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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.

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.

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

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.

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The HealthCCSng device is intended for use as a non-invasive post-processing software to evaluate calcified plaques in the coronary arteries, which present a risk for coronary artery disease. The software generates an estimated coronary artery calcium detection category.

The HealthCCSng device analyzes existing non-cardiac-gated CT studies that include the heart of adult patients above the age of 30. The device generates a three-category output representing the estimated quantity of calcium detected together with preview axial images of the detected calcium meant for informational purposes only. The device output will be available to the radiologist as part of their standard workflow. The HealthCCSng results are not intended to be used on a stand-alone basis for risk attribution, clinical decision-making or otherwise preclude clinical assessment of CT studies.

HealthCCSng product is a software device that automatically estimates the coronary arterv calcium category from non-cardiac-gated adult CT scans. The product is aimed to leverage the high utilization of CT scans in the medical care environment (both inpatient and outpatient), including lung cancer screening programs, in order to automatically detect calcification in the coronary arteries of patients in an opportunistic manner.

Zebra's HealthCCSng product analyzes cases using an artificial intelligence algorithm for the automated detection and estimation of coronary calcium and outputs a result for review by the radiologist. The device works in parallel to and in conjunction with the standard of care workflow. The final diagnosis is made by the radiologist after reviewing the scan independently of the software. The device is intended for use by the radiologists as a non-diagnostic analysis software in conjunction with additional patient information and professional judgment.

HealthCCSng receives a non-cardiac-gated CT study from the storage application, Zebra's Imaging Analytics Platform (IMA). For each CT study received, the software shall validate there is at least one compliant series in which the entire heart is present, and perform an analysis. For each compliant study, the software shall output:

1.Estimated Coronary Calcium Detection, based on the measurement of calcium deposits in the coronary arteries.
2. A corresponding Estimated Coronary Calcium Detection Category, based on the Estimated Coronary Calcium measurement.

The software output will include the following calcium categories:

| Estimated Coronary Calcium
Detection | Corresponding Estimated Coronary Calcium
Detection Category |
|-----------------------------------------|----------------------------------------------------------------|
| 0-99 | Low |
| 100-399 | Medium |
| ≥400 | High |

For patients in which calcium was detected, the user will be presented with representative images - all the slices containing the measured coronary calcifications (130 HU and above). On these images, the calcified areas will be annotated (with an option for the user to toggle on and off the annotation).

The following modules compose the HealthCCSng software:

Data input and validation: Following retrieval of a study, the validation feature assessed the input data (i.e. age, modality, view, etc.) to ensure compatibility for processing by the algorithm.

HealthCCSng algorithm: Once a study has been validated, the algorithm analyzes the CT for analysis and quantification.

IMA Integration feature: The study analysis and the results of a successful study analysis is provided to IMA.

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 the study proving the device meets them, based on the provided FDA 510(k) summary for HealthCCSng:

1. Table of Acceptance Criteria and Reported Device Performance

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

• Sample Size: 447 anonymized CT chest cases.
• Data Provenance: Retrospective study from two healthcare institutions, composed of multiple clinical sites. The specific country of origin is not mentioned.

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

• Number of Experts: Three radiologists.
• Qualifications: The document does not specify the qualifications (e.g., years of experience, subspecialty) of these radiologists.

4. Adjudication Method for the Test Set

• Adjudication Method: "Majority agreement of two of three radiologists" was used to determine the ground truth category. This is a 2-out-of-3 majority consensus method.

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

• Was an MRMC study done? The document describes a standalone performance study comparing the device's output to ground truth. It does not mention a comparative effectiveness study involving human readers with and without AI assistance to measure an effect size.

6. Standalone (Algorithm Only) Performance

• Was a standalone study done? Yes, the document explicitly states: "The HealthCCSng software device performance was validated in a stand-alone retrospective study for its overall agreement compared to the established ground truth..."

7. Type of Ground Truth Used

• Type of Ground Truth: Expert consensus. Specifically, the "ground truth category was determined by the majority agreement of two of three radiologists."

8. Sample Size for the Training Set

• The document does not specify the sample size for the training set. It only details the validation (test) 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 describes the ground truth establishment for the validation (test) set. It's common for training data ground truth to be established through expert annotations, but the method is not stated here.

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The Zebra Health.JOINT device is a software tool for 3D reconstruction of bones from a set of 2D radiographs. The device is intended for assisting clinicians in the preoperative planning of knee orthopedic surgical procedures. Zebra's HealthJOINT analyzes cases using an artificial intelligence algorithm for the 3D model reconstruction. In addition to the model, the software provides a list of anatomical landmarks with their position on the 3D model. The result is made available via a 3rd parties' software interface for further display and analysis of the 3D bone model. Clinical judgement and experience are required to properly use the models produced by this software.

Zebra's HealthJOINT device is a software product that uses an artificial intelligence algorithm to analyze X-ray scans. The HealthJOINT is indicated for the analysis of X-rays scans. The device receives a set of 2D radiographs and automatically provides a 3D model of the bones together with a list of anatomical landmarks with their position on the 3D model may be used by physicians for pre-operative planning of knee orthopedic surgeries. The HealthJoint supports 3D reconstructions of healthy bones, and osteoarthritis patients graded 1 to 4 based on the Kellgren-Lawrence grading system.

The HealthJOINT device functions as a component that can be used by 3rd parties via an API to generate the 3D models and provides a list of anatomical landmarks with their position on the 3D model. The software communicates with the API only, and is not user-facing. The software does not recommend clinical decisions or treatment.

The software is intended for use by clinicians in conjunction with additional patient information and professional judgment.

The following modules compose the HealthJoint software:

Data input and validation: performs validation of the input, X-ray DICOM images, assesses the input data (i.e. age, modality, view) to ensure compatibility for processing by the algorithm.

HealthJoint algorithm: Once the study has been validated the algorithm analyzes the AP (anteriorposterior) along with the LAT (lateral) knee X-ray study in order to provide 3D bone models and locations of anatomic landmarks.

IMA Integration feature: provides the capability to post studies for processing, get the study analysis status and the results of successful study analysis via a Web API.

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 calling 3rd party via the Web API.

1. Acceptance Criteria and Reported Device Performance

2. Sample Size and Data Provenance for Test Set

• Sample Size: 67 pairs of knee X-rays and CT scans.
• Data Provenance: The document states "US Board-Certified radiologist" when describing the ground truth expert, but doesn't explicitly state the country of origin for the data itself. The study was retrospective.

3. Number and Qualifications of Experts for Ground Truth (Test Set)

• Number of Experts: One
• Qualifications of Expert: Experienced US Board-Certified radiologist.

4. Adjudication Method (Test Set)

The document does not explicitly describe an adjudication method for the test set. It mentions ground truth established by a single experienced US Board-Certified radiologist.

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

No, a multi-reader multi-case (MRMC) comparative effectiveness study was not conducted. The performance evaluation was a standalone retrospective study of the device's accuracy.

6. Standalone Performance Study

Yes, a standalone performance study was conducted. The document states: "The HealthJOINT device performance was evaluated in a stand-alone retrospective study for accuracy..."

7. Type of Ground Truth Used (Test Set)

The ground truth for the 3D model accuracy and anatomical landmark positioning was established by comparing the software's output to an "established Ground Truth by an experienced US Board-Certified radiologist" using CT scans as a reference.

8. Sample Size for Training Set

The document does not provide the sample size used for the training set. It focuses on the performance evaluation using an independent test set.

9. How Ground Truth for Training Set Was Established

The document does not provide information on how the ground truth for the training set was established.

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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.

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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HealthVCF is a passive notification for prioritization-only, parallel-workflow software tool used by clinicians to prioritize specific patients within the standard-of-care bone health setting for suspected vertebral compression fractures. HealthVCF uses an artificial intelligence algorithm to analyze chest and abdominal CT scans and flags those that are suggestive of the presence of at least one vertebral compression at the exam level. These flags are viewed by the clinician in Bone Health and Fracture Liaison Service programs in the medical setting via a worklist application on their Picture Archiving and Communication System (PACS). HealthVCF does not send a proactive alert directly to the user.

Health VCF does not provide diagnostic information beyond triage and prioritization, it does not remove cases from the radiology worklist, and should not be used in place of full patient evaluation, or relied upon to make or confirm diagnosis.

Zebra's HealthVCF solution is a software product that automatically identifies suspected findings suggestive of vertebral compression fractures on chest and abdominal CT scans and provides a passive notification to the workstation of the presence of this finding in the scan. This notification is received by the standalone desktop Zebra Worklist application which flags the identified scan and assists clinicians engaged in bone-health management in viewing the scan ahead of others. The device aim is to aid in prioritization and triage of radiological medical images only and does not provide diagnostic information beyond triage.

The software uses an artificial intelligence algorithm to automatically analyze chest and abdominal CT scans. If a suspected vertebral compression fracture is found in a scan, the alert is automatically sent to the Zebra Worklist application on the workstation used by the bone-health clinician in parallel with the ongoing standard of care within the bone health setting. The standard of care radiology workflow (i.e. reviewing and reporting the findings that initiated the request for CT) continues unaffected by the parallel workflow of the bone health program. For clarity, the HealthVCF device does not flag/prioritize cases within this radiology workflow. The standalone desktop application, Zebra Worklist, includes three sagittal preview images meant for informational purposes only and is not intended for diagnostic use. The Zebra Worklist presents all cases processed by the algorithm, and flags those with a suspected finding.

Zebra's HealthVCF device works in parallel to and in conjunction with the standard care of workflow within bone health programs, and completely independent of the standard of care workflow within the radiology department. After a chest or abdominal CT scan has been performed, a copy of the study is automatically retrieved and processed by the HealthVCF device. The device performs the analysis of the study and returns a notification about a suspected vertebral compression fractures to the Zebra Worklist to notify the clinicians in Bone Health and Fracture Prevention Programs reviewing the chest and abdominal CTs for at-risk patients. The clinician is then able to review the study earlier and recall the patient for further evaluation.

The primary benefit of the product is the ability to reduce the time it takes to alert physicians to the presence of a finding such as a vertebral compression fracture. The software does not recommend treatment or provide a diagnosis. It is meant as a tool to assist in improved workload prioritization of cases in bone health and fracture prevention programs. The final diagnosis is provided by a clinician after reviewing the scan itself.

The following modules compose the HealthVCF 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.
HealthVCF algorithm: Once a study has been validated, the algorithm analyzes the chest and abdominal CT scans for detection of suspected finding suggestive of vertebral compression fracture.
IMA Integration feature: The study analysis and the results of a successful study analysis is provided to IMA, to then be sent to Zebra Worklist application for triaging.
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 a breakdown of the acceptance criteria and the study proving the HealthVCF device meets them, based on the provided FDA 510(k) summary:

1. Table of Acceptance Criteria and Reported Device Performance

Note: The document states the AUC performance goal was >95%, and the device achieved 0.9504, indicating it met this goal. The sensitivity and specificity figures were reported for "this operating point" but the specific thresholds for these points as acceptance criteria are not explicitly stated, beyond the general concept of "accuracy performance goals."

2. Sample Size and Data Provenance for the Test Set

• Sample Size: A retrospective cohort of 611 anonymized Chest and abdominal CT cases.
  • 306 cases positive for vertebral compression fractures (severe and moderate fractures).
  • 305 cases negative for vertebral compression fractures (mild or no fracture), including confounding imaging factors.
• Data Provenance: Retrospective, from the USA and Israel.

3. Number of Experts and Qualifications for Ground Truth

• Number of Experts: Three experts were used to establish ground truth.
• Qualifications: All three experts were US Board-Certified Radiologists. Their years of experience are not specified in the provided text.

4. Adjudication Method for the Test Set

The document explicitly states that the validation data set was "truthed (ground truth) by three US Board-Certified Radiologists." It does not specify the exact adjudication method (e.g., 2+1, 3+1 consensus, majority vote) beyond stating that three radiologists established the ground truth. There is no mention of "none" for adjudication.

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

No Multi-Reader Multi-Case (MRMC) comparative effectiveness study was mentioned. The study focused on the standalone performance of the HealthVCF device against ground truth, not on how human readers' performance improved with AI assistance.

6. Standalone Performance (Algorithm Only)

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

7. Type of Ground Truth Used

The type of ground truth used was expert consensus (from 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 describes the validation/test set.

9. How Ground Truth for the Training Set Was Established

The document does not specify how ground truth for the training set was established. It only details the ground truthing process for the independent validation/test set.

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The Zebra HealthCXR device is a software workflow tool designed to aid the clinical assessment of adult Chest X-Ray cases with features suggestive of pleural effusion in the medical care environment. HealthCXR analyzes cases using an artificial intelligence algorithm to identify suspected findings. It makes case-level output available to a PACS/worldion for worklist prioritization or triage. HealthCXR is not intended to direct attention to specific portions or anomalies of an image. Its results are not intended to be used on a stand-alone basis for clinical decision-making nor is it intended to rule out pleural effusion or otherwise preclude clinical assessment of X-Ray cases.

The HealthCXR solution is a software product that automatically identifies suspected findings on chest x-rays (e.g. pleural effusion) and notifies PACS/workstation of the presence of this critical finding in the scan. This notification allows for worklist 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 standalone desktop application, Zebra Worklist, includes a compressed preview image meant for informational purposed only and is not intended for diagnostic use.

The HealthCXR 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 HealthCXR device performs the analysis of the study and returns a notification about the relevant pathology to the PACS/workstation which prioritizes it through the worklist interface or alternatively, the Zebra Worklist will notify the user through the standalone desktop application. 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 HealthCXR software for Pleural Effusion:

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.

Pleural Effusion algorithm: Once a study has been validated, the algorithm analyzes the frontal chest x-ray for detection of suspected finding suggestive of pleural effusion.

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.

HealthCXR Acceptance Criteria and Performance Study

This response details the acceptance criteria and the study conducted to prove the HealthCXR device meets these criteria, based on the provided FDA 510(k) summary.

1. Table of Acceptance Criteria and Reported Device Performance

Note: The acceptance criteria for sensitivity and specificity are implicitly derived from the statement that the device met the performance goal and was found to be "substantially equivalent to the predicate device, HealthPNX (K190362)." The document does not explicitly state numerical thresholds for the predicate's performance for these specific metrics, but rather that the HealthCXR results were comparable. The AUC goal of >0.95 for effective triage is explicitly stated.

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

• Sample Size: 554 anonymized Chest X-ray cases.
• Data Provenance: Retrospective cohort from the USA and Israel.
  • 276 cases positive for Pleural Effusion.
  • 278 cases negative for Pleural Effusion (including confounding imaging factors).

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

• Number of Experts: Three (3)
• Qualifications: US Board-Certified Radiologists.

4. Adjudication Method for the Test Set

The document states the data was "truthed (ground truth) by three US Board-Certified Radiologists." It does not explicitly state the adjudication method (e.g., 2+1, 3+1). It can be inferred that a consensus or majority opinion was used to establish the ground truth among the three, but the specific process isn't detailed.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study was Done, What was the Effect Size of How Much Human Readers Improve with AI vs. Without AI Assistance

No MRMC comparative effectiveness study involving human readers and AI assistance was reported in this document. The study focused on the standalone performance of the AI algorithm.

6. If a Standalone (i.e., algorithm only without human-in-the-loop performance) was Done

Yes, a standalone performance study was done. The document explicitly states: "The stand-alone detection accuracy was measured on this cohort respective to the ground truth."

7. The Type of Ground Truth Used

Expert consensus (established by three US Board-Certified Radiologists).

8. The Sample Size for the Training Set

The document does not provide the sample size for the training set. It only describes the validation data set.

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

The document does not provide information on how the ground truth for the training set was established. It only details the establishment of ground truth for the test/validation set.

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The Zebra Head CT triage device is a software workflow tool designed to aid the clinical assessment of adult non-contrast head CT cases with features suggestive of intracranial hemorrhage in the medical care environment. HealthICH 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. HealthICH is not intended to direct attention to specific portions of an image or to anomalies other than 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.

Zebra's HealthICH solution is a software product that automatically identifies suspected finding suggestive of Intracranial Hemorrhage and notifies the PACS/workstation of the presence of this critical finding in the scan. This notification allows for worklist prioritization of the identified scan and assists clinicians in viewing the prioritized scan before others. The device aim is to aid in the prioritization and triage of radiological medical images only.

Zebra's HealthICH Triage Device uses an artificial intelligence algorithm to analyze CT scans. The HealthICH is indicated for the analysis of non-contrast head CT scans. The algorithm output does not include an image and therefore it does not mark, highlight or direct users' attention to a specific location on the original CT scan.

The Zebra's HealthICH device works in parallel to and in conjunction with the standard of care workflow. After a head CT scan has been performed, a copy of the study is automatically retrieved and processed by the HealthICH device. The device performs the analysis of the study and return a notification about a suspected finding suggestive of Intracranial Hemorrhage to the PACS/workstation which prioritizes it through its worklist interface. 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 notify physician to the presence of a critical finding such as suspected Intracranial Hemorrhage in the head CT scan.

The software does not recommend treatment or provide a diagnosis. It is designed as tool to assist the medical staff and hospital networks in workflow triaging by highlighting and prioritizing studies containing suspected findings. The final diagnosis is provided by a radiologist or other qualified physician after examining the original scan as determined by the clinical standard of care. The software is intended for use by radiologists and other qualified medical staff who read head CT scans on a regular basis

The following modules compose the HealthICH 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.

HealthICH algorithm: Once a study has been validated, the algorithm analyzes the head CT for identification of suspected finding suggestive of intracranial hemorrhage.

IMA Integration feature:
HealthICH interacts with users through the PACS/workstation via the Zebra Imaging Analytics platform (IMA). It does not have a Graphical User Interface (GUI). The HealthICH sends the results of the study analysis to the Zebra Imaging Analytics Platform, which then distributes the HealthICH results 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 Zebra Imaging Analytics Platform

Here's a summary of the acceptance criteria and the study that proves the device meets them, based on the provided text:

1. Table of Acceptance Criteria and Reported Device Performance

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

• Sample Size: 427 anonymized head CT cases.
  • 199 intracranial hemorrhage positive cases
  • 228 intracranial hemorrhage negative cases
• Data Provenance: Retrospective cohort from USA and Israel.

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

• Number of Experts: Initially two, with a third senior expert in case of disagreement.
• Qualifications: US Board Certified neuro-radiologists. The third expert was a "more senior" US Board Certified neuro-radiologist.

4. Adjudication Method for the Test Set

• Adjudication Method: 2+1 (Two experts established ground truth, and if they disagreed, a third, more senior expert made the final determination).

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

• No, an MRMC comparative effectiveness study was not done. The performance data provided is for the device operating stand-alone.

6. Standalone Performance (Algorithm Only) Study

• Yes, a standalone performance study was done. The text explicitly states: "The performance of the HealthICH device has been validated in retrospective stand-alone performance study..." and "The stand-alone detection accuracy was measured on this cohort respective to ground truth."

7. Type of Ground Truth Used

• Type of Ground Truth: Expert consensus (from US Board Certified neuro-radiologists).

8. Sample Size for the Training Set

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

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

• The document does not provide information on how the ground truth for the training set was established. It focuses solely on the ground truth establishment for the validation/test set.

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The Zebra Pneumothorax device is a software workflow tool designed to aid the clinical assessment of adult Chest X-Ray cases with features suggestive of Pneumothorax in the medical care environment. HealthPNX 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. HealthPNX is not intended to direct attention to specific portions or anomalies of an image. Its results are not intended to be used on a stand-alone basis for clinical decision-making nor is it intended to rule out Pneumothorax or otherwise preclude clinical assessment of X-Ray cases.

Zebra's HealthPNX is a radiological computer-assisted triage and notification software system. The software automatically analyzes PA/AP chest x-rays and alerts the PACS/workstation once findings suspicious of pneumothorax are identified.

The following modules compose the HealthPNX software system:

Data input and validation: After a chest x-ray has been performed, a copy of the study is automatically retrieved and processed by the HealthPNX device. Following retrieval of a study, the validation feature assesses the input data (i.e. age, modality, view) to ensure compatibility for processing by the algorithm.

HealthPNX algorithm: Once a study has been validated, the algorithm analyzes the frontal chest x-ray for detection of suspected findings suggestive of pneumothorax.

IMA Integration feature: The study analysis and the results of a successful study analysis is provided to IMA, that notifies the PACS/workstation through the worklist interface.

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.

The radiologist is then able to review the study earlier than in standard of care workflow.

In summary, the HealthPNX device is intended to provide a passive notification through the PACS/workstation to the radiologists indicating the existence of a case that may potentially benefit from the prioritization. It doesn't output an image and therefore it does not mark, highlight, or direct users' attention to a specific location on the original chest X ray.

The device aim is to aid in prioritization and triage of radiological medical images only.

Here's a breakdown of the acceptance criteria and study details for the HealthPNX device, based on the provided FDA 510(k) summary:

1. Table of Acceptance Criteria and Reported Device Performance

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

• Sample Size for Test Set: 588 anonymized Chest X-Ray cases (146 pneumothorax positive, 442 pneumothorax negative).
• Data Provenance: Retrospective cohort from the USA and Israel.

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

• Number of Experts: Three (3) US Board Certified Radiologists.
• Qualifications: US Board Certified Radiologists. Years of experience are not specified.

4. Adjudication Method for the Test Set

The provided text states that "The validation data set was truthed (ground truth) by three US Board Certified Radiologists (truthers)." It does not explicitly detail an adjudication method like 2+1 or 3+1. This implies that the consensus of these three radiologists established the ground truth, but the specific rules (e.g., majority vote, unanimous) are not described.

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

• Yes, an MRMC-like study was done. The text states: "The triage effectiveness was evaluated by three different US Board Certified Radiologists (readers) that read these cases prospectively in real time with the HealthPNX device (HealthPNX prioritized work-list) and without (standard of care, 'First-in-First-out' or 'FIFO' queue) with a washout period separating between the two read periods with and without the HealthPNX device."
• Effect Size of Human Readers' Improvement:
  • Without AI (Standard of Care): Mean triage time of 68.98 minutes (95% CI: [60.53, 77.43] minutes).
  • With AI (HealthPNX): Mean triage time of 8.05 minutes (95% CI: [5.93, 10.16] minutes).
  • Improvement (Reduction): 60.93 minutes. This represents a statistically significant reduction in triage time for time-sensitive images.

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

• Yes, a standalone study was done. The text explicitly states: "The stand-alone detection accuracy was measured on this cohort respective to ground truth." and "Overall, the HealthPNX was able to demonstrate an area under the curve (AUC) of 98.3% (95% CI: [97.40%, 99.02%])".

7. The Type of Ground Truth Used

• Expert Consensus: The ground truth for the test set was established by three (3) US Board Certified Radiologists.

8. The Sample Size for the Training Set

The document does not explicitly state the sample size for the training set. It only discusses the validation/test set.

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

The document does not provide information on how the ground truth for the training set was established. It only describes the process for the validation/test set.

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The HealthCCS Device is intended for use as a non-invasive post-processing software that can be used to evaluate calcified plaques in the coronary arteries, which may be a risk factor for coronary artery disease. The software can be used to generate reports of the total risk category of coronary calcium. This information can then be used by a physician for further analysis and treatment. The HealthCCS Device analyses pre-existing heart or chest ECG-Gated Triggered CT scans. The Device is indicated for use only on patients whose age at the CT scan was taken, was above 20 years old. This device generates a 4-category Agatston-equivalent risk score, and the patient management, especially for the patient with the score from 0-10, will depend on the physician's own judgment. It may require further testing to evaluate the appropriate clinical management.

The HealthCCS Device is an automatic non-invasive post processing tool that uses cardiac CT images to identify and quantify calcification in the coronary arteries, known to be a risk factor for coronary disease. HealthCCS Device quantifies calcification on non-contrast cardiac computed tomography (CT) scans. HealthCCS Device calculates the amount of identified calcification and reports the risk category of coronary calcium. This information can then be used by a physician for further analysis and treatment.

The provided document describes the HealthCCS device, a non-invasive post-processing software for evaluating calcified plaques in coronary arteries. Here's a breakdown of the acceptance criteria and the study that proves the device meets those criteria:

1. Table of Acceptance Criteria and Reported Device Performance

The document explicitly states the overall acceptance criterion as "adequate overall agreement" and "adequate agreement per category." It then reports the achieved performance against these criteria.

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

• Sample Size for Test Set: 249 studies.
• Data Provenance: The document states a "retrospective performance study." No specific country of origin is mentioned for the data, but the applicant's address is Israel.

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

• Number of Experts: 3 radiologists.
• Qualifications of Experts: Not specified in the document beyond "radiologists." No information on their years of experience or subspecialty focus is provided.

4. Adjudication Method for the Test Set

The ground truth was established by "3 radiologists using the Kodak Carestream PACS device (K053347)." This implies a consensus or majority rule approach, but the specific adjudication method (e.g., 2+1, 3+1) is not explicitly stated. It seems to be a form of expert consensus derived from their use of a reference device.

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

• A formal MRMC comparative effectiveness study comparing human readers with and without AI assistance was not described. The study described focused on the agreement between the HealthCCS device's categorization and the ground truth established by radiologists using a reference PACS device. It was a comparison of the AI's output against human-derived ground truth, not an assessment of human performance improvement with AI assistance.

6. If a Standalone (Algorithm Only Without Human-in-the-Loop Performance) Was Done

• Yes, the performance study described is essentially a standalone (algorithm only) performance study. The HealthCCS device's output (4-level risk categorization) was directly compared to the ground truth established by the radiologists. There was no human-in-the-loop component described for this performance evaluation.

7. The Type of Ground Truth Used

• Expert Consensus (using a reference device): The ground truth was established by "3 radiologists using the Kodak Carestream PACS device (K053347)." This indicates that the radiologists performed the calcification scoring using a legally marketed device, and their determinations served as the "ground truth" for comparison.

8. The Sample Size for the Training Set

• The document does not specify the sample size used for the training set. It only details the performance validation study (test set).

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

• The document does not provide information on how the ground truth for the training set was established. The focus is solely on the validation of the device's performance using a test set. However, a CNN-based probability threshold is mentioned, implying that the model was trained on data with ground truth about what constitutes coronary arterial calcification.

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