interstate commerce prior to May 28, 1976, the enactment date of the Medical Device Amendments, or to devices that have been reclassified in accordance with the provisions of the Federal Food, Drug, and Cosmetic Act (Act) that do not require approval of a premarket approval application (PMA). You may, therefore, market the device, subject to the general controls provisions of the Act. Although this letter refers to your product as a device, please be aware that some cleared products may instead be combination products. The 510(k) Premarket Notification Database located at https://www.accessdata.fda.gov/scripts/cdrh/cfdocs/cfpmn/pmn.cfm identifies combination product submissions. The general controls provisions of the Act include requirements for annual registration, listing of devices, good manufacturing practice, labeling, and prohibitions against misbranding and adulteration. Please note: CDRH does not evaluate information related to contract liability warranties. We remind you, however, that device labeling must be truthful and not misleading.

If your device is classified (see above) into either class II (Special Controls) or class III (PMA), it may be subject to additional controls. Existing major regulations affecting your device can be found in the Code of Federal Regulations, Title 21, Parts 800 to 898. In addition, FDA may publish further announcements concerning your device in the Federal Register.

Please be advised that FDA's issuance of a substantial equivalence determination does not mean that FDA has made a determination that your device complies with other requirements of the Act or any Federal statutes and regulations administered by other Federal agencies. You must comply with all the Act's requirements, including, but not limited to: registration and listing (21 CFR Part 807); labeling (21 CFR Part 801); medical device reporting of medical device-related adverse events) (21 CFR 803) for

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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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510(k) Number (if known)

K201310

Device Name

Accipiolx

Indications for Use (Describe)

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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510(k) SUMMARY

MaxQ AI's Accipiolx

Submitter

MaxQ AI Ltd. 96 Yigal Alon Street, Tel Aviv, Israel 6789140 Tel: +1-617-765-0333 Contact Person: Josh Schulman, Ph.D.

Date Prepared: May 15, 2020

Name of Device: Accipiolx

Common or Usual Name/ Classification Name:

Radiological Computer-Assisted Triage and Notification Software

Regulatory Class: Class II

Regulatory Classification and Product Code: 21 C.F.R. § 892.2080; QAS

Predicate Device: Accipiolx, K182177, Manufactured by MaxQ Al

Device Description

Intended Use / Indications for Use

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suspected findings. It makes case-level output available to a PACS/workstation for worklist prioritization or triage.

Summary of Technological Characteristics

The technological characteristics and principles of operation of the subject Accipiolx device are substantially equivalent to the predicate device, Accipiolx (K182177). The subject Accipiolx and its predicate are DICOM-compliant software devices incorporated into the radiology infrastructure of a clinical center. Both employ algorithms developed through artificial intelligence methodologies to analyze head CT images received from a CT scanner.

The subject Accipiolx, like its predicate device, was developed using a training CT cases collected from multiple institutions and CT manufacturers. This training process included pilot development, optimization of object and feature identification, algorithmic training and selection/optimization of thresholds above which cases are considered positive.

The subject device and the predicate device also have highly similar principles of operation: the software analyzes CT images and prioritizes cases for physician review when there is suspected alCH. In both the predicate and subject devices, the algorithm identifies applicable CT cases based on image parameters. In both devices, skull stripping and registration steps are performed, the relevant tissues are identified and segmented, and a feature identification process, which includes measures of hyperdensity compared to a pre-defined threshold, is performed. Findings above this threshold cause the software device to generate a case-level identifier which is used for prioritization of cases based on a suspected cerebrovascular finding. The system output is used for prioritization within radiological workflow and viewing systems. In both cases, the procedure is performed in parallel to and in conjunction with the standard processing of image and their availability for clinician assessment.

The algorithmic functionality of Accipiolx is substantially equivalent in all aspects to that of the predicate device and differs only with respect to the segmentation method used and inclusion of additional algorithmic features for enhanced detection of alCH. These improvement changes do not alter the safety and efficacy profile of the device.

The subject Accipiolx and the predicate device have the same intended use and indications for use. Both devices are assistive software tools, designed to analyze head CT images for findings suggestive of a pre-specified clinical condition -- specifically, alCH. Both the subject and predicate devices support the rapid assessment of alCH. For these reasons, the subject device is substantially equivalent to the predicate device.

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A summary table comparing the key features of the subject and predicate devices is provided below.

	Subject Device:MaxQ Al Accipiolx	Predicate Device:MaxQ Al Accipiolx(K182177)
Intended Use /Indications for Use	Accipiolx is a software workflowtool designed to aid in prioritizingthe clinical assessment of adultnon-contrast head CT cases withfeatures suggestive of acuteintracranial hemorrhage in theacute care environment. Accipiolxanalyzes cases using an artificialintelligence algorithm to identifysuspected findings. It makes case-level output available to aPACS/workstation for worklistprioritization or triage.Accipiolx is not intended to directattention to specific portions of an	Accipiolx is a software workflowtool designed to aid in prioritizingthe clinical assessment of adultnon-contrast head CT cases withfeatures suggestive of acuteintracranial hemorrhage in theacute care environment. Accipiolxanalyzes cases using an artificialintelligence algorithm to identifysuspected findings. It makes case-level output available to aPACS/workstation for worklistprioritization or triage.Accipiolx is not intended to directattention to specific portions of an
	image or to anomalies other thanacute intracranial hemorrhage. Itsresults are not intended to be usedon a stand- alone basis for clinicaldecision-making nor is it intendedto rule out hemorrhage orotherwise preclude clinicalassessment of CT cases.	image or to anomalies other thanacute intracranial hemorrhage. Itsresults are not intended to be usedon a stand- alone basis for clinicaldecision-making nor is it intendedto rule out hemorrhage orotherwise preclude clinicalassessment of CT cases.
Population	Adult patients (21 years of age andolder) indicated for head CT	Adult patients (21 years of age andolder) indicated for head CT
Notification-only, parallel workflowtool	Yes	Yes
Intended users	Hospital networks and trainedclinicians	Hospital networks and trainedclinicians
Setting	Acute care	Acute care
Identify patients with a pre-specified clinical condition	Yes	Yes
Clinical condition	Cerebrovascular Event:Intracranial hemorrhage	Cerebrovascular Event:Intracranial hemorrhage
Alert to finding	Yes; flagged for review	Yes; flagged for review
Independent of standard of care	Yes; No cases are removed from	Yes; No cases are removed from
workflow	worklist	worklist
Modality	Non-Contrast head CT	Non-Contrast head CT
Artificial Intelligence algorithm	Yes	Yes
Software segmentation method	CNN-based segmentation	Machine-vision based segmentation
Case level assessment of clinicalcondition probability	Yes - case-level assessment isderived both from detection ofindividual aICH findings andalgorithmic assessment of the fullcase.	Yes - case-level assessment isderived from detection of individualaICH findings

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Limited to analysis of imaging data	Yes	Yes
Non-Diagnostic DICOM Preview	No	No
Aids prompt identification andprioritization of cases withindicated findings	Yes	Yes
Medical systems/devices used inconjunction with the device	1. Device is used with CT systemsfor DICOM input and with PACSsystems, which may present/usethe data.2. Device is used with MedicalImage Communications Devices(MICDs), such as the AccipioAgent, to receive native CTimages for processing and togenerate visual results	1. Device is used with CT systemsfor DICOM input and with PACSsystems, which may present/usethe data.2. Device is used with MedicalImage Communications Devices(MICDs), such as the AccipioAgent, to receive native CTimages for processing and togenerate visual results
Output	Suspected hemorrhage /No suspected hemorrhage	Suspected hemorrhage /No suspected hemorrhage
Systems where results aredisplayed	PACS / Workstation	PACS / Workstation
Performance results	Sensitivity- 97%(95% Cl: 92.8% - 98.8%)Specificity- 93%(95% Cl: 88.6% - 96.6%)Processing time - 1.17 minutes(95% Cl: 1.16 - 1.18 minutes)	Sensitivity - 92%(95% Cl: 87.29 - 95.68%)Specificity - 86%(95% Cl: 80.18 - 90.81%)Processing time - 4.1 minutes(95% Cl: 3.8 - 4.3 minutes)

Performance Testing

Performance testing was conducted using the same approach as that used for the predicate device. MaxQ AI conducted a retrospective study to test the clinical performance of Accipiolx in processing non-contrast head CT cases with a high or low probability of ICH. Performance of the device was analyzed based on validation testing results that were compared to the predefined performance qoals.

. Analysis of 360 newly tested cases collected from multiple sites across 17 US states demonstrated device Sensitivity and Specificity of 97% (95% Cl: 92.8%) and 93% (95% Cl: 88.6% - 96.6%), respectively.
Sensitivity for intra-axial and extra-axial hemorrhages was 100% (95% Cl: 96.6% 100%) and . 92% (95% Cl: 82.7% - 96.9%), respectively. Although the Sensitivity observed in the extra-axial subgroup was lower compared to the intra-axial subgroup, the result was still notably high, considering the fact that extra-axial hemorrhages are known to be more difficult for accurate assessment among radiologists.
The average per-case processing time (confirmatory secondary endpoint) was 1.17 minutes (95% Cl: 1.16 - 1.18 minutes), demonstrating that when using the subject Accipiolx for alCH prioritization, the intended users will have improved benefit in time saving compared to the predicate device.

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Analysis of additional accuracy parameters, tested as secondary endpoints, demonstrated NPV . and PPV of 99.8% (95% Cl: 99.7% - 100%) and 43.3% (95% - 53%), respectively, The very high NPV result reflects relatively very low probability of false positive results given by Accipiolx.
These results exceeded the predefined performance goals and demonstrated improved clinical performance, which is substantially equivalent to the predicate device.

Based on the software testing and clinical performance, Accipiolx has a safety and effectiveness profile that is substantially equivalent to the predicate device for the proposed indications for use.

Conclusion

The Accipiolx device is as safe and effective as its predicate device. Accipiolx has the same intended use and indications for use, very similar technological characteristics and principles of operation as its predicate device. The minor differences do not alter the intended prioritization and triage use of the device and do not affect its safety and effectiveness when used as labeled, and also do not raise any new or different questions of safety or effectiveness. Clinical testing demonstrates an improved performance profile and supports the intended software function. Thus, the subject Accipiolx is substantially equivalent to the predicate Accipiolx (K182177).

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.